1 COMPUTATIONAL DRUG DESIGN AND DISCOVERY FOR TRYPANOSOMA cruzi TRANS SIALIDASE By BILL MILLER III A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2013
2 2013 Bill Miller III
3 To my parents, Robin and Bill Jr, my wife, Ryan, and children, Rylee and Billy
4 ACKNOWLEDGEMENTS The work I present in this dissertation would not have been possible without the help, support, and encouragement of many people. My advisor, Dr. Adrian Roitberg, gave me the opportunity to succeed in his lab and gave me a project that was a perfect fit for me. He allowed me to beco me a better scientist by guiding me and always giving great advice. More importantly, I have to thank Dr. Roitberg for being very lenient and accommodating with my personal needs, allowing me to have a flexible work schedule so that I could spend more time with my children. Becoming a father twice while in graduate school is not ideal for most doctoral students, but Dr. Roitberg gave me the opportunity to not put my personal plans on hold and allowed me the chance to more comfortably balance life as a Ph.D. student and life as a father and husband. Over the last five years, Dr. Roitberg has helped me develop professionally, while at the same time allowing me to spend quality time with the people that are most important in my life. Many other University of Fl orida (UF) faculty have also been instrumental in helping me along the way. I want to thank Dr. Kennie Merz for being a great scientific collaborator that was always pushing me to do better and for being available to talk sports. I would like to thank Dr. Erik Deumens for being a great resource as a computer expert. I also want to thank Dr. Nicole Horenstein for her patience in helping teach me about the process of making protein and running kinetic assays in a biochemistry lab. I acknowledge the rest of my committee, Dr. Gail Fanucci and Dr. Art Edison, for being resources to me for my oral defense all the way through my doctoral dissertation. My early transition from undergraduate to graduate school was aided by many people in the Quantum Theory Project (Q TP), including Dr. Dan Sindhikara, Dr. zlem Demir, and Dr. Gustavo Seabra. They helped me become acclimated to conducting
5 graduatelevel research in the Roitberg group. I also could not have survived graduate school without all the help provided by the Q TP staff, especially Judy Parker and Coralu Clements they were always willing to help me with any problems. My life in graduate school would not have been complete without so many graduate students and post docs at UF helping me along with way, both profes sionally and personally. I want to give a special thanks to Dwight McGee and Jason Swails for with me since the beginning. I want to thank Johan Cruz, Dr. Juan Bueren Calabuig, Natali di Russo, Matias Pascualini, and Shuai Liang for many great memories in the lab. Dr. Thomas Watson helped recruit me to UF and has been a great friend ever since. Finally, I want to acknowledge the help and support of my family. I attribute my intelligence and work ethic from my mom and dad, Robin and Bill Jr. They have been n othing but supportive of me despite the fact that I moved halfway across the country from them. My children, Rylee (3 years) and Billy IV (6 months ), are the best kids any father could ask for. Thanks to them, I have enjoyed nearly every minute that I have spent away from work for the past three years. My final acknowledgement goes to my loving wife, Ryan. She agreed to pick up and move away from her family so I could go to school. She has allowed me the time I needed to graduate so that I can make our fami ly move across the country once again. She has proofread papers and posters, and listened to practice presentations all to help meand I could not thank her enough for that.
6 TABLE OF CONTENTS Page ACKNOWLEDGEMENTS ............................................................................................... 4 LIST OF TABLES ............................................................................................................ 9 LIST OF FIGURES ........................................................................................................ 10 LIST OF ABBREVIATIONS ........................................................................................... 14 ABSTRACT ................................................................................................................... 15 CHAPTER 1 INTRODUCTION .................................................................................................... 17 1.1 Prologue ........................................................................................................... 17 1.2 Chagas Disease ............................................................................................... 17 1.3 Trypanosoma cruzi ........................................................................................... 21 1.4 Trypanosoma cruzi Trans sialidase .................................................................. 24 1.4.1 TcTS Structure ....................................................................................... 25 1.4.2 TcTS Reaction Mechani sm .................................................................... 29 1.4.3 TcTS Dynamics ..................................................................................... 34 1.4.4 TcTS Drug Design and Discovery Efforts .............................................. 37 2 METHODS .............................................................................................................. 42 2.1 Molecular Structure and Function ..................................................................... 42 2.2 Molecular Docking ............................................................................................ 42 2.2.1 Receptor Grid Generation ...................................................................... 45 2.2.2 Generating Ligand Conformations ......................................................... 47 2.2.3 Search Algorithms ................................................................................. 48 2.2.4 Scoring Functions .................................................................................. 52 2.3 Pharmacophore Models .................................................................................... 60 2.4 Molecular Dynamics .......................................................................................... 64 2.5 Binding Free Energy Calculations ..................................................................... 70 3 DESIGN OF e PHARMACOPHORE MODELS USING COMPOUND FRAGMENTS FOR the TR ANS SIALIDASE of TRYPANOSOMA cruzi: SCREENING FOR NOVEL INHIBITOR SCAFFOLDS ........................................... 71 3.1 Preliminaries ..................................................................................................... 71 3.2 Methods ............................................................................................................ 72 3.2.1 Receptor Structures and Grid Generation ............................................. 72 3.2.2 Docking Fragments to TcTS Structures ................................................. 74 3.2.3 E pharmacophore Generation ............................................................... 75
7 3.2.4 Pharmacophore Screening .................................................................... 75 3.2.5 Molecular Dynamics and Free Energy Calculations .............................. 76 3.3 Results and Discussion ..................................................................................... 78 3.3.1 Pharmacophore Hypotheses ................................................................. 78 188.8.131.52 1S0I pharmacophore model ...................................................... 78 184.108.40.206 Holo pharmacophore model ...................................................... 81 220.127.116.11 Apo pharmacophore model ....................................................... 84 3.3.2 Screening Results Using TcTS Pharmacophore Models ....................... 87 18.104.22.168 Screening results for the 1S0I pharmacophore ......................... 87 22.214.171.124 Screening results for the holo pharmacophore ......................... 91 126.96.36.199 Screening results for the apo pharmacophore .......................... 93 3.3.3 Analysis of the Most Promising TcTS Inhibitors ..................................... 96 188.8.131.52 ZINC13359679 ......................................................................... 96 184.108.40.206 ZINC0257613 2 ....................................................................... 100 3.4 Concluding Remarks ....................................................................................... 104 4 COMBINED MOLECULAR DOCKING, MOLECULAR DYNAMICS AND BINDING FREE ENERGY CALCULATIONS TO DESIGN AND DISCOVER NOVEL INHIBITORS FOR TRYPANOSOMA cruzi TRANS SIALIDASE .............. 106 4.1 Preliminaries ................................................................................................... 106 4.2 Methods .......................................................................................................... 107 4.2.1 Acquisition of Compounds ................................................................... 108 220.127.116.11 Molecular libraries ................................................................... 108 18.104.22.168 De nov o design ....................................................................... 109 22.214.171.124 Rational drug design ............................................................... 109 4.2.2 Docking Potential Ligands with Glide ................................................... 109 126.96.36.199 Receptor grid generation ........................................................ 109 188.8.131.52 Molecular docking ................................................................... 110 4.2.3 De novo Drug Design .......................................................................... 110 4.2.4 Molecular Dynamics ............................................................................ 111 4.2.5 Free Energies of Binding ..................................................................... 111 4. 3 Results ............................................................................................................ 111 4.4 Discussion ...................................................................................................... 116 4.4.1 Best Potential Inhibitors ....................................................................... 117 184.108.40.206 DANA Result 034 .................................................................... 117 220.127.116.11 Oseltamivir Result 70 .............................................................. 122 18.104.22.168 PubChem 54353125 ............................................................... 125 22.214.171.124 ZINC16247520 Rank 10 ......................................................... 129 126.96.36.199 Sialic Acid Rank 1 ................................................................... 135 188.8.131.52 ZINC04939766 ....................................................................... 137 184.108.40.206 Zanamivir phosphonate .......................................................... 142 4.4.2 De novo Design Candidate Combining ZINC Fragments .................... 147 4.5 Concluding Remarks ....................................................................................... 149
8 5 MMPBSA.py : AN EFFICIENT PROGRAM FOR ENDSTATE FREE ENERGY CALCULATIONS .................................................................................................. 151 5.1 Preliminaries ................................................................................................... 151 5.2 Theory and Methods ....................................................................................... 152 5.2.1 Stability and Binding Free Energy Calculations ................................... 153 5.2.2 Free Energy Decomposition ................................................................ 155 5.2.3 Entropy Calculations ............................................................................ 156 5.3 Results and Dis cussion ................................................................................... 157 5.3.1 General Workflow ................................................................................ 157 5.3.2 Poisson Boltzmann: MM PBSA ........................................................... 160 5.3.3 Generalized Born: MM GBSA .............................................................. 161 5.3.4 Reference Interaction Site Model: MM/3D RISM ................................. 163 5.3.5 Free Energy Decompo sition ................................................................ 164 5.3.6 Alanine Scanning ................................................................................. 165 5.3.7 Solute Entropy ..................................................................................... 166 5 .3.7.1 Normal Mode Analysis ............................................................ 166 220.127.116.11 Quai harmonic Approximation ................................................ 167 5.3.8 Running in Parallel ............................................................................... 168 5.3.9 Differences to mm_pbsa.pl .................................................................. 169 5.4 Concluding Remarks ....................................................................................... 170 6 CONCLUSIONS ................................................................................................... 172 APPENDIX: SUPPLEMENTARY MATERIAL .............................................................. 175 A.1 Hit Compounds From epharmacophore Screening ....................................... 175 A.2 Potential Inhibitors of TcTS From Molecular Docking and de novo Design .... 191 LIST OF REFERENCES ............................................................................................. 210 BIOGRAPHICAL SKETCH .......................................................................................... 224
9 LIST OF TABLES Table Page 3 1 Pharmacophore sites of the 1S0I model. ............................................................ 79 3 2 Pharmacophore sites of the TcTS holo model.. .................................................. 82 3 3 Pharmacophore sites of the TcTS apo model. ................................................... 85 3 4 The best five comp ounds from screening results of the 1S0I pharmacophore according to MM GBSA binding free energies. .................................................. 88 3 5 The best five hit compounds from screening results of the holo pharmacophore according to MMGBSA binding free energies.. ....................... 92 3 6 The best five hit compounds from screening of the TcTS apo pharmacophore according to MM GBSA binding free energies. .................................................. 95 4 1 List of ZINC molecular libraries and their corresponding number of compounds used for virtual screening. ............................................................. 108 4 2 The best potential inhibitors of TcTS fr om virtual screening and de novo drug methods.. .......................................................................................................... 112 A 1 All hit compounds from screening results of the 1S0I pharmacophore according to MM GBSA binding free energies.. ............................................... 175 A 2 All hit compounds from screening results of the holo pharmacophore according to MM GBSA binding free energies.. ............................................... 180 A 3 All hit compounds from s creening results of the apo pharmacophore according to MM GBSA binding free energies.. ............................................... 187 A 4 Compounds from molecular docking and de novo design considered as potential inhibitors of TcTS. .............................................................................. 191
10 LIST OF FIGURES Figure Page 1 1 Romana's sign, a typical sign of Chagas' disease, occurs on the eyelid after being bitten by an infected Triatomi ne bug. ........................................................ 19 1 2 Structures of drugs currently available as treatments of Chagas' disease. Specifically, a) benznidaole and b) nifurtimox .................................................... 20 1 3 Life cycle of T. cruzi as it progresses through triatomine bugs and humans. ..... 22 1 4 Image of T. cruzi in the trypomastigote form taken from a blood smear of an infected patient. .................................................................................................. 23 1 5 The general structure of sialic acid, where R=H is neuraminic acid, R=CH3CO is Nacetyl neuraminic acid, and R=HOCH2CO is Nglycolyl neuraminic acid.. ................................................................................................ 24 1 6 Structure of TcTS showing the three major domains. ......................................... 26 1 7 Depictions of the different regions in the TcTS active site with sialyl lactose bound.. ............................................................................................................... 28 1 8 Chemical mechanism of T. cruzi trans sialidase, showing the Tyr342/Glu230 hydrogen transfer and the nucleophilic attack of Tyr342 on the anomeric carbon of the substrate. ...................................................................................... 31 1 9 Conformation of sialyl lactose at the transition state showing the planarity of the carbocation of sialic acid, as well as the positions of the catalytic residues Tyr342, Glu230, and Asp59. ............................................................................... 32 1 10 Overlay of TcTS strutures displaying the flexibility of the Tyr119 (right) and Trp312 (left) flaps. .............................................................................................. 35 1 11 Hydrogen bonding n etwork of the Trp312 loop (red) .......................................... 36 2 1 Schematic diagram of common models for a substrate binding to the active site of an enzyme. .............................................................................................. 44 2 2 Depiction of how Glide scores a hydrogen bond based on the distance between the heavy atom (X) and hydrogen atom (H). ........................................ 56 2 3 An example of a pharmacophore model. ............................................................ 61 2 4 Workflow summary of epharmacophore generation from docked fragments using Schrdinger. .............................................................................................. 63
11 2 5 The workflow used in this study using a combination of molecular docking, MD, and binding free energy calculations to discovery new potential inhibitors of TcTS. .............................................................................................................. 70 3 1 The required matches for the 1S0I pharmacophore used for screening lead compo unds. ........................................................................................................ 80 3 2 The required sites for the holo pharmacophore model used for screening lead compounds. ................................................................................................ 83 3 3 The required sites for the TcTS apo pharmacophore model used for screening lead compounds. ................................................................................ 86 3 4 The RMSD of TcTS and ZINC13359679 using the initial frame as reference. ... 97 3 5 The MM GBSA binding free energy of ZINC13359679 bound to TcTS as a function of time for a 10ns MD simulation. ........................................................ 98 3 6 The proteinligand interactions between TcTS and ZINC13359679. ................ 100 3 7 The RMSD of TcTS and ZINC02576132 using the initial frame as reference. 101 3 8 The binding free energy of ZINC02576132 bound to TcTS as a function of time for a 10ns MD simulation. ........................................................................ 102 3 9 The main interactions between TcTS and ZINC02576132 observed during a 10ns MD simulation. ........................................................................................ 103 4 1 A plot of MM GBSA binding free energies compared to Glide XP docking free energies. ........................................................................................................... 116 4 2 The RMSD of TcTS and DANA Result 034 using the initial frame as reference. ......................................................................................................... 118 4 3 Depiction of the flexibility of the naphthalene region (green) of DANA Result 034 compared to the rest of the ligand. ............................................................ 118 4 4 The MM GBSA binding free energy of DANA Result 034 bound to TcTS as a function of time for a 50ns MD simulation. ...................................................... 119 4 5 Modifications made to DANA Res ult 034. ......................................................... 120 4 6 The RMSD of TcTS and Oseltamivir Result 70 using the initial frame as reference. ......................................................................................................... 123 4 7 Depiction of the rotat ion conformational change observed for Oseltamivir Result 070 during MD around 4 ns. .................................................................. 123
12 4 8 The MM GBSA binding free energy of Oseltamivir Result 70 bound to TcTS as a function of time for a 10ns MD simulation. ............................................... 124 4 9 The 2D structure of chEMBL 198898 and PubChem 54353125. ...................... 125 4 10 The RMSD of TcTS and PubChem 54353 125 using the initial frame as reference. ......................................................................................................... 126 4 11 The MM GBSA binding free energy of PubChem 54353125 bound to TcTS as a function of time for a 110ns MD simulation. ............................................. 126 4 12 Proteinligand interactions between TcTS and PubChem 54353125. .............. 127 4 13 Representations of the conformational changes for two TcTS residues duri ng MD of the TcTS PubChem 54353125 complex. ............................................... 128 4 14 The 2D structures o f ZINC16247520 and a derivative ...................................... 130 4 15 Depiction of ZI NC16247520 interacting with the arginine triad of TcTS. .......... 131 4 16 The RMSD of TcTS and ZINC16247520 Rank 10 using the initial frame as reference. ......................................................................................................... 132 4 17 The MM GBSA binding free energy of ZINC16247520 Rank 10 bound to TcTS as a function of time for a 10ns MD simulation. ..................................... 132 4 18 The hydrogen bonding network between Z INC16247520 Rank10 and TcTS residues Arg93, Asp59, and Ser118. ................................................................ 134 4 19 The RMSD of TcTS and Sialic Acid Rank 1 using the initial frame as reference. ......................................................................................................... 135 4 20 Overlay of 250 evenly spaced conformations of Sialic Acid Rank 10 bound to TcTS during a 10ns MD simulations. ............................................................... 136 4 21 The RMSD of TcTS and ZINC04939766 using t he initial frame as reference. 139 4 22 The MM GBSA binding free energy of ZINC04939766 bound to TcTS as a function of time for a 10ns MD simulation. ...................................................... 139 4 23 The binding mode of ZINC04939766 with the arginine triad of TcTS. .............. 140 4 24 The hydrogen bonding network between ZINC04939766 and the Asp96/Glu230 motif on TcTS. ........................................................................... 141 4 25 The RMSD plots from TcTS ZP1 and TcTS ZP62 using the initial frame as reference. ......................................................................................................... 143
13 4 26 The MM GBSA binding free energy of ZP1 (blue) and ZP62 (purple) bound to TcTS as a function of time for a 10ns MD simulation. ..................................... 143 4 27 The hydrogen bonding network between the phosphonate group of ZP62 and the arginine triad of TcTS. ................................................................................ 145 4 28 The RMSD of TcTS and the Super fragment using the initial frame as reference. ......................................................................................................... 148 4 29 The MM GBSA bindi ng free energy of the Super fragment bound to TcTS as a function of time for a 72ns MD simulation. .................................................... 148 4 30 The position of the Super fragment relative to the TcTS active site at 0 ns (red) a nd 72 ns (blue) showing the dissociation of the ligand during MD. ........ 149 5 1 Thermodynamic cycles for common free energy calculations. ......................... 152 5 2 General workflow for performing endstate calculations with MMPBSA.py ..... 158 5 3 MMPBSA.py scaling comparison for MM PBSA and MM GBSA calculations on 200 frames of a 5910atom complex. .......................................................... 168
14 LIST OF ABBREVIATIONS TERM: Definition CI Covalent intermediate CDC Centers for Disease Control and Prevention DANA 2,3 dehydro3 deoxy N acetylneuraminic acid GA Genetic algorithm GB Generalized Born MC Michaelis c omplex MD Molecular dynamics MMGBSA Molecular Mechanics/Generalized Born Surface Area MMPBSA Molecular Mechanics/Poisson Boltzmann Surface Area MTP Multiple Trajectory Protocol NAB Nucleic Acid Builder PB Poisson Boltzmann QM Quantum Mechanics QM/MM Quan tum mechanical/molecular mechanical RISM Reference Interaction Site Model RMSD Root meansquare deviation SAPA Shed acute phase antigen SASA Solvent accessible surface area SP Soft Precision STP Single Trajectory Protocol TcTS Trypansoma cruzi trans sialid ase VS Virtual screening XP Extra Precision
15 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy COMPUTATIONAL DRUG DESIGN AND DISCOVERY FOR TRYPANOSOMA cruzi TRANS SIALIDASE By Bill Miller III August 2013 Chair: Adrian E. Roitberg Major: Chemistry Chagas disease is a lethal condition that affects millions of people worldwide, although the highest prevalence of Chagas is found in rural regions of Central and South America. The World Health Organization has recognized Chagas as a neglected tropical disease due to its low prevalence in developed countries. Although two drug treatments are available, they only work during the early phase of infection and incur unwanted side effects for patients. Thus, there is a critical need for new drugs in the treatment of Chagas disease. Trypanosoma cruzi the protozoa that causes Chagas disease, expresses a trans sialidase (TcTS) enzym e that is vital to the parasitic life cycle due to its role in host cell invasion and defiance of the host immune system. TcTS specifically catalyzes the transfer of sialic acids from host glycoconjugates to glycoconjugates attached to the parasitic cell s urface. Thus, TcTS is an ideal biological target for the design of drugs against Chagas disease. Despite high structural and sequence similarities with neuraminidase enzymes, there are currently no known strong inhibitors of TcTS. In this study, computati onal techniques are employed to identify novel lead compounds in the development of
16 inhibitors against TcTS. Pharmacophore models were developed using molecular docking of fragment compounds to TcTS. A virtual screening of the ZINC Leads molecular database using the pharmacophore models resulted in 82 novel inhibitor scaffolds. Molecular dynamics (MD) simulations followed by free energy of binding calculations were used to further refine the binding modes and relative theoretical binding affinity of the new scaffolds. Furthermore, virtual screening and de novo drug design procedures were utilized that involved docking of millions of compounds to TcTS in the pursuit of stronger inhibitors. The most promising candidates from docking were also subjected to furt her MD and free energy of binding calculations. Computational drug design would be useless without the development of methods to estimate the free energies of binding of compounds. A newly developed program, MMPBSA.py is introduced that was designed to quickly approximate the relative binding free energies of compounds from MD simulations.
17 CHAPTER 1 INTRODUCTION 1.1 Prologue In this chapter, a description of Chagas disease (American trypanosomiasis) will be introduced, including its transmission, epidemiology clinical symptoms, and current prevention and treatment methods. The parasitic cause of Chagas disease, Trypanosoma cruzi ( T. cruzi ), will be described in the following section. The subsequent sections will introduce the structural and mechanistic detai ls of T. cruzi trans sialidase (TcTS), which plays several key roles in the parasitic life cycle. Finally, the last section will include a review of the current efforts to design and discover strong, specific inhibitors of TcTS. 1.2 Chagas Disease Chagas dis ease1 is a lethal disease that currently affects approximately 10 million people, while the death toll each year is estimated at 12,500 people worldwide.2 Although the disease is found mostly in underprivileged regions of Central and South America, recent immigration and international travel has ca used Chagas to spread to other parts of the world leaving nearly 25 million more people at risk of infection.3 In fact, estimates suggest the re may be as many as 1 million cases of Chagas in the United States alone,4 with thousands of other confirmed cases in Canada, Europe, Australia, and Japan.5 Chagas relatively low prevalence in developed countr ies has left it mostly ignored by the international community, prompting the World Health Organization to consider it one of 17 neglected tropical diseases.6 The globalization of C hagas has created more attention and research recently to combat the disease.7
18 In 1909, Brazilian physician Dr. Carlos Chagas fully described the disease named after him, including identifying and detailing the complete life cycle of the flagellated parasite Trypanosoma cruzi determine by him as the lone cause of the diseas e.1 T. cruzi can be transferred between humans by many mechanisms blood8 or organ transfusions,9,10 from mother to baby during pregnancy,11 and ingesting contaminated food or drink.12,13 But the most common method of infection is being bitten by the insect vector known as triatomines (members of the family Reduviidae and subfamily Triatominae), which are bloodsucking insects more commonly know n as kissing bugs in some countries that live in the walls and roofs of poor buildings. Chagas disease has two clinical stages acute14 and chronic.15 The acute stage is often symptom free, but can be characterized by the presence of swelling and redness at the infection site (typically the cut in the skin caused by the triatomine feeding) that lasts for weeks or months after infection. When the swelling is located on the eyelid, it is known as Romaas sign ( Figure 11 ) The acute phase can also be associated with headaches, fever, rash, fatigue, vomiting, and diarrhea.16 The initial symptoms are all either nonexistent or not specific to only Chagas, making the disease extremely difficult for physicians to diagnose. Following the acute phase, the symptoms (if present) fade and the condition appears to become dormant in the patient for years or even decades. However, during this period T. cruzi is hidden in specific tissues of the body, typically the heart or intestinal tract muscles. The chronic stage can be characterized by much more severe symptoms, usually arising from swelling of the tissues where T. cruzi resides. Parasites in the intestinal tract typically result in damage of the digestive system, such as megacolon or megaesophagus, while heart damage is
19 associated with arrhythmia, cardiomyopathy, and complete heart failure.17 However, the heart and intestinal problems are not mutually exclusive, as many in the chronic stage actually suffer from both sets of symptoms. The tissue enlargement is the result of an inflammatory response to cell death induced by the parasite, and is often fatal if untreated.18,19 Figure 11 Romana's sign, a typical sign of Chagas' disease, occurs on the eyelid after being bitten by an infected Triatomine bug. This image is taken with permission from the CDC website ( www.cdc.gov ), which is an online source for credible health information and is the official Web site of the Centers for Disease Control and Prevention (CDC). Currently, benznidazole20 and nifurtimox21 ( Fi gure 12 ) are the only two administered drug treatments for Chagas disease, although the details of exactly how they work are unknown. Both drugs, which are only distributed by the Centers for Disease Control and Prevention (CDC), can be effective when c orrectly administered, but are most beneficial when treatment occurs in the acute phase of the disease, which is problematic given the difficulty in diagnosing Chagas early. Treatment is lengthy and there can be many potential side effects to the drugs, o ften resulting in treatment being ended prematurely either by the patient or physician.22 The potential side effects from nifurtimox include anorexia, psychic alterations, excitability or sleepiness, and digestive
20 or intestinal problems (such as nausea, vomiting constipation, or diarrhea). Benznidazole side effects mostly include skin manifestations (dermatitis, hypersensitivity, and skin blisters) or reduction in bone marrow. Some strains of T. cruzi are also known to be resistant to nifurtimox or benznidazole, and often when it is resistant to one of the drugs, it is resistant to the other.23 Figure 12 Structures of drugs currently available as treatments of Chagas' disease. Specifically, a) benznidaole and b) nifurtimox Benznidazole and nifurtimox are anti fungal treatments that combat the parasite directly, but recent progress has been made in the discovery of specific biochemical enzymes and pathways to target that are important to the T. cruzi life cycle. Studies indicate triazole derivatives can inhibit ergosterol synthesis and make promising drug candidates to treat patients in both the acute and chronic phases of Chagas disease.24 Cruzipain, a cysteine protease responsible for most of the proteolytic activity of T. cruzi is an ideal drug target that can help prolong the lifespan of infected individuals with the proper inhibitor.25,26 Enzymes that have no mammalian equivalent make perfect drug targets for the design of selective T. cruzi treatments. One example of such a n enzyme is trypanothione reductase that can induce oxidative stress on the parasite if disabled by an inhibitor.27 Studies have also had success attempting to inhibit hypoxanthinegua nine phosphoribosyl transferase,28 the synthesis pathway of phosphatidylcholine,29 the
21 isoprenoid bi osynthetic pathway,30 and inositol metabolism31 in T. cruzi in the pursuit of novel treatments for Chagas disease. The lack of effe ctive treatments has only helped increase the focus on prevention measures against Chagas disease. There are currently no vaccines approved for use against the disease, but promising results have been found recently on infected mice using Trypanosoma rangeli epimastigotes to prevent T. cruzi infection.32 Another means of prevention available to the public directly targets the triatomines using insecticides.33 Most p reventative measures impact the life style of people in infected regions, and are intended to prevent infection from the insect vector. People are encouraged to solidify their home with concrete floors, plastered walls, and corrugated galvanized iron roofi ng to prevent the insect from entering the home. A still experimental method invented by Spanish chemist Pilar Mateo currently being tested in Bolivia uses paint infused with insecticides in microcapsules on the walls of homes to prevent triatomines from entering the living areas of the house.34 More personal protection involves using nets covering the bed while sleeping and proper hygienic measures to prevent the consumption of contaminated food or liquid. Government regulation has helped with the implementation of laws to screen donated blood and tissue for T. cruzi infection prior to permitting its use in blood transfusions and other medical procedures.35 Furthermore, organizations, such as the American Red Cross that screens all donated bl ood, are beginning to test more regularly for Chagas disease. Despite the recent scientific advancements, there is still no single vaccine or drug approved to treat Chagas disease. 1.3 Trypanosoma cruzi The etiological agent of Chagas disease is the flagell ated protozoa Trypanosoma cruzi ( T. cruzi ), which can be generally described as physically consisting
22 of a DNA containing organelle connected to a flagellum. Although some details of the evolution of the parasitic life cycle ( Figur e 13 ) remain unknown, it is generally accepted that T. cruzi has three distinct life cycle stages that can be observed using microscopy trypomastigote, amastigote, and epimastigote. Figure 13 Life cycle of T cruzi as it p rogresses through triatomine bugs and humans. Source: CDC website ( www.cdc.gov ). When the bloodsucking triatomines feed on an infected mammal, T. cruzi is primarily in the trypomastigote form.36,37 The trypomastigote stage cannot replicate and is characterized by the parasite with a large flagellum connected to the nucleus ( Figure 1 4 ). Upon entering the midgut of the insect vector, the trypomastigotes differentiate into the amastigote form, which are round, have a very short flagellum, and can undergo replication. The amastigotes quickly undergo differentiation into the epimastigote form, although this transition is reversible and T. cruzi is likely to be found in both forms within the insect vector. Epimastigotes can reproduce and are significantly longer than amastigotes with a clear and distinct flagellum. The epimastigotes proliferate as they progress through the intestinal tract of the insect and attach to the inside wall of the
23 rectum where they differentiate into metacyclic trypomastigotes,38,39 which are infective in mammals. During the triatomines next meal, the trypomastigotes are deposited on the skin of the mammal in the insects feces and make their way into the blood stream of the mammalian host through the skin abrasion created by the insect during feeding. Figure 14 Image of T. cruzi in the trypomastigote form taken from a blood smear of an infected patient. Source: CDC website ( www.cdc.gov ). Upon entering the mammalian blood, the infective trypomastigotes attempt to enter the host cells by interacting with the host cell surface. Invasion of the mammalian cells is mediated by the interaction of trans sialidase and neuraminidase enzymes on the trypomastigote surface with the host cell surface. Once the trypomastigotes enter the host cell and reach the intracellular cytoplasm they differentiate back into the amastigote form to reproduce.40 The amastigotes divide and multiply repeatedly followed by differentiation back into the trypomastigote form triggered by a yet unknown cause. T. cruzi multiplication fills the interior of the host cells until the pressure forces the cell wal ls to rupture, releasing infective trypomastigotes back into the host blood stream. This process of host cell invasion and induced cell death by T. cruzi continues
24 for years inside the mammalian host as the infected individual progresses into the chronic s tage of Chagas disease. The host immune system is able to create a balance with the parasite that slows the progression of the disease into the chronic stage. The level of trypomastigotes in the blood stream also makes the mammal a reservoir of infective parasites waiting to be ingested during a meal by another insect vector. 1.4 Trypanosoma cruzi T rans sialidase Sialic acids ( Figure 15 ) are neuraminic acid (5amino 3,5 dideoxy D glycero D galacto2 nonulosonic acid) derivatives that have many important cellular functions.41 In particular, their location at the terminal position of cell surface glycoconjugates makes sialic acids ideally suited for intercellular communicati on, such as intercellular transport and immune system recognition.42 T. cruzi is unable to synthesize its own sialic acids de novo,43 which prevents parasitic cells from invading the host mammalian cells and leaves it vulnerable to attacks from the host immune system. To combat this dilemma, prior to host cell invasion the parasite expresses a glycophosphatidyl inositol (GPI) anchored surface enzyme called trans sialidase (TcTS). Figure 15 The general structure of sialic acid, where R=H is neuraminic acid, R=CH3CO is Nacetyl neuraminic acid, and R=HOCH2CO is Nglycolyl neuram inic acid. Traditional carbon atom numbering is shown along with the location of the anomeric carbon.
25 TcTS, expressed only in the trypomastigote stage of the T. cruzi life cycle, efficiently catalyzes the transfer of sialic acid from the host cell surface to glycoconjugates attached to the parasitic cell surface.44,45 The importance of TcTS, which has no human analog, to T. cruzi s survival and ability to invade mam malian cells makes it an interesting drug target to combat Chagas disease. 1.4.1 TcTS Structure The X ray crystal structure of free and liganded TcTS was resolved more than a decade ago by Buschiazzo et al .46 For the holo complex (defined as the structure of the enzyme with substrate bound), the crystal structure had a resolution of 1.58 with 94.3% completeness, and an R factor of 16.3%. The structure with DANA bound to TcTS was resolved to 2 with 85.9% completeness and an R factor of 16.3%. Finally, for the apo conformation (defined as the structure of TcTS with no ligand in the active site), the crystal structure had a resolution of 2.25 with 99.7% completeness and an R factor of 21.8%. TcTS has a 70 kDa globular core connected to a variable length (100500 residues) hydrophilic region called the shed acute phase antigen (SAPA) consisting of repeating 12residue motifs.47 The globular core consists of the N terminal catalytic domain (residues 1371) connected to a C termin al lectin like domain (residues 395helix linker region (residues 372394) ( Figure 16 ). Finally, the C terminal lectin helix region (residues 614626). The catalytic and lectinlike domains are both similar in structure and sequence to related sialidases,48,49 with the tertiary structure of the catalytic domain consisting of a six helix linker region between the two domains is more hydrophobic, resulting in significantly more
26 buried surface area (2550 2 for TcTS, 13001600 A2 for sialidases).46 This observation suggests the protein core is more rigid, implying the lectinlike domain is not directly involved in catalysis giv en its spatial proximity to the catalytic domain. Although it is unlikely to be directly involved in transglycosylation, the C terminal lectin like domain has the potential have a role in allosteric binding for substrate specificity. Figure 16 Structure of TcTS showing the three major domains. The catalytic domain is shown in orange ribbons, the lectinlike domain is shown in blue, and the alphahelix linker region is shown in brown. Sialic acid is shown in silver balls and sticks within the active site of the catalytic domain. Coordinates are from PDB code 1S0I.
27 The TcTS active site ( Figure 17 ) also shares several conserved residues and structural similarities with the sialidase family.48,50 In particular, the arginine triad (Arg35, Arg245, and Arg314) stabilizing the substrate carboxylic acid is conser ved in TcTS compared to related neuraminidases, along with Glu357 that helps stabilize the side chain of Arg35. The catalytic residues Asp59, Glu230, and Tyr342 were proposed based on their similarities to the corresponding residues in sialidases. Other residues involved in substrate binding were also conserved in TcTS Val95, Trp120, and Leu176that form the hydrophobic pocket where the N acetyl group of the substrate binds. Compared to related sialidases, the binding site of TcTS is narrower and more hydrophobic, primarily due to the presence of two aromatic residues (Tyr119 and Tyr248) at the mouth of the binding pocket not present in sialidases. These residues (along with Trp312) galactose acceptor molecule in the binding pocket of TcTS, which at least in part prevents sialidases from performing transglycosylation because sialidases lack these aromatic residues.46 The binding pocket of TcTS ( Figure 17 ) can be divided into two main regions the sialic acid binding site and the galactose acceptor/donor bi nding site. The substrate sialic acid binds deep into the active site, with the arginine triad motif stabilizing the carboxylic acid. Glu230 and Tyr342 of the catalytic motif are positioned below the anomeric carbon of the sugar. The N acetyl group of sial ic acid occupies the hydrophobic region near Val95 and Leu176, while the glycerol side chain resides in a more hydrophilic area of the active site near Trp120, Thr121, and Ser229. The galactose binding site is a narrow, hydrophobic region near the mouth of the active site located between the side chains of Tyr119 and Trp312 that helps position the acceptor
28 galactose molecules for catalysis. Tyr119 and Trp312 lie parallel to one another with galactose situated between the two aromatic rings. Fi gure 17 Depictions of the different regions in the TcTS active site with sialyl lactose bound. The TcTS active site regions include a) the sialic acid binding site, b) the galactose binding site, c) the N acetyl binding region, and d) the glycerol tail binding region.
29 1.4.2 TcTS Reaction Mechanism The details of the TcTS catalytic mechanism have also been under investigation for more than a decade. Interestingly, TcTS differs from sialyltransferases because TcTS transfers sialic acid with retention of configuration,45 suggesting a unique mechanism. Kinetic isotope studies were the fi rst to suggest a nucleophilic enzyme residue,51 but elucidation of the X ray crystal structure of TcTS showed Tyr342 as the only possibility based on proximity of the side chain with the anomeric carbon of the substrate46 and that was deemed unlikely because the pKa value of free tyrosine is too high to make Tyr342 a suitable nucleophile. However, Tyr342 was soon shown to not only be the nucleophilic residue, but was also part of a covalent intermediate formation between the enzyme and sialic acid.52 The hydroxy side chain of tyrosine typically has a pKa value near 10, so the environment of the active site was clearly affecting the protonation state of Tyr342. Specifically, the side chain of Glu230 is positioned perfectl y to accept the Tyr342 proton, thus altering the pKa of the tyrosine and facilitating nucleophilic attack of the substrate by deprotonated Tyr342.53 Recent computational results suggest this proton transfer is stable within the TcTS active site only after substrate binding because of the stabilization of the resulting charge density on the nucleophilic oxygen atom of Tyr342.54 Before the substrate binds, Arg245 of the arginine triad forms a hydrogen bond with the side chain of Glu230, making Glu230 unlikely to accept a hydrogen transfer from Tyr342. However, upon substrate binding the arginine triad motif, including Arg245, hydrogen bonds with the carboxylate of sialic acid, creating a more suitable environment for Glu230 to acceptor a proton. Upon deprotonation, the nucleophilic oxygen atom of Tyr342 contains a larger electron
30 density when the substrate is bound, making the interacting of tyrosinate with the side chains of Arg35 and Arg314 more favorable with sialy l lactose in the active site. The attack of Tyr342 to form a covalent intermediate does not fully explain the lactose leaving group must also have a corresponding residue responsible for its deprotonation prior to its departure from the act ive site. Protonated Asp59 performs the role of proton donor for TcTS catalysis because it is ideally situated for proton donation to the leaving group due to a conformational distortion of sialyl galactose in a pseudoax ial position.53 Additionally, the entrance and exit of the donor and acceptor molecules, respectively, is aided by the flexible loop of Trp312 that positions and orients the sugar in the acceptor/donor binding region of the active site.55 In general, the TcTS catalytic mechanism can now be rationalized as depicted in Figure 18 Pierdominici Sottile et al recently described the pathway and energetics of the mechanism from the Michaelis complex (MC)53 to the covalent intermediate (CI)52 via nucleophilic attack of Tyr342 using computational methods.56 The computed free energies showed a reaction barrier of +20.80 0.70 kcal/mol and an overall free energy change of 0.89 0.43 kcal/mol from the MC to the CI. A negative free energy change for this reaction helped confirm the stability of the CI during the reaction. A carbocation was assumed to be formed during the reaction, but it was unknown if this state was a transition state or intermediate state until Pierdominici et al .56 confirmed the carbocation state as the transition state ( Figure 18 ). The carbocation arises from the dissociation of the lactose leaving group from sialic acid prior to the nucleophilic attack of Tyr342 on the anomeric carbon of sialic acid. The elucidation of the transition state is vital for drug
31 design efforts because it is well known that the best inhibitor for an enzyme will mimic the transition state conformation.57,58 Figure 18 Chemical mechanism of T. cruzi trans sialidase, showing the Tyr342/Glu230 hydrogen transfer and the nucleophilic attac k of Tyr342 on the anomeric carbon of the substrate. Asp59 is also shown as a hydrogen donor for the leaving group. Note that sialic acid is rotated 180o compared to its orientation in Figure 15 The TcTS transition state conform ation is shown in Figure 19 where sialic acid is shown as a carbocation and the sugar ring is planar at the anomeric carbon Although the transition state is not a pure carbocation because full dissociation of the leaving group does not occur prior to nucleophilic attack, the confirmation of little partial positive charge on the anomeric carbon is still helpful for drug design. The presence of an atom with partial positive charge character on an inhibitor near the hydroxyl group of Tyr342 would be logical. Furthermore, the transition state depicts the sialic acid sugar ring in a semi chair conformation around the anomeric carbon compared to the traditional boat or
32 chair conformations found in the MC and CI, respectively. This obse rvation suggests an aromatic ring of an inhibitor may be preferable for binding in the sialic acid region of the active site as it would be a better mimic of the transition state conformation. This is ideal for medicinal chemists because an aromatic ring i s preferred over a sugar moiety in an inhibitor because it is more rigid with less rotatable bonds. Figure 19 Conformation of sialyl lactose at the transition state showing the planarity of the carbocation of sialic acid, as well as the positions of the catalytic residues Tyr342, Glu230, and Asp59. Note that the hydrogen transfer from Asp59 to the leaving group has already occurred, while all bonds in transition are
33 shown in orange. For clarity, all hydrogen atoms not involved in catalysis have been removed. Further calculations by Pierdominici Sottile et al .56 showed the active site provided 21.09 kcal/mol more stabilization for the transition state than the MC, reemphasizing the idea that inhibitors should be designed based on the transition state conformation of the substrate. The free energy w as decomposed by residue to determine exactly which residues were most important for transition state stabilization. Six residues Arg35, Arg53, Asp96, Asp247, Arg311, and Glu230/Tyr342provided the most stabilization (greater than 5 kcal/mol), where the Gl u230/Tyr342 motif was considered a single residue because of the proton shuttle between them during catalysis. Arg35, Asp96, and Glu230/Tyr342 all directly stabilize the substrate in the transition state, while Arg53, Glu247, and Arg311 indirectly help sta bilize the complex by forming favorable interactions with enzymatic residues involved in catalysis or positioning of the substrate. Previously, only residues that were directly involved in the reaction mechanism were known to be important for stability of the transition state. Detailed analysis of the stabilization of the transition state is important for drug design efforts targeting TcTS. The importance of the Glu230/Tyr342 motif was already well understood because of its role in catalysis, but other resi dues important for transition state stabilization namely Arg35 and Asp96 provide medicinal chemists with TcTS residues to target with rational drug design. Future inhibitors can be designed to interact directly with these residues, while secondary consider ation should also be given to the other major transition state stabilizing residues Arg53, Asp247, and R311. This information is vital for medicinal chemists when designing specific inhibitors of TcTS. For example, the transition state structure of TcTS and related sialidases led to
34 the design of 2,3dehydro3 deoxy N acetylneuraminic acid (DANA), a strong transition state analog inhibitor of sialidases that only weakly inhibits TcTS. 1.4.3 TcTS Dynamics The structures derived from theory, NMR, and X ray crystall ography are vital to drug design, but proteins are dynamic molecules that cannot be fully described by a single conformation. Recently, Demir et al .59 used classical molecular dynamics (MD) computer simulations to investigate the dynamics of TcTS in the bound (holo) and unbound (apo) state. The ligands used in the holo complexes included the covalent intermediate, the substrate silayl lactose, and DANA A comparison of the dynamics from these simulations showed significant variability of the acceptor binding site regions, specifically the Trp312 loop, compared to the conformations proposed by the available X ray crystal structures. This region of TcTS is vital for trans sialidase activity because Tyr119 and Trp312 help orient the acceptor lactose in the active site for catalysis. The Tyr119 loop was mostly rigid throughout the simulations, but the Trp312 loop consisting of residues 310314 showed varying flexibility depending on the particular ligand present in the active site ( Figure 110). The Tyr119Trp312 distance was 14.714.8 (closed conformation) for the crystal structures, which was consistent with the sialyl lactose bound TcTS simulation results. However, MD simulations of the DANA bound TcTS and TcTS covalent intermediate complexes showed a more open protein conformation with Tyr119Trp312 distances of 16 20 (semi open conformation). The difference in loop flexibility between the X ray crystal structure and the conformations generated from MD is likely the result of a glycerol molecule present in the acceptor binding site of these crystal structur es (PDB codes 1MS1 and 2AH2), forming hydrogen bonds with the loop regions and preventing the active site from relaxing to the more
35 open conformation observed with MD. The Trp312 loop was even more flexible in the TcTS apo simulation, with Tyr119 and Trp31 2 reaching as much as 25 apart (open conformation). Crystal contacts and unit cell packing likely prevented this open conformation from being resolved using X ray crystallography. Figure 110. Overlay of TcTS strutures displ aying the flexibility of the Tyr119 (right) and Trp312 (left) flaps. Shown are representative flap orientations of the closed (blue), semi open (orange), and open (red) conformations from MD. The sialyl lactose substrate is shown for reference to depict the location of the binding site. A hydrogen bonding network between residues Lys309, Asp337, Glu338, Asn339, and Arg7 help anchor the Trp312 loop while the interior of the Trp312 loop remains free to move in the apo form of TcTS ( Fi gure 111) The difference between the semi open and open conformation is related to the presence of a ligand in the sialic acid binding site of TcTS that interacts with the arginine triad (Arg35, Arg245, and Arg314), such as DANA and the TcTS covalent in termediate. The carboxylic acid interacting with
36 the Arg314 guanidinium group reduces the flexibility of the Trp312 loop by fixing the position of Arg314, preventing the loop from accessing the open conformation observed in the apo simulation. The side chain conformation of Tyr119 also appeared to affect the flexibility of the Trp312 loop. With the acceptor site occupied, Tyr119 prefers an outward conformation that leaves the side chain interacting with the acceptor lactose. Conversely, simulations with the acceptor site unoccupied showed a preferred conformation with the phenol positioned inward towards the catalytic cleft of the protein. This conformation of Tyr119 disrupts the water network in the active site enough to weaken the hydrogen bonds between th e side chain of Trp312 and water in the active site, allowing the Trp312 loop to become more mobile. Figure 111. Hydrogen bonding network of the Trp312 loop (red). The loop becomes less flexible when Arg314 hydrogen bonds to a ligand, such as DANA.
37 Enzyme mobility is becoming increasingly recognized for its importance in the drug design process,60,61 thus the discovery of the W312 loop flexibility on TcTS using MD has major implications for the future design of inhibitors. Previously, the crystal structures were the only structures available, but the dynamics on the apo, DANA bound, and covalent intermediatebound structures of TcTS provide more conformations to consider for drug design. Furthermore, the simulation results show the importance of targeting the acceptor binding site with potential inhibitors of TcTS. The presence of DANA, a weak inhibitor of TcTS, in the active site diminishes the mobility of the W312 loop compared to the apo dynamics, but DANA does not stabilize the loop as much as sialyl lactose because DANA is not able to directly interact with the Tyr119/Trp312 motif. Thus, a compound is more likely to be a strong inhibitor of TcTS if it interacts with both the sialic acid binding site and the acceptor binding region of the catalytic cleft. 1.4.4 TcTS Drug Design and Discovery Efforts The structural, mechanistic, and dynamics work on T. cruzi trans sialidase has been performed with one ultimate goal in mind: design and discovery of a strong in hibitor that can be used as a drug to treat Chagas disease. Significant effort both computational and experimentally has gone into searching for a TcTS inhibitor, but still no strong inhibitor exists. Nevertheless, drug design and discovery on TcTS has uncovered several interesting lead molecules. Being structurally and sequentially similar to related sialidases such as neuraminidase, naturally led to the consideration of DANA as a potential inhibitor of TcTS given its strong inhibition of influenza neuram inidase as a transition state analog.62 Surprisingly, DANA is a very poor inhibitor of TcTS, with an inhibition constant
38 ( Ki) of only 12.3 mM.63,64 Regardless of the poor inhibitory activity, many derivatives of DANA have been designed attempting to inhibit sialidase and trans sialidase activity with little success.65 Similarly, successful inhibitors of other enzymes with sugar moiety substrates have been assayed for inhibition of TcTS, such as the common glycosidase inhibitor Neu5A 3 S O octyl,66 with no observed inhibition even at mM concentrations. The lack of affinity of TcTS for compounds that inhibit similar enzymes reveals the extreme diff iculty in designing a strong inhibitor of TcTS. Significant research has also been done on designing an irreversible inhibitor that would form a covalent bond with TcTS, preventing further catalysis. The first breakthrough for irreversible inhibitor design of TcTS was 2,3difluorosialic acid, which forms a covalent bond w ith the nucleophilic oxygen of Tyr 342 equivalent to the covalent intermediate in the catalytic mechanism.52 Despite inactivation of the enzyme in a timedependent manner, TcTS fully regained catalytic activity in the presence of lactose unless very high concentrations (20 mM) of 2,3di fluorosialic acid were applied. Buchini et al .67 investigated this obs ervation further, and determined that 2,3 difluorosialic acid could be altered at the C9 position to accommodate the large and hydrophobic region in the lactose binding site, potentially making the inhibitor more specific for TcTS. Addition of an aromatic ring at the C9 position never created an inhibitor worse than 2,3difluorosialic acid, and one substituent was significantly better at inactivating TcTS. Furthermore, although reactivation of TcTS catalysis still occurred in the presence of lactose, higher concentrations of lactose were necessary compared to using 2,3difluorosialic acid. This observation was consistent with the hypothesis that the ligand aromatic ring occupied the lactose binding site, which was confirmed using X ray
39 crystallography, and t hat TcTS inhibitors should target both the sialic acid and lactose binding sites. A unique irreversible inhibitor, 2difluoromethyl 4 nitrophenyl 3,5 dideoxy D glycero D galacto2 nonulopyranosid acid (NeuNAcFNP), was designed by Carvalho et al .68 that surprisingly results in the formation of a covalent bond between two enzymatic residues Asp247 and Arg 245. Unlike the high concentrations necessary for 2,3 difluorosialic acid inhibition to be observed, only 0.6 mM of NeuNAcFNP was necessary to reach 50% inhibition of trans sialidase activity. Furthermore, 0.1 mM and 10 mM concentrations of NeuNAcFNP were able to prevent mammalian cell invasion of T. cruzi by 34.0% and 90.0%, respectively. This inhibitor has proven to be a strong lead candidate as a drug against Chagas disease, but more optimization is necessary to increase the inhibition affinity and specificity for TcTS, such as substitution of an aromatic group at the C9 position of sialic acid as suggested with 2,3difluorosialic acid. Noncovalent inhi bitors have been designed to mimic the lactose binding mode of the substrate. The first such inhibitor was lactitol (and similar lactose derivatives), which directly competes with sialic acid acceptors in vitro and in vivo .69 In fact, sialic acid is preferentially transferred to lactitol 74.3% of the time over typical in vitro acceptor lact ose and N acetyllactosamine. The same group consequently synthesized and assayed derivatives of lactose covalently bound to polyethylene glycol (PEG) or oseltamivir (a sialid acid mimic), but the derivatives showed lower inhibition than lactitol.70,71 A different s tudy used click chemistry72 to synthesize and test a library of 1,4 disubstituted 1,2,3triazole derivatives of galactose modified at the C1 or C6 positions designed to act as acceptor substrates of TcTS .73 The compounds in the
40 library showed weak inhibition of TcTS (i.e. less than 40% inhibition at 1 mM inhibitor concentration), but interestingly did show trypanocidal activity against the trypomastigote form of T. cruzi Y strain at concentrations in study on galactose derivatives examined the ability of TcTS to tolerate various functional groups substituted at the C2, C4, and C6 positio ns .74 Finally, substituted C glycosides have shown some promise as lead drug candidates with dissociation constant ( KD) values as low as 0.16 mM.75 The lack of immediate success in designing strong, specific substrate analog inhibitors of TcTS naturally led to attem pts to discover novel compound scaffolds with inhibitory activity. The first reported inhibitors not mimicking the TcTS substrate were benzoic acid and pyridine derivatives with poor Ki discovered using virtual screening of the Asinex m olecular library using DOCK .76 The same group performed another virtual scree ning of the Evotec database using the GOLD docking software, resulting in the discovery of over 20 unique compound scaffolds yielding inhibitory activity.77 A fluorimetric assay was used to measure the percent inhibit ion for analogs of two of the best scaffolds, resulting in IC50 values in the sub mM range. Sub synthesis and testing of sulfonamidecontaining chalcones and quinoline derivatives.78 Furthermore, a QM/MM study by Lima et al.79 on two of the quinoline derivatives, DHQ and THQ, was reported detailing the binding modes and inhibitory mechanism of these inhibitors in the TcTS active site. However, the most promising novel scaffolds reported to date flavonoids and anthraquinones involved screening of a natural products library .80 The strongest inhibitor analyzed, 6chloro9,10 dihydro4,5,7trihydroxy 9,10-
41 dioxo2 anthracenecarboxylic acid, had a reported IC50 value of 0.58 best inhibitor of TcTS discovered so far. Additionally, this compound was specific to TcTS, with a >170fold specificity for TcTS over human neuraminidase. Despite the discovery of several novel inhibitor scaffolds of TcTS, we still have n ot found a compound with inhibitory activity strong enough to be considered a good drug candidate. However, given the vast size of chemical space, the likelihood that a strong inhibitor exists is a distinct possibility. T he task is difficult and unlikely t o succeed without the efforts and collaboration of many researchers, especially those studying other sialidases, neuraminidases, and trans sialidases because of the similarities between those enzymes and TcTS.
42 CHAPTER 2 METHODS 2.1 Molecular Structure and Function The primary structure of a biological macromolecule (e.g. proteins or nucleic acids) describes the sequence of components amino acids or bases that comprise the molecule as a whole, but gives no details about their threedimensional (3D) structure. The 3D structure of macromolecules is of great interest to structural biologists because the precise folded structure of a macromolecule determines its function. More than simply the approximation of the overall structure, the details of the orientation for all atoms in the molecule help structural biologists elucidate the details of chemical mechanisms for biomolecules, such as proteins and enzymes. These structures are equally important to medicinal chemists designing compounds to inhibit the function of these macromolecules. Many computational methods and techniques are available to medicinal chemists to design and discover novel inhibitors, such as molecular docking, de novo design, molecular dynamics, and free energy calculations. 2.2 Molecular Docking Molecular dock ing attempts to orient a compound (usually a small molecule, or ligand) into a favorable conformation within the confines of macromolecular binding site typically using known threedimensional coordinates for the macromolecule, or receptor. Additionally, a n empirical scoring function is used to estimate the activity of the ligand based on its intramolecular atomic interactions with the receptor. A brief description of molecular docking methods, with specific emphasis on Schrdingers Glide software, is pres ented here.
43 Structural drug design can attribute its start to Nobel laureate Dr. Emil Fischer who proposed the lock and key model for enzymes, describing how the enzyme active site is structurally complementary to the substrate structure as a key would f it directly into a lock ( Figure 21 ).81 Drug design efforts using molecular docking traditionally used the lock and key model to describe the binding process, and th e theory still has a prominent role in many molecular docking algorithms used by current computational software. The binding process involves not only the conformations of the complex, but also the structures of the receptor and ligand separately as they w ould appear in solution. By considering the receptor and ligand as rigid molecules, the number of degrees of freedom involved in the binding process is greatly reduced, which simplifies docking algorithms. The model of rigid molecules here is unphysical, but despite its limitations the assumption has led to many successful molecular docking applications attempting to predict and reproduce experimental proteinligand bound structures.82 85 Unfortunately, for many receptors and especially small molecule ligands a rigid model is not appropriate and structural flexibility must be considered during the docking process.86 In particular, sophisticated molecular docking methods should strive more to fit the inducedfit binding model, which suggests that an enzyme will adopt a different conformation upon ligand binding in order to maximize favorable protei n ligand interactions ( Figure 21 ).87 As the availability of computational resources has increased, so has th e development of more sophisticated docking methods and algorithms based on our understanding of flexible molecular recognition, both for the receptor and the ligand.
44 Figure 21 Schematic diagram of common models for a substr ate binding to the active site of an enzyme. Specifically, a) the "lock and key" model and b) the induced fit model The first docking software, DOCK,85 released in 1982 used rigid spheres to represent both the receptor and ligand atoms, and all internal degrees of freedom were removed for docking. The receptor structure was actually depicted as a collection of spheres in grooves and pockets within the binding site instead of explicitly representing the receptor atoms. To account for ligand structure fl exibility, in 1986 DOCK introduced an algorithm that divided the ligand into fragments and docked those fragments to the receptor binding site.88 When the fragments were finally joined together, flexibility was integrated into the connection between the fragments. While DOCK developers were working on ligand flexibility, others were redesigning the way docking software handles the receptor structure by creating grid files that contain physical and chemical
45 information about the receptor. Grid files increase the efficiency of docking methods by pre calculating information that can be reused for each docking ligand. DOCKER used the grid files to define extended van der Waals radii of the receptor atoms,89 while GRID pre calculated the potential energy of receptor ligand interactions with various functional groups at each point of the grid file.90 These grids were further improved by including estimates to directly calculate the proteinligand interaction energies by summing the van der Waals, Coulombic, and hydrogen bonding contributions.91,92 These grids set the precedent for automated grid fi le usage by many future docking programs. The development of docking algorithms has progressed significantly since the release of the first docking program more than 30 years ago. Currently, the docking procedure generally involves four main steps receptor grid creation, ligand conformation generation, ligand docking, and scoring of docked ligand poses. Although most docking programs use these steps, their details may vary (including combining certain steps) depending on the docking method and software. What follows is a brief description of the options and details of each step in the docking process. 2.2.1 Receptor Grid Generation Many docking software packages now utilize a receptor grid file that contains structural and chemical information about the receptor and potential interactions with ligand atoms. The grid file contains the coordinates and charges of all receptor atoms within the defined binding region. Additionally, proteinligand interaction energies are typically precomputed by considering the possibl e interactions the receptor atoms can make with ligand atoms within the active site and then these values are stored in a table within the grid file. During the docking process, these energies are taken directly from this table instead of being computed onthe fly during each docking run. When
46 performing a virtual screening on millions of compounds, a grid file with precomputed interaction energies saves an enormous amount of computational time. In addition to interaction energies, the grid file also speci fies the binding site dimensions and location with respect to the receptor structure. The binding site location is typically chosen based on prior knowledge of the protein, such as the location of a substrate or inhibitor compound within the active site fr om an X ray crystal structure, but software is available to help predict binding sites of macromolecules if their location is unknown.93 96 Furthermore, the size of the grid can also be adjusted to accommodate the goal of the doc king run. For example, the grid size can be small when attempting to reproduce a crystal structure conformation because the binding mode is known a priori but the grid size can be larger when screening for new inhibitors because the size and binding mode of each ligand may vary substantially within the binding pocket. Many docking programs can also account for receptor flexibility using various methods. In 1991, the first method to include protein flexibility made the receptor atoms soft by reducing the van der Waals repulsion of overlapping atoms,97 which is still being used by docking programs today.98,99 Explicit protein flexibility was implemented a few years later by creating different receptor structures that explore the flexibility of rotatable bonds on protein side chains.100 102 Rotatable bonds are identified and the torsions are systematically sampled to create different receptor structures that are used for ligand docking, although this method is clearly more computationally demanding since docking must occur to multiple receptor conformations instead of a single structure. The rotatable bonds can include all possible rotatable bonds of specified side chains, or simpler methods only consider the rotation of polar hydrogen atoms on
47 receptor side chains. More computationally expensive algorithms involve simultaneous optimization of the ligand and receptor active site atoms during the docking process to include protein flexibility during docking.103 105 The Induced Fit Docking protocol used by the Schrdinger program suite106 involves docking ligands to a receptor structure with a soft van der Waals potential and relaxing the protein iteratively until the protein structure stops changing.98 Molecular dynamics (MD) can be used for a more comprehensive sampling of protein conformations, and representative structures from the simulations can be used for ensemble based docking methods,107 such as the relaxed complex scheme.108 110 Ensemblebased docking methods use receptor conformations from MD simulations as the receptors in ligand docking, and the different conformations represent the global flexibility of the protein with or without a ligand in the binding site in contrast to the local flexibility probed by exploring rotatable bonds of protein side chains. 2.2.2 Generating Ligand Conformations Ligand conformation generators are significantly more utilized than flexible receptor methods, especially when the ligand binding mode is u nknown as in the case of virtual screening for drug design. When the bound conformation of a ligand has not been determined experimentally, docking programs often create a collection of many different ligand conformations using several different methods, although it is important to note that bond distances are typically not varied during this process. The first developed method broke the ligand into fragments and then joined the fragments after docking, creating flexibility at the location where the fragments were joined back together.88 More recently, docking programs began using energy optimization techniques that minimize the docked structure in the active site to maximize favorable proteinligand interactions.104,111,112 A more common approach initially proposed by
48 Judson et al .113 and used by programs such as AutoD ock,114 DA RWIN,115 and GOLD116 among others117 119 utilizes genetic algorithms (GA) to generate ligand conformations. GAs use evolutionary ideas to solve search and optimization problems, such as determining the optimal binding mode of a ligand in an active site. Initially, a population of ligand conformations are generated and docked to the receptor binding site, and then the torsions and coordinates of the lowest energy ligand structures are used to generate new conformations for the next population of structures. The torsions and coordinates are passed down from population to population, although mutations randomly create new torsion or coordinate values to ensure conformational diversity of the ligand as new populations are generated. This process is repeated successively for a determined number of generations to determine the lowest energy binding modes for each ligand. Stochastic ligand conformer generators, such as Schrdingers LigPrep program, generat e different ligand poses by using a random number to vary the internal torsions for the rotatable bonds present. As a result, stochastic docking programs will yield different results with repeated runs regardless of the input ligand structure.120 Deterministic docking programs use a systematic approach to create ligand conformers, which leads to identical results if the same docking calculation is performed repeatedly on the s ame processor. However, even deterministic docking programs have results that can vary significantly depending on the input ligand coordinates, especially for algorithms that use structure minimization methods during conformer generation.121 2.2.3 Search Algorithms Docking programs use many different search algorithms to dock ligands into the binding sites of receptors, of which several have already been introduced as part of the ligand conformer generation, such as genetic, stochastic, and deterministic algorithms.
49 These algorithms all attempt to place the ligand into the proper binding mode within the defined receptor binding region. Stochastic models typically involve using random numbers to place and/or alter the ligand within the active site of the receptor. An example of a stochastic method is the previously mentioned genetic algorithm, which places the ligand within the active site and then makes random changes to the ligand translations, rotations, and torsional angles. Additionally, a few programs such as AutoDock122 perform an energy minimization of the structure followed by a Monte Carlo123 evaluation of the Metropolis criterion124 to determine which ligand conformations ar e used to create the next generation of structures.125 The deterministic methods energy minimization and MD are also available using programs such as CHARMM,126 but these algorithms often suffer from being unable to overcome large energy barriers, which can keep the ligand trapped in a local minimum conformation and not sampling all conformations within the receptor binding site. In order to surmount this dilemma, several MD algorithms have attempted using biasing potentials to lower energy barriers126 128 or sampling at increased temperatures129,130 to enhance ligand sampling and improve docking results. Schrdingers docking program Glide131 uses a unique docking process compared to other available software that combines several of the already described methods. Ligand conformations are generat ed by Glide using an exhaustive search of torsionangle space, which is rare among docking programs. Glide performs a quick initial screening of all generated ligand conformations to determine promising ligand conformations, followed by a minimization of t he ligand within the active site using the standard molecular mechanics OPLS AA force field combined with a distance-
50 dependent dielectric model.132 After minimization, a Monte Carlo procedure is applied to the internal dihedral angles of three to six best ligand poses to sample the local torsional minima and optimize the structure within the receptor binding region. T his procedure is thorough because it screens a large number of ligand conformations without being too computationally demanding, which is ideal for any docking procedure. Incremental construction (i.e. de novo docking or anchor and grow method) is an alter native docking algorithm available for computational drug design that typically places a small molecule anchor or fragment into the binding site, and then attempts to grow that molecule to maximize proteinligand interactions. However, the first anchor a ndgrow methods published actually docked multiple fragments of a single ligand into the docking site and later tried to join those fragments together.88,133 In 1992, DOCK became the first docking software to dock a single ligand anchor fragment into the active site and then systematically extend the anchor by exploring the t orsions of all additions systematically,134 which was later replaced with a minimization procedure.135 Multiple Copy Simultaneous Search (MCSS) 136 and LUDI137 were the pioneer programs that began using incremental construction algorithms as de novo drug design tools. MCSS randomly places 1,000 to 5,000 copies of many functional groups into a receptor binding site followed by minimization to obtain a m ap of the energetically favorable interaction sites of a receptor surface for ligand drug design. On the other hand, LUDI directly designs new enzyme inhibitors specific to the active site starting from an initial anchor fragment and adding new functional groups to interact with the protein.
51 More recently, another de novo software, AutoGrow, was released that uses AutoDock and a genetic algorithm to design new inhibitions.138 A previously docked fragment provides the anchor in the active site, while hydrogen atoms are randomly substituted for other fragments from a database of small molecules (AutoGrow provides its own sma ll and large fragment library). The genetic algorithm combines and mutates the functional groups from the best fitting ligands of the initial population to create a new population of compounds that are docked and rescored used AutoDock. This process is re peated many times to remove undesirable functional groups, and properly orient favorable groups to evolve a novel compound that inhibits the target enzyme. LigBuilder is a de novo program written in C++ that gives the flexibility to grow compounds from an initial anchor compound or link multiple docked compounds within the confines of a receptor binding site.139,140 The POCKET module of LigBuilder initially analyzes the receptor binding site from the provided 3D structure for key interaction sites. Unlike AutoGrow, LigBuilder does not have the ability to dock compounds, even though the initial anchor compounds must be docked into the active site frame of reference pr ior to using LigBuilder. But similar to AutoGrow, the growing and linking strategies of LigBuilder are controlled by a genetic algorithm designed to efficiently design new inhibitors, although the new population of ligands is generated using the elitism algorithm,141 where the top 10% of the original population is directly copied into the new population while the rest of the new population is created by combining the remaining ligands from the old population. The genetic algorithm significantly speeds up the design process compared to a systematic approach that could generate an impossibly enormous number of molecules after just a few growing rounds. Each newly
52 created compound is compared to all other compounds in the same population to ensure compounds are different and promote the analysis of unique molecules each generation. Furthermore, each structure is checked for chemical validity using built in rules based on current drug molecules to guarantee only compounds with reasonable chemical connectivity are generated. Despite their obvious ability to design novel drug compounds, de novo docking programs have several limitations. The number of chemically feasible compounds is enormous, which means it is highly unlikely that these programs will be able to f ind the absolute ideal drug molecule. Incremental growth programs also do not include receptor flexibility during the design process, which as mentioned previously can be a major contributor for ligand binding. Additionally, because the anchor fragment is static in the binding site, most programs do not minimize the final compounds, so it is suggested to optimize the ligand within the active size using an external structure minimization procedure. 2.2.4 Scoring Functions After a ligand pose is generated from the searching algorithm of docking programs, that pose must be accurately scored in order to properly rank the compounds of interest. Regardless of how exhaustive or correct the docking algorithm is for a program, if the program fails to accurately predict the binding affinity of compounds then the results are useless. Meanwhile, the calculation of proteinligand binding affinities must be quick and efficient because docking programs must be flexible enough to screen millions of potential drugs accurately in a reasonable time frame to be useful for drug design and discovery. Unlike more sophisticated binding affinity calculations such as thermodynamic integration142 or free energy perturbation,143 molecular docking
53 programs use a simplified equation (or set of equations) called a scoring function to estimate a binding score or binding affinity. Each docking program uses a different scoring function determined to be a match for the docking algorithm. In general, scoring functions can be divided into three main categories force field based, empirical, and knowledgebased.144 Force field based scoring functions are the most computationally expensive scoring functions because of the complicated nature of the equations, but nevertheless the functions are built on first principles and corr espond to the nonbonded interactions (i.e. van der Waals and electrostatic) as they are described by classical MD simulations. The van der Waals contributions are described by a LennardJones potential function145 designed to estimate the attractive and repulsive nonpolar interactions between the receptor and ligand. The electrostatic interactions are estimated using a Coulombic interaction term based on the charge and distance between two atoms. Because of the computational ti me required to calculate these interactions, a distance cut off is usually applied to reduce the number of interactions for each atom, although this clearly reduces the accuracy of the scoring function. The functional forms of additional energy terms hydrogen bonding, solvation energies, and entropic contributions have also been considered and implemented by several programs using forcefield based scoring functions, such as GOLD,146 DOCK,135 and AutoDock.114 Empirical scoring functions are relatively simple, quick to evaluate, and based on a fitting of data from receptor ligand complexes with know n binding affinities. The equations do not reflect classical nonbonded interaction functionals, but can typically still be separated into distinct energy terms, such as hydrogen bonding, solvation free
54 energies, entropic contributions, ionic interactions, and nonpolar interactions. The functions are easy to solve, but the accuracy is based entirely on the chosen test set of complexes used to determine the coefficients in the equations. A test set can have a number of problems, such as the number and variet y of complexes analyzed, leading to limitations of scoring function accuracy when the program is confronted with a complex not in the test set. Empirical scoring functions can vary significantly by the software package since the functions depend significan tly on the test set chosen, but examples of empirical scoring functions can be found with LUDI,137 FlexX,133 and ChemScore.147 Knowledgebased scoring functions are derived from the frequency of certain atom atom interactions found in known proteinligand complexes. Experimental proteinligand s tructures are analyzed for the distance and frequency of each interaction; the higher the occurrence of a particular interaction, the more favorable that interaction is considered. These atom atom contact populations are translated into a scoring function describing the interaction between two atom types where favorable interactions are favored over repulsive interactions. Similar to the empirical approach, knowledgebased scoring functions are simple and quick to compute. Furthermore, they are similarly limited by the chosen test set of proteinligand structures, but knowledgebased approaches have the extra ability to specifically account for unique atom atom stacking contacts and interactions with sulfur, phosphorous, halogens, or metals) that might not be described using an empirical method. Several knowledgebased scoring functions are available, including the PMF score,148 DrugScore,149 and SMoG150 scoring functions.
55 Schrdingers Glide docking program131 utilizes an empirical approach to describe its two most rigorous scoring functions StandardPrecision (SP)131 and ExtraPrecision (XP)151that are based on the ChemScore function,147 as shown in Equation 2 1. The SP function is suited for discovering ligands that bind well within a time frame suitable for scree ning large numbers of compounds. Consequently, the Glide SP scoring function is softer and allows some imperfections in the proteinligand docked pose. The XP scoring function is stricter and applies a significant penalty for any docked pose that violates any known physical chemistry properties (protein/ligand strain, loss of entropy, and desolvation effects), which makes Glide XP ideal for obtaining more accurate docked poses and binding affinities. The increased accuracy of the XP scoring function comes at the cost of computational time, limiting the applicability of Glide XP to smaller numbers of compounds that can be reasonably analyzed. GbindC0Clipof(rlr)ChbondgrhCmetalfrlmCrotbHrotb (2 1 ) In Equation 21, the constants C are derived from the experiment al data obtained from the proteinligand parameterization set. The first two summation terms extend over all atom atom pairs for hydrophobic (i.e. lipophilic) and hydrogen bonding interactions between the ligand and receptor, while the final summation term s extends over all pairwise interaction between receptor metal atoms and the ligand atoms. The functions f g and h are distance ( rlr for ligandreceptor distances and rlm for ligandmetal distances) and/or angle ( ) dependent functions that yield a maxim um value of 1.00 when the interaction between two appropriate atoms is within an ideal range. The functions are linear between 1.00 and 0.00 for distances and angles just outside the ideal range, and 0.00 beyond the cutoff for a favorable interaction (see Figure 22 for
56 the example of a hydrogen bond distance). The final term in Equation 21, CrotbHrotb, applies an entropic flexibility penalty for frozen ligand bonds, defined as a rotatable bond where the atoms on either side of th e bond are in contact with the receptor. Figure 22 Depiction of how Glide scores a hydrogen bond based on the distance between the heavy atom (X) and hydrogen atom (H). A maximum score of 1.00 is given for distances between 1.60 and 2.10 with a linear decrease from 1.00 to 0.00 for distances from 2.10 to 2.25 and the score is zero beyond this region. Glide expands the ChemScore function by dividing the summations into more specific categories, as shown in Equation 22. GbindClipolippfrlrChbondneutneutgrhChbondneutchargedgrhChbondchargedchargedgrhCmaxmetalionfrlmCrotbHrotbCpolarphobVpolarphobCcoulEcoulCvdWEvdWsolvation (2 2 ) The form of the pairwise lipophilic term remains unchanged from the initial ChemScore function. The hydrogen bonding term from ChemScore has been replaced
57 by three hydrogen bonding terms that describ e typespecific hydrogen bonding interactions, allowing the three functions to be weighted differently, providing more flexibility than treating all hydrogen bonds equally. Fits to experiment recognized the hydrogen bond between two neutral functional groups as the most favorable, while the interaction between two charged species was the weakest. The fifth term in Equation 22 describes the interaction of a charged ligand functional group with a receptor metal ion in order to model the extreme favorability of metal anionic coordination sites compared to metal neutral interactions, which differs from the original ChemScore function that considers all metal ligand interactions equal. The entropic cost of restricting ligand conformational motion upon binding is described by the sixth term in the scoring function. The seventh term represents the favorable interaction energy of a polar group without hydrogen bonding capability in a hydrophobic region. The Coulomb and van der Waals interactions (the eighth and nint h terms, respectively) required adjustments of atomic descriptors to improve the estimated binding affinities; reduction of the net ionic charge and van der Waals radii of formally charged groups such as carboxylates or guanidinium groups significantly improved the Glide SP binding energies compared to experiment by allowing the resulting hydrogen bonds to be in their ideal conformations. Lastly, solvation terms describing solvated charged and polar groups on the ligand and receptor are typically computationally expensive to calculate and are often neglected in docking scoring functions, but Glide estimates the solvation effects of the most energetically promising poses by explicitly docking water molecules into the binding site. The presence of explicit wat er molecules in the active site has a distinct advantage over using a continuum solvation model because the compact and
58 limited space in the active site dictates that the specific conformation of a water molecule is meaningful. Overall, this Glide SP funct ion showed improved enrichment over previous scoring functions for docking a diverse test set of proteinligand complexes.152 The Glide XP scoring function151 is intended to be a significant improvement of accuracy over Glide SP, with the expected consequence of increasing the necessary computational time. The general form of the Glide XP scoring function is shown in Equation 23, while the details of represented functions are similar to those for Glide SP. XP GlideScoreEcoulEvdWEbindEpenalty (2 3 ) The interactions that are favorable for ligand binding are represented by Ebind, which is defined in Equation 24. EbindEhyd_enclosureEhb_nm_motifEhb_cc_motifEPIEhb_pairEphobic_pair (2 4 ) The first term, Ehyd_enclosure, estimates the favorable interaction energy of a ligand lipophilic group surrounded (i.e. enclosed) by receptor hydrophobic regions arising from the displacement of water molecules that were in a lipophilic environment in the active site, as well as a loss of the unfavorable relationship between bulk water molecules and the lipophilic group of the ligand upon binding. Theoretical and experimental analyses of pharmaceutical drugs and their targets illustrate the increased significance of hydrogen bonding between neutral groups of the receptor and lig and for drug potency, which is estimated in the Ehb_nm_motif term of Equation 24. Another hydrogen bonding term, Ehb_cc_motif, estimates the energetic contribution to binding of special chargedcharged hydrogen bond motifs. EPI estimates the favorable cation
59 interactions attributed to ligand binding. The final two terms that contribute to ligand binding, Ehb_pair and Ephobic_pair, represent the placement of ligand halogen atoms in a hydrophobic region of the receptor and a correction term to improve the binding affinity of small molecules that cannot make as many proteinligand interactions as larger ligands, respectively. The Epenalty term in Equation 23 describes the unfavorable energetic contributions of protein ligand binding, and is defined in Equation 25. EpenaltyEdesolvEligand_strain (2 5 ) As was the case for the Glide SP scoring function, the desolvation energy term, Edesolv, is calculated by docking explicit water molecules into the binding pocket instead of using conventional continuum solvation models. The efficiency of the calculation is improved by precalculating the energies during the receptor grid generation. However, the Glide XP scoring function is more heavily weighted and rigorous than SP using increased sampling techniques for docking the water molecules. Finally, the Eligand_strain term does not contribute significantly to the overall binding energy, but accounts for the internal strain of a ligand structure and appropriately penalizes highenergy docked conformations of the ligand with close internal contacts. Overall, docking programs aim to provide adequate assessment of the propensity for a ligand to bind to a particular receptor conformation, which is useful for drug design and discovery efforts but there are many limitations of these programs that should be considered. The algorithms and scoring functions are intended to be quick and relatively accurate allowing the screening of millions of compounds within a reasonable timeframe, which means the docking results should not be considered blindly. Docked
60 poses and energies should be considered the initial step in the drug design process, whereby more sophisticated theoretical and experimental methods should follow. Among the clear limitations of docking methods, the resulting poses are static structures that do not describe the dynamic nature of proteinligand interactions. Other theoretical approaches such as molecular dynamics (as described later in Section 2.3) along with a more sophisticat ed binding free energy calculation should be considered after obtaining a docked proteinligand conformation in order to more accurately represent the complex. Finally, a recent review showed little consistency among the results of several docking algorithms, and more importantly observed virtually no correlation between the docking scores and in vitro binding affinities.153 This conclusion highlights the danger of utilizing just molecular docking during the drug design process. 2.3 Pharmacophore Models Monty Kier originally proposed the idea of pharmacophore models in the late 1960s as a novel drug design tool.154,155 Kiers idea was to create a 3D arrangement of chemical functional groups that are necessary for a molecule to initiate or hinder a receptors biological activity. For drug design, these pharmacophore features represent the location and type of functional group that a drug molecule should possess to inhibit a particular target protein ( Figure 23 ). Pharmacophore models are traditionally generated using the chemical features and structures of compounds with known experimental inhibitory activity. The six main chemical features in pharmac ophore models hydrogen bond donor, hydrogen bond acceptor, negative charged group, positive charged group, aromatic ring, and hydrophobic group are used to represent the common functional groups of known active molecules. Furthermore, many software
61 packages are available to create pharmacophore hypotheses, including HypoGen,156 HipHop,157 Pocket v.2,158 and Phase.159,160 Figure 23 An example of a phar macophore model. Here the red sphere represents an important negative ionizable group, while the orange donut represents the location of a key aromatic group. Pharmacophore models can be created using either a ligandbased approach or structure based appro ach.161 When struct ural data is not available depicting the binding modes of the known active molecules (i.e. inhibitors) in complex with the target receptor, then a ligand based approach must be applied. Since the conformation of the bound ligand is unknown, many conformers of each active molecule are initially generated followed by an alignment of all conformers for the active molecules. Once aligned, the features of the pharmacophore model are extracted from the repeated occurrence of similar functional groups at the same spatial location. This approach has two main limitations that reduce its efficiency drastically. First, the results of any ligandbased method is linked directly to breadth of ligand conformations generated. Creating too few ligand conformations likely prevents the pharmacophore from using the proper ligand structure, while too many conformations will make the alignment process
62 extremely timeconsuming and unlikely to converge. Second, the alignment process of the ligand poses is not well established, allow ing for different pharmacophore models generated depending on the aligning algorithm. The ligand conformations can be aligned by maximizing atomic, fragment, or functional group overlap between different structures, and each will likely result in a different pharmacophore outcome. Nevertheless, ligandbased approaches can produce useful pharmacophore models for drug design. The structurebased pharmacophore approach circumvents both limitations of the ligandbased approach by using the conformations of liga nds bound to the target receptor as resolved from experiment (e.g. X ray crystallography, nuclear magnetic resonance, etc.) or theory (e.g. molecular docking, molecular dynamics, etc). Furthermore, because the experimental ligand conformations are bound to the receptor, the conformations are already located in the same frame of reference (the receptor active site) and do not need to be aligned against one another. Instead, the ligands can be directly overlaid in their bound conformations, and statistical analysis is performed to extract the pharmacophore features for the model. The obvious disadvantage to this method is it requires available 3D proteinligand structures and cannot be performed otherwise. The ligandbased and structurebased approaches as described both create pharmacophore models based on structural information of known inhibitors that hinder biological activity, which prevents pharmacophore models from being created for target molecules that have no strong inhibitors. Recently, an algorithm was implemented in Schrdingers Phase that is able to generate pharmacophore models using fragments
63 (with no known binding affinity for the receptor) docked to a target receptor, as summarized in Figure 24 .162 Initially, fragment molecules are docked into the active site of the molecule using Glide XP151 with an enhanced sampling procedure intended to allow the fragment ample probability to find the lowest energy conformation in the binding site. After docking, the docked fragments (up to 2,000 total fragments) are clustered into distinct groups to ensure the pharmacophore features represent a variety of locations within the receptor active site. Phase extracts the pairwise decomposable receptor ligand interaction energies from the Glide XP output and a representative fragment structure of each cluster to generate features for energy optimized pharmacophore models (epharmacophores), and then Phase ranks those features based on their perceived importance from the XP energetic terms.163 These epharmacophores provide chemical features important for drug design for biological targets that currently have no known strong inhibitors. Figur e 24 Workflow summary of epharmacophore generation from docked fragments using Schrdinger. Besides containing important chemical features for biological activity, pharmacophore models are used for virtual screening of large molecular databases in the pursuit of novel inhibitors. The models provide a template of chemical features spatially arranged within the active site that is ideal for screening. Pharmacophore screening algorithms attempt to align the appropriate functional groups of potential
64 inhibitors in molecular databases to the chemical features of the pharmacophore. Hit compounds are molecules that match the required pharmacophore features within a specified tolerance distance of the feature location of the pharmacophore. Furthermore, pharmacophore models, such as those generated by Phase, use excluded volumes, spheres corresponding to the space occupied by receptor atoms surrounding the ligand, to prevent potential inhibitors from occupying the same space as the receptor atoms. Large database screening of pharmacophore models has resulted in the discovery of many novel enzyme inhibitors.164 166 2.4 Molecular Dynamics Unlike docking calculations, molecular dy namics (MD) simulations provide atomistic details about the motions of a system over time. First introduced in the 1950s,167,168 MD propagates Sir Isaac Newtons laws of motion describing the forces acting on atoms (Equation 26) to create successive configurations of a system depic ting the movement of those atoms. aFm (2 6 ) In particular, the differential equations corresponding to Newtons 2nd law of motion, as shown in Equation 27 using Cartesian coordinates, describe this atomic motion exactly, but they can only be solved analytically for simple systems. d2xidt2 Fximi d2yidt2 Fyimi d2zidt2 Fzimi (2 7 )
65 Unfortunately, assumptions and techniques must be applied to estimate the motions of large systems where the coordinat es of all atoms are relevant to the system dynamics. In numerical analysis, the solutions to differential equations are often approximated using finitedifference methods that divide the calculation into discrete infinitesimal time steps ( ) and assume the forces remain constant between time steps. The Verlet algorithm169 is a common finitedifference method implemented by MD engines that uses a Taylor expansion to calculate the atomic coordinates and velocities as a function of time, as summarized in Equation 28. The Verlet algorithm can be applied using the leapfrog integration method170 that calculates the current coordinates at time t using the velocity information from the following half step ( t + 0.5 ). Meanwhile, the velocity information at t + 0.5 is updated using the forces present at t + in the leapfrog scheme. Consequently, the coordinates and veloc ities are always a half step apart during a traditional MD simulation. rttrttvt12 t2at...rttrttvt12 t2at...vt12 trttrtt (2 8 ) The time step size used during MD simulations must be chosen carefully to balance computational performance and the potential of ca using instabilities in the simulation. A small time step requires more computational time to complete a simulation, while a large time step can break energy conservation rules and destabilize the system by allowing atoms to travel a relatively long distanc e between force updates. As a compromise for traditional MD simulations, the time step size is suggested to be onetenth of the fastest motion of the molecules involved, which for most biological
66 systems is defined as the C H stretching motion that occurs over 10 fs.171 Thus, many classical simulations utilize a 1 fs time step, unless the bond distances between all heavy atoms and hydrogen are fixed at their equilibrium distance using the SHAKE algorithm,172 which allows a 2 fs time step. To be consistent with most experiments, MD simulations are often performed using constant (i.e. average) temperature conditions. Thermostat algorithms are modifications of an MD engine that regulates the internal temperature of the system according to the equipartition theorem that relates the average kinetic energy, K of a system with its temperature ( T ), as shown in Equation 29, where kB is Boltzmanns constant and Ndof is the internal number of degrees of freedom for the system. K 32 kBNdofT (2 9 ) A common practice to control the internal temperature of a simulation is to scale the atomic velocities up or down accordingly at each step, adjusting the total kinetic energy of the system to reach the desired target temperature. Furthermore, stochastic thermostat algorithms, such as the Andersen thermostat,173 additionally reassign the velocities of atoms by occasionally choosing a new velocity randomly using a Maxwell Boltzmann probability distribution, which is intended to mimic the propensity of a system to have irregular atomic collisions. A system obtained from experiment (e.g. X ray crystallography or NM R) or theory (e.g. molecular docking or pharmacophore screening) does not contain initial velocities for the atoms, so these values must be assigned. A common approach is to assign these velocities based on a random value chosen from a Gaussian or Maxwell Boltzmann distribution of attainable velocities for a given temperature. The distribution is
67 designed to give reasonable velocities for the desired simulation temperature, although the random number must also be chosen wisely to prevent synchronization of trajectories that can lead to corruption of simulations using stochastic thermostats.174 The velocities themselves are typically altered to force the overall net linear and angular momentum to zero for the system prior to any dynamics calculations. Experiments with biological complexes are typically performed in solution, thus MD simulations must account for interactions with the solvent using a solvation model. Two general types of solvation models implicit and explicit can be chosen to describe the solvent. Im plicit solvation models use a continuum dielectric medium to represent the solvent, while explicit models contain discrete solvent molecules surrounding the solute. Implicit models such as Generalized Born (GB),175 Poisson Boltzman,175 and Reference Interaction Site Model (RISM)176 are generally computationally efficient, but typically are unable to capture proper timescale motions and are more prone to system instabilities than explicit models. Explicit solvent models such as TIP3P water177 and TIP4P water178 generally result in more realistic solute conformations, but are significantly more computationally expensive than their implicit model counterparts. When explicit water model s are used to represent the solvent, periodic boundary conditions are often used to treat the interactions of the unit cell with its surroundings. For periodic boundaries, identical unit cells effectively surround the primary unit cell to mimic the presenc e of the solute in bulk solvent and remove interactions of the solvent or solute atoms with a solid surface (i.e. the edge of the unit cell). Although the atoms can move from one unit cell to another freely, the only coordinates that are recorded are
68 for a toms in the primary unit cell. Since all the unit cells are identical, as an atom from the primary cell enters a neighboring imaginary cell, the corresponding atom in the opposite imaginary unit cell will simultaneously enter the primary unit cell, ens uring that the number of atoms in each cell remains exactly the same. The Particle Mesh Ewald method uses an Ewald summation that converges quickly to efficiently calculate the long range electrostatic interaction energies and forces between atoms in neighboring cells.179 Classical MD uses molecular mechanics to calculate the energy of the system using only the atomic coordinates (excluding any electron information) generated by integrating Newtons laws of motion. Molecular mechanics uses force fields empirical mathematical functions that describe atomic interactions to calculate the bonded and nonbonded energy terms for the system. Force fields primarily treat interactions like electr ostatics or changes to bond distances, angles, and dihedrals, but are unable to treat processes that involve bond formation or bond breaking. Several different force fields have been developed that can produce reasonable dynamics and conformational energet ics, such as the AMBER ff99SB,180 parmbsc0,181 and gaff182 force fields, CHARMM27,183 GROMOS,184 and OPLS.132 The MD performed throughout this study utilizes the AMBER force field, which is shown in its mathematical functional form in Equation 210. The first two terms on the right hand side of Equation 210 determine the energetic penalties associated with bond distance and angle deviations from their equilibrium values. The third term represents the energy associated with the dihedral angles. The last two terms represent the nonbonded van der Waals and electrostatic interactions, respectively, between atoms i and
69 j Specifically, the fourth term is a 612 LennardJones potential function145 commonly used for computational reasons to describe the van der Waals interactions, while the final term uses Coulombs law to calculate the interaction energy between two charged particles. U(R)Krrreq2bondsKeq2anglesVn2 1cosnndihedralsAijRij12 BijRij6 qiqjRij ijatomsijatoms (2 10) In addition to the functional form of the force field, the val ues of the constants (e.g. equilibrium bond distances and angles, spring constants, etc.) are also dependent on the chosen force field. These parameters are empirically derived to reproduce either experiment or highlevel quantum mechanics calculations. Al though every molecule has unique properties, the force field constants are chosen to be transferable to a variety of interactions in many diverse compounds. For example, enzymes are all structurally and sequentially different, but their amino acid building blocks are the same so many of the interactions can be described using similar parameters. Similarly, many force fields, including the AMBER force fields, define atom types that help group atoms into categories based on their atom number, expected hybridi zation, and connectivity to other atom types. Atom types allow force fields to be transferrable from one system to another by assuming that atoms in different systems with similar connectivity will behave the same. Many force fields define specific atom ty pes for use with families of molecules, such as the AMBER ff99sb force field for proteins,180 the AMBER parmbsc0 force field for nucleic acid biomolecules,181 or the general AMBER force field (gaff) for
70 simple organic molecules.185 The atom types are often stored in a parameter or topology file that is referenced during simulations for force and energy calculations. 2.5 Binding Free Energy Calculations The ensemble of conformat ions generated from MD simulations provides the necessary information to estimate the binding free energies of ligands bound to receptor molecules. These endstate free energy calculations are independent of the binding pathway, but have shown the capabili ty to accurately predict relative binding free energies for drug discovery.186 188 The use of molecular docking using Glide combined with MD and binding free energy calculations to discover new potential inhibitors is depicted in Figure 25 The theory of free energy calcul ations will be discussed in more detail in Chapter 5 alongside the discussion of a recently developed program MMPBSA.py189 for performi ng these calculations. Figure 25 The workflow used in this study using a combination of molecular docking, MD, and binding free energy calculations to discovery new potential inhibitors of TcTS.
71 CHAPTER 3 DESIGN OF e PHARMACOPHORE M ODELS USING COMPOUND FRAGMENTS FOR the TRANS SIALIDASE of TRYPANOSOMA cruzi: SCREENING FOR NOVEL INHIBITOR SCAFFOLDS 3.1 Preliminaries Molecular docking has become a highly useful tool for drug discovery.190 In particular, docking molecular fragments has aided in the discovery of new lead compounds for drug design.191 Due to their relatively small size, fragments help identify new regions of an enzymes active site that were previously untargeted.192 Although most docking scoring functions are not robust, a recent study showed that Schr dingers Glide131 ca n be a useful tool for docking small molecular fragments.193 Structure based pharmacophore models have also become increasingly important in computational drug design.194 Pharmacophore models are used to identify the key features vital to inhibition activity for drug molecules. Traditionally, pharmacophore models are designed using a training set o f active molecules. Thus, the question naturally arises what to do prior to any actives being identified for a given enzyme. Recently, Loving et al. developed a method that uses docked fragments to create structurebased energy optimized pharmacophore (ep harmacophore) hypotheses.162 This eliminates the need for known active molecules when generating pharmacophore models and helps identify new regions of the active site to target for drug design. Once developed, these epharmacophore models may also be used to screen for new lead drug molecules using the key features of the pharmacophore, which is a fast technique typically used for screening large databases of compounds.195 Unlike docking that usually provides a single conformation, molecular dynamics (MD) provides insights into the dynamical behavior of proteinligand complexes. MD is
72 also used to refine biological structures using classical laws of motion to describe the movement of atoms in time. It naturally arises that molecular dynamics can be used to further refine a proteinligand structure from molecular docking calculations. Additionally, the MD simulations can be used to calculate the relative free en ergies of binding for a ligand bound noncovalently to a receptor, using such methods as Molecular Mechanics Poisson Boltzmann Surface Area (MM PBSA) or Molecular Mechanics Generalized Born Surface Area (MM GBSA).196 Finally, it has been shown that using these free energy calculations from MD simulations on docked proteinligand poses can aid the virtual screening process of drug discovery.197 We creat ed the first e pharmacophore models of T. cruzi trans sialidase using fragments docked to the active site. Pharmacophore models were generated for the holo crystal structure (PDB code 1S0I), as well as holo and apo conformations of TcTS from previous MD si mulations.59 Using the epharmacophore models, we characterized the key chem ical features important for strong inhibition of the enzyme for each conformation and used those features to screen the Clean Leads database from ZINC,198 followed by MD and relative binding free energies calculations on all hit compounds. The best compounds from binding free energy calculations are presented in detail and proposed as novel lead drug candidates of TcTS. 3.2 Methods 3.2.1 Receptor Structures and Grid Generation Pharmacophore models were created using three different structures of TcTS one X ray crystal structure and two structures from MD. These structures were chosen because they represent three unique and available conformations of TcTS for drug design. The crystal structure represents the most common conformation for analysis
73 because it corresponds to the only experimental structure of TcTS with the substrate bound, which is often considered the best conformation for inhibitor design. The structures from MD simulations represent relaxed conformations of the protein with the potential to rev eal novel inhibitors that a pharmacophore model from the crystal structure is unable to discover. The X ray crystal structure of TcTS with the substrate (sialyllactse) bound was obtained from the Protein Data Bank (PDB code 1S0I53) for e pharmacophore model generation (called the 1S0I structure from now on), but the unliganded crystal structure (PDB code 1MS3) was not used because of its inability to capture the flexibility of the catalytic cleft.59 Instead, a structure of TcTS in the apo conformation (d efined as the structure of an enzyme without ligand present in the active site) was obtained from a MD simulation previously published by Demir et al .59 Because the simulation produced thousands of conformations of the protein, clustering was performed with GROMACS199 to obtain a more manageable number of statistically different conformations. The snapshots from the apo simulation were clustered with a root mean square deviation (RMSD) cutoff of 0.125 for all heavy atoms in the active site (defined as any residue within 15 of the nucleophilic Tyr342 residue). The representative conformation of the most populated clus ter (62% of the total structures) was chosen as the apo structure of TcTS for pharmacophore model creation, and will be referred to as the apo structure henceforth. For comparison purposes, clustering was also performed on a MD trajectory of TcTS bound t o the substrate with a RMSD cutoff of 0.085 to maintain approximately the same number of structures in the most populated cluster between the two simulations. The most populated cluster corresponded to 56% of the total structures from the simulation, and the representative
74 structure from this cluster was chosen for pharmacophore hypothesis generation, and will be referred to as the holo structure (defined as the conformation of an enzyme when the substrate is present in the active site). All three TcTS structures were prepared using the Protein Preparation Wizard (PrepWizard) in Schrdinger, which is shown to improve virtual screening enrichment by fixing common problems with initial protein structures.200 The PrepWizard added any necessary hydrogen atoms and removed all water molecules in preparation for the receptor grid generation. The substrate location in the 1S0I structur e was used to choose the center and size of the receptor grid, which was generated using Schrdingers Glide with default settings for all parameters. The grid size was chosen sufficiently large to include all active site residues involved in substrate bin ding, including residues at the mouth of the active site used to position the acceptor/donor lactose molecule during catalysis. The grids for the apo and holo structures were chosen to have the same size and corresponding centroid point of the active site for consistency. 3.2.2 Docking Fragments to TcTS Structures The Clean Fragments library of 504,074 compounds was downloaded in structure data file (SDF) format from the ZINC198 website. LigPrep,201 which prepared ligands for docking with Glide, was used to generate reasonable conformers of each fragment using the default settings. All ligand conformers were docked to each of the three receptor grid files (1S0I, apo, and holo) using Schrdingers Glide131 in High Throughput Virtual Screening (HTVS) mode with default settings to filter out c ompletely unreasonable ligands from being considered. The highest ranking 120,000 compounds from virtual screening were redocked using Glide SP with default settings, followed by
75 re docking of the top 6,513 fragments from Glide SP using Glide XP151. The Glide XP settings were chosen based on the protocol of Loving et al .162 determined to be ideal for generating epharmacophores. 3.2.3 E pharmacophore Generation The results from fragment docking with Glide XP were used to generate epharmacophore hypotheses for each Tc TS conformation using the E pharmacophore script163 available from the Schrdinger Script Center. The E pharmacophore script works by mapping the Glide XP energies for the top 2,000 docked fragment poses onto the corresponding ligand atoms aligned to the receptor conformations, followed by volume clustering of the fragments into 15 distinct clusters. Phase159 was used to generate the pharmacophore hypotheses from the docked poses of the clusters using the default set of six chemical features in Phase: hydrogen bond acceptor, hydrogen bond donor, negative ionizable, positive ionizable, aromatic ring, and hydrophobic. The Glide XP energies for each pharmacophore site in the hypothesis are summed and ranked based on their total energetic contributions. A van der Waals scaling of 0.5 was used for receptor based excluded volumes in each hypothesis to account for the shape of the active site. For each hypothesis, the 15 most favorable pharmacophore sites were kept, which is more than typical hypotheses. For TcTS, we were attempting to disco ver new regions of the active site that could lead to novel inhibitors, so analyzing more sites increases our likelihood of doing so. 3.2.4 Pharmacophore Screening We searched the Clean Leads library of 4,230,832 compounds in order to find novel inhibitor scaffolds of TcTS. This library represents much of chemical space, which is ideal for this type of screening. Molecules were required to match a minimum of 3 or 4
76 sites on each hypothesis to be considered a hit compound, but preference was given to any molecul es that matched more than the required sites. The chemical features of the required pharmacophore sites were used to filter the Leads library with Schrdingers Maestro v9.3 interface for only compounds that contained those features. Filtering the initial database saves time, but should have no effect on the screening results since the excluded compounds would never be chosen by Phase as hit compounds anyway. During the screening process, conformers were generated for each ligand using a rapid conformational sampling procedure. Ten conformers were created for each rotatable bond of each ligand, with a maximum of 100 conformers generated per structure (both defaults), which was a reasonable value given that the lead compounds are all relatively small and typi cally contained less than 10 rotatable bonds. Excluded volumes, required matches, and an intersite distance matching tolerance of 2.0 for all sites were applied during the screening process. Phase attempts to maximize overlap between the ligand conformat ions and the locations of the pharmacophore sites, which means the protein structure is not explicitly included during the virtual screening process. Only one structure was returned for each compound that matched the hypothesis. Finally, default settings w ere used for all coefficients when calculating the Phase fitness score and ranking the hit compounds. 3.2.5 Molecular Dynamics and Free Energy Calculations To refine the ligand structures from Phase, the GPU accelerated pmemd code202 of Amber 12203 was used to perform MD on each hit compound in complex with the catalytic domain of TcTS. The C terminal lectin like domain and the helix linker between the C and N terminal domains were excluded during MD simulations because the crystal structures implied these domains were unlikely to be directly involved with
77 the catalysis, and any allosteric binding contributions would not apply in the binding event of small molecule inhibitors. Because the e pharmacophore screening process does not include the protein structure, several ligands had atoms that overlapped with protein atoms, so the atom positions were manually moved using VMD204 to remove any overlap prior to MD. The antechamber module of Amber was used to calculate the AM1BCC charges for all ligands. The gaff182 and ff99SB180 force fields were used to define all remaining parameters for the hit compounds and TcTS, respectively. All complexes were neutralized with counter ions and solvated with TIP3P water177 in a truncated octahedron with a minimum of 12.0 between the solute and the edge of the unit cell. All initial structures underwent a sevenstep minimization procedure using positional restrain t s on all solute heavy atoms, and slowly reducing the restraint weight from 10.0 to 0.0 kcal/mol/2. After minimization, the protein was restrained with a positional restraint of 10.0 kcal/mol/2 while the system was linearly heated from 10 K to 300 K over 2.0 ns. A sevenstep equilibration period over 3.5 ns was used to slowly remove the positional restraints on the protein from 10.0 to 0.0 kcal/mol/2. Finally, 10.0 ns of unrestrained MD was performed on all solvated proteinligand complexes with coordinates s aved every 1.0 ps, which was used for all analyses. The SHAKE algorithm172 was applied to fix all covalent bond distanc es involving hydrogen, allowing a time step of 2 fs for dynamics. All simulations were performed using periodic boundary conditions under constant pressure and temperature using an Andersen thermostat.173 Hydrogen bonding analysis was performed using the cpptraj module of AmberTools using default hydrogen bond definitions. Single trajectory free energy of binding calculations were performed on 200 evenly spaced snapshots from the
78 unrestrained MD simulation with the MMPBSA.py program189 released alongside AmberTools using the Gener alized Born (GB) model205 to calculate solvation free energies. GB is a good approximation of solvation energies compared to more rigorous free energy perturbation calculations when computing relative free energies of binding.206 We assume the solute entropy for all systems remains the same, so all entropic contributions to binding were neglected during free energy calculations.196 It is important to note that the calculated free energies are not expected to be true binding free energies, and are used only to compare relative to one another. 3.3 Results and Discussion In this section, we will describe the details of all three pharmacophore models, the rankings of the hit compounds discovered, and details of the two most promising inhibitors of TcTS obtained from pharmacophore screening. 3.3.1 Pha rmacophore Hypotheses 18.104.22.168 1S0I pharmacophore model This pharmacophore model was generated using the TcTS conformation from the 1S0I PDB X ray crystal structure. Table 3 1 provides a breakdown of all 15 pharmacophore sites generated by Phase, including the corresponding interaction with TcTS each site represents. Several of the pharmacophore sites found by Phase were expected. For example, it is well known that the arginine triad (Arg35, Arg245, and Arg314) is important for stabilizing the substrate carboxylic acid during ligand binding for both sialidases and TcTS,46 so it was not surprising that feature N10 was the most favorable site. Furthermore, the acceptor binding site between residues Tyr119 and Trp312 at the mouth of the binding site was previously shown to be important for substrate positioning in the active site,59 explaining the presence of feature R20 in Table
79 3 1 The occurrence of these two features (N10 and R20) is a testament to the validity of the method at selecting reasonable pharmacophore sites. Clearly, no single drug molecule would possibly form all 15 interactions described by the pharmacophore model. Thus, when screening for new inhibitors, we decided to require a compound match four of these features to be considered a hit compound because we found using more than four features resulted in no hit compounds. Table 31 Pharmacophore sites of the 1S0I model. Sites marked with an asterisk (*) were required matches during screening. Feature labels were given by Phase, with the letter indicating the type of interaction (N = negative ionizable, A = hydrogen acceptor, D = hydrogen donor, H = hydrophobic, and R = aromatic r ing). The scores are Glide XP summed energy values in units of kcal/mol. Rank Feature Label Score Interaction with TcTS 1 N10* 13.39 Arginine triad (Arg35, Arg245, Arg314) 2 N109 4.19 Arg35 and Arg53 3 A18 3.14 Asp59 b ackbone NH and side chain OH o f Tyr119 4 H84 2.52 Hydrophobic pocket near Val95 and Leu176 5 D69 2.20 Side chain carbonyl oxygen of Asn60 6 A3* 1.94 Arg93 7 N17* 1.67 Arg53 and Arg35 8 A22 1.60 Ser229 9 D43 1.60 Side chain oxygen of Tyr342 10 D60 1.60 Side chain carbonyl oxygen of Gln195 11 D82 1.37 Gln362 12 R20* 1.25 Acceptor binding site between Tyr119 and Trp312 13 D45 0.80 Asp96 14 D56 0.72 Asp247 15 A16 0.70 Arg314 and Arg35
80 Phase provides the ability to not specify the four mandated features, allowing any four features to be matched. However, this is very computationally demanding using a pharmacophore model with 15 features and seems unnecessary since we have knowledge of a few important features a priori We decided to select the four required features based on our understanding of the protein structure and catalysis. Initially, we chose both N10 and R20 because of their previously mentioned importance for substrate binding. Our third choice was N17 (negative ionizable and hydrogen acceptors were both considered matches) because Pierdominici Sottile et al recently found Arg53 to be the most stabilizing noncatalytic residue of the transition state complex for TcTS.56 Figure 31 The required matches for the 1S0I pharmacophore used for screening lead compounds. Feature N10 interacts with the arg inine triad (Arg35, Arg314, and Arg245), while N17 and A3 represent favorable interactions with the side chains of Arg53 and Arg93, respectively. The aromatic feature R20 is located in the acceptor binding site near Tyr119.
81 We wanted the final required feature to be a new site that has not been previously targeted by inhibitor design on TcTS. For this feature, we wanted to avoid parts of the active site that were solvent exposed and near the edge of the active site to avoid nonspecific interactions and com petition with the solvent. Feature A3 was chosen because it was in a back pocket of the active site and contained a key target residue Arg93. This pocket is mostly hydrophobic except for the Arg93 residue, and is known to bind the N acetyl group of the substrate.46 From our understanding, Arg93 has never been targeted for drug design and thus has the ability to aid in the discovery of novel inhibitors against TcTS. It would have been easy to choose the four features with the highest score from Phase, but we believe that the four sites chosen here (N10, R20, N17, and A3Figure 3 1 ) are logical choices based on th e available information on TcTS. 22.214.171.124 Holo pharmacophore model The holo pharmacophore model was generated using a TcTS structure from clustering of an MD simulation with TcTS bound to sialyl lactose. The pharmacophore sites found by Phase for this model are des cribed in Table 32 There are many similarities between this model and the 1S0I hypothesis described above. Once again, the topranking site was a negative ionizable feature (N39) interacting with the arginine triad. Other sites from the holo pharmacophore model that were very similar to the 1S0I model include N38, D41, A34, D55, and R137. Given that the 1S0I and holo pharmacophore models were generated using different conformations of TcTS, differences in the features were antici pated. The major differences included the presence of features interacting with Trp120, Arg145 and Ser57 only in the holo pharmacophore, and the presence of sites interacting with Ala59, Asn60, Ser229,
82 Gln195, Gln362, and Asp247 only in the 1S0I hypothesis However, several of these differences are considered irrelevant because the TcTS residues involved are mobile and/or solvent exposed, such as Ser57, Asn60, and Gln362. When selecting four sites as required matches when screening possible inhibitors, we w anted to choose sites that were different from the 1S0I model, but still maintained at least two features known as important for substrate binding to TcTS. Table 32 Pharmacophore sites of the TcTS holo model. Sites marked with an asterisk (*) were required matches during screening. Feature labels were given by Phase, with the letter indicating the type of interaction (N = negative ionizable, A = hydrogen acceptor, D = hydrogen donor, and R = aromatic ring). The scores are Glide XP summed energy values in units of kcal/mol. Rank Feature Label Score Interaction with TcTS 1 N39* 10.43 Arginine triad (Arg35, Arg245, and Arg314) 2 N38* 3.72 Arg53 3 A5 3.27 Arg245 4 N117 2.87 Backbone NH of Trp120 5 A33 1.77 Trp120 backbone NH and side chain OH of Thr121 6 D41 1.60 Side chain oxygen of Tyr342 7 A8 1.59 Side chain OH of Tyr342 8 A17 1.49 Side chain NH of Trp120 9 R45* 1.37 Hydrophobic pocket near Val95 and Leu176 10 A34 1.31 Arg35 11 D80 1.00 Backbone carbonyl oxygen of Ser57 12 D55 0.80 Asp96 13 D20* 0.71 Asp96 and Glu230 14 R137 0.48 Acceptor binding site between Tyr119 and Trp312 15 R136 0.39 Tyr119
83 The differences were to ensure the same hit compounds were not discovered while screening compounds against the pharmacophore models. Thus, features N39 and N38 were chosen as the two sites that were consistent from the 1S0I model, while the acceptor binding site feature was excluded to increase hit compound diversity. As mentioned previously, the hydrophobic pocket near Val95 and Leu176 contains the N acetyl group of the substrate, but Phase found the pocket large and favorable enough to accommodate an aromatic ring. This finding is contrary to most TcTS drug design efforts that typically try to place a s imilar N acetyl group at this location, so we selected R45 to represent this region for screening. Figure 32 The required sites for the holo pharmacophore model used for screening lead compounds. Features N39 and N38 repres ent favorable interaction sites with Arg35/Arg314 and Arg53, respectively. The aromatic feature R45 is located in the hydrophobic region near Val95 and Leu176, while the hydrogen bond donor D20 feature is positioned to form interactions with the Asp96/Glu2 30 motif.
84 Finally, feature D20 was chosen as a new region of the active site to target. Investigations of the TcTS structure near feature D20 discovered that this region is an ideal location for a hydrogen donor because of the presence of nearby negatively charged residues Asp96 and Glu230. Furthermore, Glu230 is important to the catalytic mechanism as a proton acceptor for the nucleophilic Tyr34254 and stabilizing the substrate transition state,56 which makes it an ideal drug design target residue. To the best of our knowledge, our screening approach is novel because no one has attempted placing an aromatic ring in the hydrophobic pocket near Val95/Leu176 or targeted the Asp96/Glu230 mo tif when designing drugs against TcTS, which makes these four features N39, N38, R45, and D20 ( Figure 32 ) ideal for screening new inhibitors. 126.96.36.199 Apo pharmacophore model The apo conformation of TcTS was the most unique of the three s tudied because of the flexibility of the unliganded enzyme.59 This was the o nly pharmacophore model that did not include a negative ionizable site interacting with the arginine triad as the top ranking feature ( Table 33 ). Instead, a negative ionizable charge interacting with the side chain of Arg93 was t he most favorable site (N136). The negative site interacting with the arginine triad (N132) unexpectedly ranked fifth among sites for the apo model. Asp59 and Glu227 are the only residues involved in an interaction with a pharmacophore site in the apo model that was not present in either of the other two models. Given the role of Asp59 as an acid/base catalyst for TcTS, it is interesting to note that the other pharmacophore models did not produce any sites interacting with the side chain of Asp59, while the apo model did. However, in the apo conformation, Asp59 is unlikely to be a residue of high interest to target for drug design because it is almost completely solvent exposed. Expectedly, Phase found no pharmacophore site in the
85 acceptor binding site between Tyr119 and Trp312 because the flexibility of the Trp312 loop precludes an aromatic ring from binding to this site in this conformation.59 The re ranking and scoring of several similar sites among the three pharmacophore models further enhances the idea that the pure rankings should not be the ultimate deciding factor when consi dering what pharmacophore sites to select as required matches during the screening process. Table 33 Pharmacophore sites of the TcTS apo model. Sites marked with an asterisk (*) were required matches during screening. Feature l abels were given by Phase, with the letter indicating the type of interaction (N = negative ionizable, P = positive ionizable, A = hydrogen acceptor, D = hydrogen donor, and R = aromatic ring). The scores are Glide XP summed energy values in units of kcal/ mol. Rank Feature Label Score Interaction with TcTS 1 N136 5.19 Arg93 2 P21* 4.37 Asp96 and Glu230 3 A20 2.60 Backbone NH of Asp96 4 A3 2.18 Backbone NH of Trp120 5 N17* 2.04 Arginine triad 6 N133 1.89 Arg53 and Arg35 7 A15 1.65 Arg245 and side chain OH of Tyr342 8 D65 1.60 Side chain OH of Tyr342 9 D70 1.60 Side chain oxygen of Tyr119 10 D77 1.60 Side chain of protonated oxygen on Asp59 11 D82 1.60 Backbone carbonyl oxygen of Asp247 12 D83 1.60 Backbone carbonyl oxygen of Asp247 13 A37 1.55 Side chain OH of Ser229 14 R22* 1.42 Hydrophobic pocket near Val95 and Leu176 15 D64 1.20 Glu230
86 The choice of required matches for screening became more complicated for the apo pharmacophore model. The negative ionizable site representing the ligand interaction with the arginine triad (N17) was the first required site chosen. The second feature chosen was P21 because it represented a positively charged group interacting with two negatively charged residues Asp96 and Glu230 that seemed extremely favorable as mentioned for the holo pharmacophore. Originally, we also chose sites N136 and R22 as the last two required matches. Both were used in screening of previous pharmacophore models, but never at the same time. The two sites represent r egions of space in the pocket originally filled by the N acetyl group of sialyl lactose. Figure 33 The required sites for the TcTS apo pharmacophore model used for screening lead compounds. N17 forms interactions with the arginine triad, while the positive charged feature P21 interacts with the Asp96/Glu230 motif. The aromatic feature R22 is positioned in the hydrophobic pocket near Val95 and Leu176.
87 Choosing both sites allowed us the ability to screen for compounds that wer e specifically suited for the sialic acid binding site and the N acetyl binding region. Unfortunately, using these four sites N17, P21, N136, and R22resulted in no hit compounds during screening of the leads database. As Demir et al .59 noted, the active site of apo TcTS is much larger and more open than the holo structure, and thus any hit compounds from screening would need to be larger to match the sites in this active site compared to the other pharmacophore models. Large molecules often do not make ideal drugs and the ZINC Leads database screened here primarily consists of smal ler lead scaffolds, not large molecules. Consequently, we trimmed the required matches down from four to three and attempted using both sites as the third and final required site. The only combination that resulted in any hit compounds was using N17, P21, and R22 ( Figure 33 ). Thus, these three sites were chosen as the required features for the screening results discussed below for the apo pharmacophore model. 3.3.2 Screening Results Using TcTS Pharmacophore Models This section provides the virtual screening and binding free energy results from MD for the hit compounds found by Phase using the pharmacophore models described above. 188.8.131.52 Screening results for the 1S0I pharmacophore Screening of the ZINC Leads database using the 1S0I pharmacophor e resulted in 25 hit compounds. After performing MD and calculating the MM GBSA binding free energy for each compound, the potential inhibitors were reranked and the best five compounds are shown in Table 34 (see Table A 1 for all compounds). For the Phase Fitness scores, the higher value the better the compound fits the shape and topology of the pharmacophore model. Conversely, the more negative the MM GBSA binding free
88 energy the better the compound is expected to bind to TcTS. Aside from the compounds, Table 34 makes it clear why MD and MM GBSA binding free energy calculations were necessary there is no correlation between the Phase Fitness score and the MM GBSA binding free energies. Table 34 The best five compounds from screening results of the 1S0I pharmacophore according to MM GBSA binding free energies. The Phase Fitness scores are unitless, while the MM GBSA have units of kcal/mol. The uncer tainties reported represent the standard error of the mean. See Table A 1 for the results of all 25 hit compounds. The binding mode of ZINC13359679 is discussed in more detail in Section 184.108.40.206. Rank ZINC code Structure Phase Fit ness MM GBSA 1 ZINC13359679 0.184 76.3 0.4 2 ZINC23187566 0.173 58.0 0.7 3 ZINC20107259 0.187 56.4 0.8 4 ZINC73658621 0.177 53.5 0.6 5 ZINC06614928 0.199 49.2 1.1
89 This observation is consistent for all hit compounds found by Phase from the 1S0I pharmacophore as well as the holo and apo (see Appendix A.1 Table A 2 and Table A 3 ). Because the pharmacophore screening includes no specific details of the receptor it would be unrealistic to expect the Phase Fitness to correspond with the MM GBSA binding energies that are calculated as an average over 200 conformations of the compound noncovalently bound to the active site of TcTS. For drug design and discovery, Phase Fitness scores should be disregarded and replaced by a more sophisticated and rigorous binding affinity calculation that is known to accurately predict relative free energy differences, such as MM GBSA.187 As all of these comp ounds are derived from the 1S0I pharmacophore, they have some structural similarities because of the required features imposed during the screening process. Per the pharmacophore model, every hit compound contains a negative ionizable functional group (typically a carboxylic acid) that interacts with the arginine triad, a hydrogen acceptor or negative ionizable group that interacts with Arg53, a hydrogen acceptor interacting with Arg93, and an aromatic ring designed to fit into the acceptor binding site bet ween Tyr119 and Trp312. Overall, the 1S0I pharmacophore model resulted in the best hit compounds of the three pharmacophore models according to the binding energies, with four compounds with binding free energies better than 50 kcal/mol, and ten compounds better than 40 kcal/mol (once again, these are not considered real binding free energies and are only used to compare relative to one another). The better TcTS inhibitors likely arise from the 1S0I model because of the pharmacophore sites chosen as requi rements during the screening process. The 1S0I pharmacophore was the only hypothesis that required hit
90 compounds to have a presence in both the sialic acid and acceptor binding sites of TcTS, which is believed to yield more specific TcTS inhibitors than in hibitors that simply target the sialic acid binding site. The results here further support the idea that a strong TcTS inhibitor should target both of these sites. The MM GBSA binding free energies reported here are relative to one another and are used to compare directly to the calculated binding free energy of the TcTS substrate, sialyl lactose. MM GBSA calculations performed on the MD simulation of sialyl lactose bound to TcTS by Demir et al .59 determined that the average binding free energy was 54.8 0.1 kcal/mol for sialyl lactose using the same protocol reported here for the hit compounds. Thus, the three most promising hit compounds discovered using the 1S0I pharmacophoreZINC13359679, ZINC23187566, and ZINC20107259are projected to bind TcTS stronger than the natural substrate. ZINC13359679 is calculated to have a significa ntly stronger binding affinity ( 76.3 kcal/mol) than the substrate ( 54.8 kcal/mol), and thus the MD of this compound will be analyzed in more detail in Section 3. 3.3.1. As is, ZINC23187566, ZINC20107259, and ZINC73658621 are likely to compete with sialyl lactose binding in vivo but are not likely to be much better than the substrate. H owever, all of these compounds are good scaffolds for drug design efforts against TcTS because their structures are unique compared to any previously reported TcTS potential inhibitors. Modifications could be made to optimize each of the hit compounds in Table 34 to improve their binding affinity for TcTS. It is important to note that unlike for the screening process, the binding energies are averag es based on a dynamic process. Many of the hit compounds are initially in a single, ideal conformation within the TcTS binding pocket from screening that is not
91 maintained during the MD simulation. Instead, the binding free energy often gets worse during t he simulation as the contacts between the protein and ligand relax until the ligand reaches a state of equilibrium with the protein that results in a binding free energy that oscillates around a single value. This natural worsening of the binding free ener gy during simulations is a result of the screening process only producing a single ligand conformation and that ligand conformation is not even directly based on the protein conformation. 220.127.116.11 Screening results for the holo pharmacophore Screening of the ZINC Leads database using the holo pharmacophore resulted in 30 hit compounds. After performing MD and calculating the average MM GBSA binding free energy for each compound, the potential inhibitors were reranked and the best five compounds are shown in Table 35 (see Table A 2 for all compounds). The hit compounds from the holo pharmacophore screening are all unique compared to the compounds discovered from the 1S0I pharmacophore, naturally because the selected required sites and locations for the two pharmacophores were different. Of course, this is ideal for the purposes of discovering new inhibitors as it broadens the search criteria for novel ligands. These compounds all have a set of common features that c an align to the required pharmacophore sites of the holo epharmacophore. Each compound has a negative ionizable group positioned to interact with the arginine triad, a hydrogen acceptor or negative ionizable group positioned to hydrogen bond with Arg53, an aromatic ring to fill the hydrophobic pocket near Val95 and Leu176, and a hydrogen donor group that can form interactions with the side chains of Asp96 and/or Glu230.
92 Table 35 The best five hit compounds from screening resul ts of the holo pharmacophore according to MM GBSA binding free energies. The Phase Fitness scores are unitless, while the MM GBSA energies have units of kcal/mol. The uncertainties reported represent the standard error of the mean. See Table A 2 for the results of all 30 hit compounds. The binding mode of ZINC02576132 is discussed in more detail in Section 18.104.22.168. Rank ZINC code Structure Phase Fitness MM GBSA 1 ZINC02576132 0.194 73.2 0.5 2 ZINC40351842 0.145 55.1 0.4 3 ZINC60181779 0.198 52.2 0.4 4 ZINC19795370 0.184 48.5 1.0
93 Table 35. Continued Rank ZINC code Structure Phase Fitness MM GBSA 5 ZINC03406782 0.197 45.0 0.5 Overall, the hit compounds from the holo pharmacophore are not as strong of inhibitors compared to the compounds from the 1S0I pharmacophore screening. Only two compounds ZINC02576132 and ZINC40351842resulted in binding energies that were better than sialyl lactose, while the other 28 hit compounds were worse. S imilar to the results from the 1S0I pharmacophore screening, one compound, ZINC02576132, stands out as significantly better than sialyl lactose and the other hit compounds with a binding free energy of 73.2 kcal/mol. For this reason, the MD and binding mode of ZINC0257 6132 will be analyzed in more detail in Section 3. 3.3.2. The free energy of binding results suggest that ZINC40351842 and ZINC60181779 have a similar binding affinity as the substrate, and could compete for binding in vivo while the other hit compounds ar e unlikely to be anything but very weak inhibitors of TcTS. However, rational drug design efforts could use these compounds as new scaffolds for modification and optimization that improve their TcTS binding affinities. 22.214.171.124 Screening results for the apo pharmac ophore Screening of the ZINC Leads database using the apo pharmacophore resulted in 27 hit compounds. After performing MD and calculating the average MM GBSA binding
94 free energy for each compound, the potential inhibitors were reranked and the best five compounds are shown in Table 36 (see Table A 3 for all compounds). The hit compounds from the apo pharmacophore screening were expected to be more chemically diverse compared to the 1S0I and holo pharmaco phore results because only three pharmacophore sites were required for hit compounds instead of four like the 1S0I and holo models. Each hit compound was required to have a negative ionizable charge that could interact with the arginine triad, an aromatic ring for the hydrophobic pocket near Val95 and Leu176, and a positive ionizable group for hydrogen bonds with the side chains of Asp96 and Glu230. Unexpectedly, many of the hit compounds had very similar chemical structures, such as ZINC72447098, ZINC72432649 ZINC72477708, and ZINC72407055 (See Table A 3 ). The likely cause of this result is the shape and topology of the apo pharmacophore required sites. The TcTS active site is known to be larger and more open in the apo conformati on, which reduces the number of likely hit compounds because any potential hit compound must be large enough to accommodate the bigger active site. Furthermore, one of the required pharmacophore features included a positive ionizable group, which significantly reduces the number of compounds that could match this pharmacophore model. Unlike the screening results from the 1S0I and holo pharmacophores, no hit compound from the apo pharmacophore screening was discovered with a binding free energy significantly better than sialyl lactose. In fact, the best hit compound, ZINC72447098, had an average binding free energy identical to sialyl lactose ( 54.8 kcal/mol).
95 Table 36 The best five hit compounds from screening of the TcTS apo pharmacophore according to MM GBSA binding free energies. The Phase Fitness scores are unitless, while the MM GBSA energies are in units of kcal/mol. The uncertainties reported represent the standard error of the mean. See Table A 3 for the results of all 27 hit compounds. Rank ZINC code Structure Phase Fitness MM GBSA 1 ZINC72447098 0.155 54.8 0.3 2 ZINC67946325 0.186 50.1 0.4 3 ZINC72145690 0.144 44.0 0.5 4 ZINC19850530 0.168 42.0 0.3 5 ZINC72432649 0.168 41.8 0.3 At best, ZINC72447098 would bind to TcTS with about the same affinity as sialyl lactose, while all the other hit compounds would bind worse. Of course, these compounds should all be considered lead candidates for rational drug design against
96 TcTS, regardless of their relatively poor calculated binding free energies compared to the 1S0I and holo screening results. Specifically, the chemical scaffolds that are observed multiple times in Table A 3 should be considered for optimization given their prevalence as hit compounds discovered using the apo pharmacophore. The repeated occurrence of compounds similar to ZINC72447098 may indicate that this scaffold is viable as an inhibitor of TcTS with some optimization. 3.3.3 Analysis o f the Most Promising TcTS Inhibitors Phase found a total of 82 hit compounds from the ZINC Leads database using the three presented pharmacophore models, and MM GBSA binding free energy calculations suggest that two of these compounds ZINC13359679 and ZINC02576132have significantly more affinity for TcTS than the natural substrate, sialyl lactose. In this section we will analyze the MD simulations of these two compounds bound to TcTS. 126.96.36.199 ZINC13359679 ZINC13359679 was the top hit compound discovered using the 1S0I pharmacophore, with a MM GBSA binding free energy of 76.3 kcal/mol ( Table 34 ). The protein and ligand were found to be relatively stable during the 10 ns production trajectory, with an RMSD of less than 2.0 for most of th e simulation ( Figure 34 ). As the RMSD plot shows, neither the protein nor the ligand are undergoing any major conformational changes after the initial relaxation and equilibration steps, suggesting ZINC13359679 is stable within t he active site of TcTS. The two spikes in RMSD near 1.5 ns and 4.3 ns are related to movements near the periphery of the enzyme and should not be associated with instability of the enzyme or active site.
97 Figure 34 The RMSD o f TcTS and ZINC13359679 using the initial frame as reference. The RMSD was calculated using only the backbone atoms for TcTS, and all atoms for ZINC13359679. Since the binding free energy reported represents an average value of a dynamic property, it is al so informative to examine the binding free energy as a function of time ( Figure 35 ). The binding energy fluctuates as the simulation progresses, but the value oscillates around the average value and does not appear to be trending upward or downward. This plot indicates the stability of ZINC13359679 bound to TcTS in the conformation from pharmacophore screening. Another important observation is that the binding free energy is never worse than the average binding free energy of sial yl lactose to TcTS ( 54.8 kcal/mol) for the 200 frames analyzed, providing more optimism that ZINC13359679 would bind to TcTS better than the substrate in vivo
98 Figure 35 The MM GBSA binding free energy of ZINC13359679 bound to TcTS as a function of time for a 10ns MD simulation. The contacts between TcTS and ZINC13359679 are a clear mixture of polar and nonpolar interactions ( Figure 36 ). The strongest set of polar interactions naturally exists bet ween the carboxylic acid and the arginine triad, which is also important for stabilizing the carboxylic acid of the substrate. In fact, hydrogen bonding analysis shows that hydrogen bonds exist between ZINC13359679 and the three residues that comprise the arginine triad (Arg35, Arg245, and Arg314) more than 80% of the simulation ( Figure 36 ). Interactions with the arginine triad are a key component of any potential inhibitor of TcTS. The initial proteinligand conformation from pharmacophore screening included the sulfate functional group forming hydrogen bonds with Arg53, and the cyano group hydrogen bonded to Arg93. However, relaxation of the ligand complexed with TcTS significantly reduced both of these interactions. The sulfate group is never close enough
99 to form a hydrogen bond with Arg53 during the simulation, but is likely close enough (~4.25 ) to make favorable Coulombic interactions for the majority of the simulation. ZINC13359679 shifted slightly during relaxation to a con formation with the cyano group of the ligand hydrogen bonding to the side chain of Thr121 for approximately 13% of the simulation instead of Arg93. The dominating nonpolar proteinstacking between aromatic rings on the ligand and three TcTS residues Phe58, Tyr119, and Trp312 ( Figure 36 ). The 1S0I pharmacophore contained a required feature involv ing an stacking interaction with Phe58 was unexpected. In the initial conformation, Phe58 is not involved in any proteinligand interactions, but ~6.7 ns into the simulation, t he side chain of Phe58 changes conformation and forms a T stacking interaction with a phenyl ring on ZINC13359679. In this conformation, Phe58 is positioned over the active site like a lid preventing the ligand from exiting. Trp312 is also making a T stacking interaction with the same phenyl ring of ZINC13359679 as Phe58, stacking interaction with the other ligand aromatic ring. Phe58 has no known role in substrate binding, and is unlikely to form a similar interaction with the substrate given that the lactose in the acceptor binding site is always attached to another sugar, preventing Phe58 from closing the top of the active site. ZINC13359679 forces Tyr119 and Trp312 of the acceptor binding site to deviat e from their traditional conformation with sialyl lactose bound. Typically, Tyr119 and Trp312 sandwich a lactose acceptor molecule, which means they are forming nonpolar stacking wit h
100 different aromatic rings. This is a reasonable observation given the known flexibility of the Trp312 loop on TcTS.59 Although ZINC13359679 did not maintain all of the interactions originally described by the 1S0I pharmacophore model, it was able to form many favorable polar and nonpolar interactions with TcTS that make ZINC13359679 a strong candidate to inhibit TcTS. Figure 36 The protein ligand interactions between TcTS and ZINC13359679. Specifically, the a) polar and b) nonpolar interactions. Percentages correspond to the time a hydrogen bond was present during the MD simulation. For clarity, all hydrogen atoms have been removed except for those involved in hydrogen bonding. 188.8.131.52 ZINC02576132 ZINC02576132 was the top hit compound discovered using the holo pharmacophore, with a MM GBSA binding free energ y of 73.2 kcal/mol ( Table 35 ). During dynamics both TcTS and ZINC02576132 were very stable, producing RMSD values less than 1.0 ( Figure 37 ), suggesting that neither the protein nor the ligand undergo any major conformational changes during the 10ns simulation. The complex appears much more stable compared to TcTS in complex with ZINC13359679 ( Figure 3 4 ), which had RMSD values closer to 2.5 The lower structural fluctuations of
101 ZINC02576132 compared to ZINC13359679 is an indication that ZINC02576132 is more stable in the TcTS active site. Figure 37 The RMSD of TcTS and ZINC02576132 using the initial frame as reference. The RMSD was calculated using only the backbone atoms for TcTS, and all atoms for ZINC02576132. We also investigated the MM GBSA binding free energy as a function of time to check for any upward or downward trends in the binding affinity ( Figure 38 ). T he binding energy fluctuates approximately the same amount as for ZINC13359679, and there is no large movement upwards or downwards that suggests the binding energy is trending to a different average energy than the one reported. Furthermore, once again th e binding free energy is consistently better than sialyl lactose ( 54.8 kcal/mol), which is desirable for any potential inhibitor of TcTS.
102 Figure 38 The binding free energy of ZINC02576132 bound to TcTS as a function of time for a 10ns MD simulation. Unlike ZINC13359679, the interface between ZINC02576132 and TcTS appears to be dominated by polar interactions ( Figure 39 ). The nonpolar interactions present are simple van der Waals contacts with no r eal possibilities for proteinstacking since the ZINC02576132 phenyl group is located in the hydrophobic pocket near Val95 and Leu176, not in the acceptor binding site between Tyr119 and Trp312. The hydrogen bonds between the ligand carboxylate and the arginine triad do not have as high occupancy as ZINC13359679 (all more than 80% for ZINC13359679; none more than 54% occupied for ZINC02576132), but this is compensated for by increased hydrogen bonds with other protein residues. For example, Arg53 i s ideally positioned to form two
103 hydrogen bonds (66% and 44% occupied) with the carbonyl oxygen on ZINC02576132. Likely more important is the NH2+ group of the ligand that forms a total of three hydrogen bonds with the Asp96/Glu230 motif, including a hydrogen bond with an occupancy over 90% with Asp96 (the most sustained TcTS ZINC02576132 hydrogen bond). Figure 39 The main interactions between TcTS and ZINC02576132 observed during a 10ns MD simulation. The percentages represent the time a hydrogen bond was present during the dynamics.
104 The holo pharmacophore specifically required a hydrogen donor be present at this location for any hit compounds with the intent of interacting with this motif. The important role that Glu230 plays in acid/base catalysis combined with the close proximity of Asp96 makes this area of the active site a key target for drug design, and ideally suited to accommodate a positive ionizable group from an inhibitor (such as the NH2+ group of ZINC02576132). The strong binding free energy observed for ZINC02576132 provides further evidence that the Asp96/Glu230 motif could be a key to designing low nM inhibitors of TcTS. Although ZINC02576132 is stable in the active site and has a strong binding affinity f or TcTS, key optimization could still make it a better inhibitor. Specifically, a clever addition of a phenyl group near the carboxylate could stacking interactions with Tyr119 and Trp312 in the important acceptor binding site. 3.4 Concluding Remarks In this work, we wanted to create pharmacophore models detailing the important residues for ligand binding to TcTS and utilize that information to screen for novel inhibitors. To our knowledge, no pharmacophore models of TcTS have been created previously due to a lack of known active molecules. But the method recently proposed by Loving et al .162 allows for the generation of pharmacophore models w ithout a priori knowledge of actives by using the energetic results of docked fragments to the protein. We created three epharmacophore hypotheses using three different conformations of TcTS (PDB 1S0I, holo, and apo) each with 15 unique features. To our k nowledge, these are the first pharmacophore models ever reported for TcTS and provide a detailed description of locations important for drug inhibition.
105 Different combinations of three or four sites from each pharmacophore were rationally chosen as required features while screening ZINCs Leads database for novel inhibitor scaffolds. The required sites included TcTS residues or motifs that were deemed important to TcTS function and substrate binding based on the pharmacophore models and available literature : Arg53, Arg93, Asp96, Tyr119, Glu230, Trp312, the hydrophobic pocket near Val95/Leu176, and the arginine triad (Arg35, Arg245, and Arg314). Screening of over 4 million lead compounds resulted in 82 hit compounds, which were all simulated using MD and anal yzed using MM GBSA free energy calculations to directly compare the relative binding affinities of each with sialyl lactose. Although many of the hit compounds are considered novel lead scaffolds, two compounds ZINC13359679 and ZINC02576132 had significant ly better average binding free energies than sialyl lactose. The MD simulations were analyzed for both compounds to determine the binding modes and important proteinligand interactions. These molecules are the newest compound scaffolds proposed to inhibit TcTS in the search for suitable drugs to combat Chagas disease. Future work will focus on using the pharmacophore models developed here to screen other databases containing drug like compounds to discover more unique hit compounds as potential TcTS inhibitors. Finally, we plan to obtain these hit compounds from vendors and perform kinetic assays to calculate in vitro inhibition constants against TcTS.
106 CHAPTER 4 COMBINED MOLECULAR DOCKING, MOLECULAR DYNAMICS AND BINDING FREE ENERGY CALCULATIONS TO DESIGN AND DISCO VER NOVEL INHIBITORS FOR TRYPANOSOMA cruzi TRANSSIALIDASE 4.1 Preliminaries Virtual screening (VS), the process of testing large numbers of compounds for inhibitory activity in silico has been useful for the discovery of new drugs. Specifically, active inhi bitors have been discovered using VS on enzymatic targets such as farnesyltransferases,207 carbonic anhydrase,208 cysteine proteases,209 and trihydroxynaphthalene reductase.210 VS typically involves molecular docking to screen a database of compounds. Because molecular databases can contain millions of compounds, VS m ethods must be quick at determining if a molecule has the potential to be active against an enzyme. As a result, VS methods often suffer from a lack of accuracy at predicting and ranking binding affinities for a set of compounds. For these reasons, VS is usually combined with other more rigorous methods, such as docking with more sophisticated search and scoring algorithms, de novo design, molecular dynamics, and free energy of binding calculations. Schrdingers Glide molecular docking program contains thr ee different search and scoring algorithms HTVS, SP, and XP that vary in their speed and accuracy. Glide HTVS is the computationally cheapest and least robust docking method available in Schrdinger and is ideal for VS studies intended to remove unrealisti c compounds as inhibitors. In most cases, the more promising compounds from HTVS docking should be considered for Glide SP, which is significantly more accurate than HTVS at distinguishing active compounds from inactive ones.152 After SP docking, Glide XP can be used to redock and re score the best compounds in order to help reproduce
107 experimental proteinligand binding modes and affinities.151 However, Glide XP is not required in this process if more sophisticated techniques will be employed, such as MD. Enzymes are not rigid structures in vivo so it would be unrealistic to expect a single proteinligand conformation from docking to fully describe all the binding modes of an inhibitor to its receptor. A trajectory generated from MD creates an ensemble of conformations that provides much more detail about the binding mode of an inhibitor than a single docked structure. Furthermore, these snapshots can be used to calculate the free energies of binding for multiple inhibitors using more rigorous endstate calculated, such as Molecular Mechanics/Generalized Born Surface Area (MM GBSA).188 The combination of molecular docking and molecular dynamics has bec ome a powerful tool for computational drug design and discovery.197 In this chapter, we use a VS method that combines molecular docking, de novo design, molecular dynamics, and free energy of binding calculations to discover new potential inhibitors of Trypanosoma cruzi trans sialidase (TcTS) Millions of compounds have been docked using Schrdingers Glide program, with MD performed on the most promising drug candidates using Amber. De novo design techniques have also been applied to several of these molecules in an attempt to optimize their binding to the TcTS active site. MM GBSA free energies of binding of each potential inhibitor have been calculated using snapshots from the corresponding MD simulations. The binding modes of the top inhibitor candidates from MD are reported in more detail. 4.2 Methods In general, the methodology described in this chapter is similar to the workflow depicted in Figure 25 The details of each step are laid out in more detail in the following sections.
108 4.2.1 Acquisition of Compounds 184.108.40.206 Molecular l ibraries Over 35 million structures were obtained from the ZINC198 website ( http://zinc.docking.org ) from 25 different libraries. A list of the molecular databases downloaded and used in this study along with their corresponding molecule counts can be found in Table 41 Table 41 List of ZINC molecular libraries and their corresponding number of compounds used for virtual screening. Molecular Library Number of Compounds ChemBridge 889,605 ChemDiv 1,020,993 Chemical Block 125,361 Clean Drug like 10,384,763 Clean Fragments 504,074 Clean Leads 5,135,179 DrugBank Experimental 5,046 DrugBank Nutriceuticals 124 DrugBank Small Molecules 1,408 Enami ne 1,698,842 Innovapharm 329,080 Natural Products 93,478 National Cancer Institute (NCI) 329,394 NCI Diversity Set II 41,674 Otava 193,226 PBMR Labs 480,244 Pharmeks 259,523 Princeton Biomolecular Research 798,402 PubChem 10,080,211 Scientific Ex change 47,827 Sigma Aldrich 77,614 Specs 206,615 TimTec 184,675 UORSY 1,190,916 VitasM 1,180,514 Total 35,258,788 Libraries obtained from other sources were also screened for potential inhibitors, including cHEMBL ( 3 027 553 compounds), Asinex Gol d ( 177 463 compounds), and a
109 collection of compounds maintained at the University of Florida by Dr. Alan Katritzky (31,754 compounds). Structure similarity searches on the PubChem website ( http://pubchem.ncbi.nlm.nih.gov/ ) were performed to help identify available derivatives of two promising compounds ZINC16247520 and ZINC19328706. 220.127.116.11 De novo design A few compounds were selected for optimization using de novo design methods with either LigBuilder140 or AutoGrow.138 These compounds included DA NA, oseltamivir, sialic acid, ZINC16247520, and ZINC20472820. DANA and oseltamivir were chosen because of their ability to strongly inhibit related neuraminidase enzymes, with the idea that a de novo design technique might improve their binding affinity for TcTS. ZINC16247520 and ZINC20472820 were chosen because they provided unique proteinligand interactions, but had the potential to make other contacts that could improve their binding affinities. LigBuilder was also used to combine (i.e. link) multiple d ocked fragments with decent docking scores into a single compound for MD analysis. 18.104.22.168 Rational drug design Two compounds ZINC02044476 and a DANA derivative were modified using rational modifications based on their respective structures from molecular docking results. 4.2.2 Docking Potential Ligands with Glide 22.214.171.124 Receptor grid generation The snapshots generated from MD simulations of TcTS apo and holo structures from a previous study59 were clustered as detailed in Chapter 3. The apo and holo grid files used in Chapter 3 are identical to the ones used for the docking calculations here.
110 126.96.36.199 Molecula r docking The Glide HTVS docking scheme with default settings was applied to all molecular libraries introduced in Section 188.8.131.52. The compounds with binding free energies better than 7.00 kcal/mol ( 41, 187 compounds) were chosen for further analysis using Glide SP. Those compounds were docked with the default setting for Glide SP using the docked conformations from HTVS. Finally, the best 3,000 compounds from SP were selected for Glide XP docking with default settings. 4.2.3 De novo Drug Design The small fragme nt library released with AutoGrow138 was used to modify ZINC16247520 in an attempt to increase the favorable proteinligand interactions without making the compound too large. All possible modification sites on the ligand were chosen as possible substitution locations. LigBuilder 2.0140 wa s used to modify DANA, oseltamivir, and ZINC20472820. Similar to AutoGrow with ZINC16247520, all possible sites (nonpolar hydrogen atoms) were used as potential locations for functional group substitutions. LigBuilder was preferred over AutoGrow for most automated design because AutoGrow tended to run significantly longer without noticeably better results. Unlike AutoGrow, LigBuilder does not explicitly dock the compounds it designs. Instead, the input ligand structure (i.e. anchor) is docked into position previously, and the de novo algorithm simply makes modifications to the ligand in its docked conformation and recalculates the new binding score. Because this is a very crude method for determining the binding free energies, all compounds created by LigB uilder were re docked using Glide SP followed by Glide XP both with default settings again.
111 Re docking with Glide results in a more reliable docked conformation and binding free energy compared to the LigBuilder results. 4.2.4 Molecular Dynamics MD was performed using Amber203 on the most promising compounds from Glide docking results. The MD procedure was exactly as described in the Methods section of C hapter 3. Ten nanoseconds of production MD was performed for each compound, except in cases where additional MD was completed to gain a better understanding of the inhibitor binding mode. 4.2.5 Free Energies of Binding The binding free energies were calculated using the Generalized Born solvation model (MMGBSA) with MMPBSA.py189 as previously described in the Methods section of Chapter 3. 4.3 Resu lts The twelve compounds with the best free energies of binding from MM GBSA calculations are listed in Table 42 The twelve compounds listed in Table 42 are the inhibitors analyzed that resulted in fre e energies of binding that were stronger than those calculated for DANA, which is known to be a poor inhibitor of TcTS. For a relative comparison, free energies of binding were calculated using MD trajectories of the substrate (sialyl lactose) and DANA previously performed in our group59 that corresponded to 54.8 0.1 kcal/mol a nd 53.8 0.3 kcal/mol, respectively. The difference between the calculated binding free energies is consistent with the experimental relative binding affinities.63,64 The MM GBSA binding affinities reported in Table 42 are averaged over 10ns MD simulations, except for DANA Result 034 and PubChem 54353125 that were run for 50 ns and 110 ns, respectively.
112 Table 42 The best potential inhibitors of TcTS from virtual screening and de novo drug methods. MM GBSA binding free energies ar e in units of kcal/mol and are averaged over 10 ns of MD, except DANA Result 034 and PubChem 54353125 that were ran for 50 ns and 110 ns, respectively. The MM GBSA errors are reported as standard errors of the mean. Rank Ligand Name Structure Source MM GBS A (kcal/mol) 1 DANA Result 034 LigBuilder 91.2 0.1 2 Oseltamivir Result 70 LigBuilder 72.5 0.2 3 PubChem 54353125 PubChem 72.1 0.2
113 Table 42. Continued Rank Ligand Name Structure Source MM GBSA (kcal/mol) 4 ZINC16247520 Rank 10 A utoGrow 62.0 0.2 5 Sialic Acid Rank 1 AutoGrow 61.5 0.3 6 ZINC04939766 ZINC Leads 58.5 0.2
114 Table 42. Continued Rank Ligand Name Structure Source MM GBSA (kcal/mol) 7 Zanamivir phosphonateRank 62 Literature 57.9 0.3 8 Zanam ivir phosophonate Rank 1 Literature 55.8 0.3 9 ZINC34334192 ZINC Leads 54.7 0.2 10 ZINC04280204 ZINC Leads 54.6 0.2
115 Table 42. Continued Rank Ligand Name Structure Source MM GBSA (kcal/mol) 11 Asinex 380341 Asinex 54.3 0.4 12 PubCh em 12000964 PubChem 54.1 0.2 Since MM GBSA calculations are more rigorous and involve the analysis of many proteinligand conformations from an MD trajectory, the relative rankings of potential inhibitors are based on these results instead of the Gl ide docking free energies. Furthermore, MMGBSA calculations have previously been shown as an improvement over docking results.197 This is especially important given the lack of correlation between Glide and MM GBSA binding free energies, even using Glide XP, the most sophisticated scoring func tion Schrdinger offers ( Figure 41 ).
116 Figure 41 A plot of MM GBSA binding free energies compared to Glide XP docking free energies. The results suggest there is no clear correlation between the two methods. 4.4 Discussion The most promising potential inhibitors of TcTS were analyzed in great detail, but a detailed list of all potential inhibitors simulated with MD after docking to TcTS can by found in the Appendix ( Table A 4 ). I n this section, we will examine the binding modes of the best eight potential inhibitors discovered from virtual screening and de novo design. Furthermore, we will detail potential structural modifications that were made attempting to improve the binding f ree energies where applicable. Finally, we will examine a molecule created by combining fragments with strong free energies of binding from docking calculations.
117 4.4.1 Best Potential Inhibitors 184.108.40.206 DANA Result 034 DANA, known as a strong inhibitor of related neurami nidases, was found to be a poor inhibitor of TcTS.63,64 Nevertheless, medicinal chemists attempting to find strong inhibitors of TcTS have attempted to modify DANA to make it specific to TcTS, but it is our understanding that de novo drug design has never been performed on DANA. Thus, a Glide XP docked conformation of DANA to the holo TcTS grid was used as input for LigBuilder 2.0140 to generate many derivatives of DANA. The top 200 derivatives from Lig Builder were subsequently docked to the holo TcTS grid using Glide SP followed by Glide XP docking methods. The 34th best derivative according to LigBuilder resulted in the best Glide XP score ( 14.38 kcal/mol) of all 200 compounds, and 50 ns of production MD on DANA Result 034 bound to TcTS resulted in a MM GBSA free energy of binding of 91.2 0.1 kcal/mol ( Table 42 ). The stability of the enzyme and ligand was measured from the initial conformation using RMSD, as shown in Figure 42 The RMSD for the backbone atoms of TcTS remains consistently below 1.5 during the 50ns trajectory, while the ligand, DANA Result 034 is very mobile with the RMSD changing rapidly between 1.5 and 3 throughout the simulation. The flexibility of the ligand is localized in the flexible region containing the naphthalene functional group, shown in green in Figure 43 The regions of DANA Result 034 that bind to the sialic acid binding site of TcTS remain mostly rigid throughout the simulation, while the naphthalene group moves freely. AutoGrow likely added the naphthalene group during de novo design to stack with Tyr119 or Trp312, that are known to be important during TcTS catalysis.
118 Figure 42 The RMSD of TcTS and DANA Result 034 using the initial frame as reference. The RMSD was calculated using only the backbone atoms for TcTS, and all atoms for DANA Result 034. Figure 43 Depiction of the flexibility of the naphthalene region (green) of DANA Result 034 compared to the rest of the ligand. Enzym e residues immediately flanking the naphthalene, Tyr119 (left) and Trp312 (right) are shown in orange. For clarity, hydrogen atoms have been excluded.
119 However, the linker region between naphthalene and the rest of the ligand is flexible, and the length of the linker forces the naphthalene group to extend partially into the solvent, which yields an extremely flexible region of the ligand. Despite the variability of the ligand, the free energy of binding of DANA Result 034 bound to TcTS remains relatively consistent during the simulation and does not appear to be getting better or worse as the simulation progresses ( Figure 4 4 ). Figure 44 The MM GBSA binding free energy of DANA Result 034 bound to TcTS as a function of time for a 50ns MD simulation. Importantly, the binding free energy remains no worse than 15 kcal/mol better than the MMGBSA binding free energy of the substrate (sialyl lactose, 54.8 kcal/mol). It should be noted once again that the calculated MM GBSA binding free energies are not considered real binding free energies, should not be considered quantitative, and should only be used to compare the relative affinities of ligands for the same system. Regardless, the strong free energy of bi nding from MM GBSA calculations is
120 encouraging and suggests that DANA Result 034 has the potential to bind much tighter to the enzyme than the substrate in vivo although the binding mode would not make it a match for any of the epharmacophore models pres ented in Chapter 3. Unfortunately, the variability in the ligand structure at the mouth of the TcTS active site means there likely are still structural improvements that can be made to improve the binding affinity. We used rational drug design ideas to propose modifications to DANA Result 034 that could potentially improve the overall binding free energy of the ligand, with a special emphasis on alterations of the linker arm and naphthalene ring ( Figure 45 ). Figure 45 Modifications made to DANA Result 034. The structural changes are color coded in the table alongside the corresponding change in free energy of binding compared to the original DANA Result 034 inhibitor. Free energies are in units of kc al/mol.
121 Because DANA was the core structure, only functional groups that were added by AutoGrow were used as potential mutation sites. Structural modifications were performed manually using Avogadro211 without changing the remaining coordinates. This allowed multiple modifications to the structure without redocki ng the ligand, which could potentially result in an undesirable change in the overall binding mode. Since the binding modes of all DANA Result 034 derivatives considered were consistent, the MD and free energies of binding could be directly compared relati ve to one another. Two of the substitutions Short Arm 1 and OH resulted in significantly better inhibitors of TcTS with improvements in the binding free energy of 23.6 kcal/mol and 21.5 kcal/mol, respectively. The Short Arm 1 modification involved the removal of a CH2 group from the linker region (circled in orange in Figure 4 5 ) that allows the naphthalene group to stacking network. Meanwhile, the OH group replac ed the NH3 + group (circled in red in Figure 45 ) that interacts with the Asp96/Glu230 motif deep in the active site. Unfortunately, the structural substitutions were not found to be additive, though, because a DANA Result 034 der ivative that combined multiple promising substitutions was actually worse than the original compound by 6.1 kcal/mol from MM GBSA calculations. In general, DANA Result 034 is predicted to be a strong inhibitor of TcTS using molecular docking, MD, and MM GB SA calculations. Proposed modifications suggest that structural improvements could make the ligand a better inhibitor of TcTS. However, DANA Result 034 has several shortcomings as a drug option to treat Chagas disease. First, DANA Result 034 is large and flexible, while an ideal drug would be small and rigid in comparison. Second, although DANA is readily available, the process of making
122 such a large, complex molecule with many chiral centers could create several technical difficulties during synthesis. Third, the specificity of DANA Result 034 to bind to TcTS over other similar enzymes (specifically neuraminidases) is unknown. Fourth, it has not been specifically tested if this compound with so many potential hydrogen bonding partners would have any unintended interactions during drug delivery in a patient. 220.127.116.11 Oseltamivir Result 70 Similar to DANA Result 034, Oseltamivir Result 70 was designed using LigBuilder based on a structure of Oseltamivir (more commonly known as the antiviral drug Tamiflu) docked to TcT S using Glide XP. Oseltamivir is a strong inhibitor of neuraminidase, which is similar in structure and sequence to TcTS. After LigBuilder generated 200 different Oseltamivir variations, the derivatives were docked to the holo TcTS grid using Glide. The be st ligand from Glide XP was Result 70, which was found to be the second best inhibitor of TcTS after 10 ns of MD and MM GBSA calculations with a free energy of binding of 72.5 0.2 kcal/mol ( Table 42 ). TcTS does not undergo any large conformational changes during the simulation with an RMSD consistently below 1.5 ( Figure 46 ). Oseltamivir Result 70, on the other hand, undergoes a conformational change corresponding to an RMSD change of more than 1.0 near 4 ns into the simulation. This structural change is subtle and involves a 180o rotation of the plane of the naphthalene functional group in the acceptor binding region of the active site ( Figure 47 ). Since this rotation res ults in a structure similar to the original conformation, it is not surprising that the binding free energy remains fairly constant over this part of the simulation without any significant changes other than anticipated fluctuations caused by the normal dy namics of the system ( Figure 48 ).
123 Figure 46 The RMSD of TcTS and Oseltamivir Result 70 using the initial frame as reference. The RMSD was calculated using only the backbone atoms for TcTS, and all atoms for Oseltamivir Result 70. Figure 47 Depiction of the rotation conformational change observed for Oseltamivir Result 070 during MD around 4 ns. For clarity, all hydrogen atoms have been removed.
124 Figure 48 The MM GBSA binding free energy of Oseltamivir Result 70 bound to TcTS as a function of time for a 10ns MD simulation. Oseltamivir is a strong inhibitor of neuraminidase and binds directly to the sialic acid binding pocket in TcTS. Lig Builder designed an oseltamivir derivative (Oseltamivir Result 70) that interacts with new regions of the TcTS. This new potential inhibitor of TcTS interacts deeper in the hydrophobic pocket near Val95/Leu176, in addition to maintaining a presence in the acceptor binding site near Tyr119 and Trp312. The formation of these new proteinligand interactions makes this oseltamivir derivative a more specific binder to TcTS than the original compound. However, Oseltamivir Result 70 still suffers from the same dis advantages as DANA Result 034, mainly being too large, flexible, and likely difficult to synthesize. Similar to DANA Result 034, Oseltamivir Result 70 would not be a fit for any of the epharmacophore models described in Chapter 3.
125 18.104.22.168 PubChem 54353125 chEMBL 198898 ( Figure 49 ) had the best free energy of binding from docking of the metabolite molecular library chEMBL with Glide HTVS to the holo grid. The proteinligand interactions from docking suggested this was a promising lead candidate to inhibit TcTS, but the MM GBSA binding free energy after 10 ns of MD was poor ( 18.1 0.2 kcal/mol) because the compound was not stable in the active site. A structure similarity search was performed on the PubChem website ( http://pubchem.ncbi.nlm.nih.gov/ ) to obtain compounds that shared at least 80% structure identity to chEMBL 198898. The PubChem search resulted in 224 unique structures that were subjected to Glide SP and Glide XP docking. The m olecule with the best binding free energy from docking was PubChem 54353125 ( Figure 49 ), which had a Glide XP binding free energy of 9.74 kcal/mol. Unlike its chEMBL counterpart, PubChem 54353125 is stable and resulted in a free energy of binding of 72.1 0.2 kcal/mol after 110 ns of unrestrained MD from MM GBSA calculations ( Table 42 ). This compound remains the best potential inhibitor of TcTS without being modified from a molecular library using eit her de novo or rational drug design methods. Figure 49 The 2D structure of chEMBL 198898 and PubChem 54353125. PubChem 54353125 (b) was obtained from performing a structure similarity search of chEMBL 198898 (a).
126 Figure 410. The RMSD of TcTS and PubChem 54353125 using the initial frame as reference. The RMSD was calculated using only the backbone atoms for TcTS, and all atoms for PubChem 54353125. Figure 411. The M M GBSA binding free energy of PubChem 54353125 bound to TcTS as a function of time for a 110ns MD simulation.
127 The plots of RMSD ( Figure 410) and free energy of binding ( Figure 4 11) for PubChem 54353125 further show the stability of the ligand in the TcTS active site. The RMSD for both the enzyme and ligand remain under 2.0 for the entire 110ns simulation, excluding a sharp increase in RMSD for TcTS near the end of the simulation corresponding to move ment of the N terminal tail in solution away from the active site. The binding free energy also remains relatively stable, and maintains a binding free energy better than sialyl lactose ( 54.8 kcal/mol) during the simulation except for a brief stretch near 37 ns. The strong binding free energy of PubChem 54353125 from MD is a direct result of the binding mode and the resulting proteinligand interactions ( Figure 412 ). Figure 412. Protein ligand inter actions between TcTS and PubChem 54353125. Polar interactions in the sialic acid binding site are shown using two different orientations a) and b) to show all hydrogen bonds. Interactions in the acceptor binding site c) are also shown. Hydrogen bonds are r epresented using black stacking interactions are depicted using orange dotted lines. For clarity, only polar hydrogen bonds are shown. Throughout the MD simulation, PubChem 54353125 forms hydrogen bond interactions with nine different residues Arg93, Asp59, Arg53, Arg35, Arg314, Arg245, Asp96, Glu230, and Tyr119 stacking interactions with two residues Tyr312 and
128 Phe58. As anticipated for any strong inhibitor of TcTS, PubChem 54353125 forms hydrogen bonds with the arginine triad (A rg35, Arg314, and Arg245), but unexpectedly the binding mode predicted by Glide XP involves two neutral carbonyl oxygen atoms on the ligand hydrogen bonding to the triad. The predicted binding mode was a surprise because the ligand has a carboxylic acid, w hich is the more traditional hydrogen bonding partner with the arginine triad, that Glide XP positioned to interact with Arg93 instead. The discovery of a molecule using carbonyl oxygen atoms instead of a carboxylic acid interacting with the arginine triad could lead to the discovery of a completely new class of TcTS inhibitors. Although, it is important to note that the lack of a carboxylic acid interacting with the arginine triad makes PubChem 54353125 not a match for any of the epharmacophore models presented in Chapter 3. Another strong proteinligand interaction is between the guanidinium group on the ligand and the Asp96/Glu230 motif. The positive charge of the guanidinium group is intended to mimic the carbocation of the transition state during catal ysis. Figure 413. Representations of the conformational changes for two TcTS residues during MD of the TcTS PubChem 54353125 complex. For a) Asp59 and b) Tyr119, the original Glide XP docked conformation is shown in red, whil e the conformation after 110 ns of MD is shown in blue.
129 Comparing the Glide docked conformation with the structures from MD help demonstrate the importance of performing simulations after docking. A docked structure from Glide is created using a fixed receptor structure, and MD allows the enzyme and ligand to adapt to one another, sometimes creating significantly different conformations of the protein ligand interface. For example, the Glide XP docked conformation of TcTS PubChem 54353125 has Asp59 and Tyr119 in their expected location with the substrate in the active site. However, during the MD simulation, it is observed that these two residues actually change conformations and rotate down to form specific hydrogen bonds with the ligand ( Figure 413). Although the MD simulation was important for obtaining the relaxed binding mode of PubChem 54353125 in complex with TcTS, it is worth noting that despite these two residues significantly changing conformations during MD, neither had an overwhelming impact on the overall free energy of binding for the potential inhibitor. PubChem 54353125 had the strongest free energy of binding of any compound docked in this study that was not derived from de novo drug design. Although the ligand is re latively large and fairly flexible, the presence of PubChem 54353125 in the PubChem database indicates that a synthesis route exists to produce the molecule. And if the compound can be created, there is an opportunity to obtain the compound and test it for inhibitory activity against TcTS in vitro Based on the docking and MD results presented here, PubChem 54353125 has the potential to be a strong inhibitor of TcTS. 22.214.171.124 ZINC16247520 Rank 10 ZINC16247520 ( Figure 414), one of the better potential inhibitors of TcTS from the ZINC ChemBridge database, had a MM GBSA free energy of binding of 46.6 0.3 kcal/mol after 146 ns of unrestrained MD. The Glide XP binding energy ( 14.19
130 kcal/mol) for ZINC16247520 was the second best of any ligand docked during this study, only slightly behind DANA Result 034 ( 14.38 kcal/mol). This ligand was unique in its binding mode because it was the first potential strong inhibitor of TcTS analyzed that did not have a carboxylic acid interacting with the argi nine triad. Figure 414. The 2D structures of ZINC16247520 and a derivative. The structure of a) ZINC16247520 was used as input for AutoGrow, which created a new compound, b) ZINC16247520 Rank 10, using de novo design. Instea d, ZINC16247520 has oxygen atoms on adjacent carbon atoms one oxygen atom that is doublebonded to a carbon while the other oxygen is negatively charged and singlebonded to its carbon that interact with the arginine triad ( Figure 4 15). Placing the oxygen atoms on adjacent carbon atoms instead of a single carbon atom (such as with carboxylic acid) allows the oxygen atoms to be separated more spatially and able to interact more strongly with all three residues in the arginine triad instead of two, which was observed during MD of several potential inhibitors with carboxylic acids at this location. Furthermore, ZINC16247520 forms several other key interactions within the active site of TcTS, including a toluene group in the acceptor binding site between
131 the aromatic rings of Trp312 and Tyr119. Another strong TcTS ZINC16247520 interaction involves a hydrogen bond between the positively charged nitrogen on the ligand and the side chain of Asp96. Unfortunately, despite the promising protein ligand interactions the MM GBSA binding free energy of ZINC16247520 was still 7.2 kcal/mol worse than DANA, a known poor inhibitor of TcTS. Considering ZINC16247520 as a strong lead scaffold inhibitor, we used its structure as input for AutoGrow to desi gn new compounds by making key substitutions at positions on the ligand using the small molecule library provided with the AutoGrow source code. The top four unique compounds with the proper binding pose (corresponding to AutoGrow results Rank 1, 2, 5, and 10) were redocked using Glide SP and Glide XP to confirm the binding mode for each. After docking, each compound was subjected to the MD procedure as described in the Methods section, followed by MM GBSA free energy of binding calculations. Figure 415. Depiction of ZINC16247520 interacting with the arginine triad of TcTS.
132 Figure 416. The RMSD of TcTS and ZINC16247520 Rank 10 using the initial frame as reference. The RMSD was calculated using only the backbone atoms for TcTS, and all atoms for ZINC16247520 Rank 10. Figure 417. The MM GBSA binding free energy of ZINC16247520 Rank 10 bound to TcTS as a function of time for a 10ns MD simulation.
133 The best AutoGrow deri ved modification of ZINC16247520 was Rank 10 ( Figure 4 14), which had Glide XP and MM GBSA binding free energies of 7.71 kcal/mol and 62.0 0.2 kcal/mol, respectively. The RMSD for both the enzyme and ligand remain under 2.0 over the duration of the 10 ns simulation ( Figure 416), suggesting a certain stability of the ligand in the active site. Furthermore, the MM GBSA free energy of binding sharply increases in the first nanosecond, followed by an os cillation around the average for the final 9 ns of unrestrained MD ( Figure 417). The structural modifications by AutoGrow actually change the binding mode of the ligand slightly. In particular, the new sulfate group pushes the li gand oxygen atoms that were interacting with the arginine triad ( Figure 415) away from Arg314 and Arg245, leaving the sulfate to interact with Arg314 and Arg245 while one of the two oxygen atoms remains interacting with Arg35. The other significant difference between ZINC16247520 and the Rank 10 AutoGrow derivative is the addition of a terminal NH2 that forms multiple hydrogen bonds deep within the TcTS active site with the side chains of Arg93, Asp59, and the backbone of Ser118 ( Figure 418). The new binding mode and modifications made by LigBuilder actually make ZINC16247520 Rank 10 a match for both the holo and apo epharmacophore models described in Chapter 3, although the aromatic ring does not overl ap perfectly with the aromatic pharmacophore feature of both models. ZINC16247520 Rank 10 also matches three of the four features of the 1S0I e pharmacophore, while the only feature with no corresponding match on this inhibitor is R20 because there is no aromatic ring in the acceptor binding site between Tyr119 and Trp312.
134 Figure 418. The hydrogen bonding network between ZINC16247520 Rank10 and TcTS residues Arg93, Asp59, and Ser118. This hydrogen bonding network is not present in ZINC16247520 because the terminal amino group was an addition by AutoGrow to the original compound. Thus, the favorable proteinligand interactions formed help explain the improved binding free energy of the Rank 10 compound compared to ZINC16247520. Based on our calculations, ZINC16247520 Rank 10 appears to be a stronger TcTS inhibitor than ZINC16247520 with a 15.4 kcal/mol improvement in the MM GBSA free energy of binding. The addition of sulfate and a terminal amino group to the ligand appear to create new favorable proteinligand interactions not found in the original compound. However, as with all de novo designed inhibitors, it is unclear if this derivative can be easily synthesized from the scaffold compound. Furthermore, questions exist regarding the efficacy of such a large, flexible inhibitor when tested in vivo but if these issues can be addressed ZINC16247520 has the potential to be a strong inhibitor of TcTS.
135 126.96.36.199 Sialic Acid Rank 1 AutoGrow was used to modify and optimize the structure of siali c acid within the TcTS active site using the large fragment library released with the AutoGrow source code. The best sialic acid derivative according to AutoGrow, which utilizes the AutoDock Vina212 scoring function, was s imulated bound to TcTS for 10 ns followed by MM GBSA calculations. This derivative, Sialic Acid Rank 1, had a MM GBSA free energy of binding of 61.5 0.3 kcal/mol, which corresponds to the fifth best inhibitor discovered in this study ( Table 42 ). From the 2D structure shown in Table 42 it is clear that AutoGrow significantly increases the size of the ligand, starting from sialic acid with 37 atoms and ending with Sialic Acid Rank 1 containing 91 atoms Despite the increase in potential ligand flexibility, the RMSD of Sialic Acid Rank 1 remains low (below 1.0 ) throughout the simulation ( Figure 419). In fact, as Figure 420 shows, Sialic Acid Rank 1 remains fairly rigid in its bound state within the TcTS active site. Figure 419. The RMSD of TcTS and Sialic Acid Rank 1 using the initial frame as reference. The RMSD was calculated using only the backbone atoms for TcTS, an d all atoms for Sialic Acid Rank 1.
136 Figure 420. Overlay of 250 evenly spaced conformations of Sialic Acid Rank 10 bound to TcTS during a 10ns MD simulations. Sialic Acid Rank 1 is involved in many protein ligand interactions with TcTS, although no hydrogen bond between the ligand and Arg53 prevents this molecule from matching any of the epharmacophore models in Chapter 3. Since the core of the compound is sialic acid, the molecule maintains those same interactions, including hydrogen bonds with the arginine triad and Asp96. However, the modifications by AutoGrow created additional interactions not present in the original compound. Specifically, new hydrogen bonds were formed with Arg93 (33% of the simulation), Trp120 (backbone 80%, side chain 29%), and Glu362 (100%). Additionally, several new stacking interactions arose because of the AutoGrow substitutions, including ring stacking between the ligand and several TcTS residues Tyr113, Tyr119, Trp120, and Trp312. Although Tyr119 and Trp312 are residues previously shown to be important for substrate binding, the stacking interactions with Tyr113 and Trp120 represent
137 previously unidentified interactions with the potential to be important for the future design of novel strong TcTS inhibitors. The binding mode of Sialic Acid Rank 1 is well defined, the ligand is stable within the TcTS active site, and the binding free energy is better than sialyl lactose, but there are still several problems with this compound as a drug molecule. Fi rst, the size and flexibility of Sialic Acid Rank 1 would make its use as a drug molecule difficult because drugs are preferably smaller and rigid. Second, synthesizing this compound is likely very difficult if not impossible. Third, the core scaffold of t his compound is sialic acid, which would likely be cleaved by the active enzyme, preventing the desired inhibition. Finally, the free energy of binding calculations performed in this study neglect any entropic contributions to binding based partly on the assumption that the unbound conformation of the ligand is similar to the bound conformation without many other available microstates. Unfortunately, this compound would be substantially more flexible in solution compared to the bound state. Thus, it is hypothesized that if entropic contributions to binding were included in these calculations, the entropic cost for this ligand would likely be significantly more compared to the other ligands, which would lower its ranking among the potential inhibitors analyzed in this study. Despite its deficiencies as a legitimate drug candidate, Sialic Acid Rank 1 displays many potential proteinligand interactions that could be useful for the rational drug design of other compounds as inhibitors of TcTS. 188.8.131.52 ZINC04939766 ZINC04 939766 was the best unmodified TcTS inhibitor from a ZINC database (ZINC Leads) and sixth best overall ( Table 42 ) with a MMGBSA free energy of binding of 58.5 0.2 kcal/mol from 10 ns of unrestrained MD. This is the smallest p otential
138 inhibitor analyzed so far with only 40 atoms, but it was still able to have a binding free energy better than the substrate of TcTS (sialyl lactose, 54.8 kcal/mol). ZINC04939766 was chosen for analysis with MD and MM GBSA calculations because it scored well during an initial screening of the ZINC Leads database with Glide HTVS. The docked conformation of ZINC04939766 would match the required features of the holo epharmacophore model described in Chapter 3, but would not match the 1S0I or apo mode ls. During the MD the ligand did not maintain its original binding mode derived from Glide, but instead slightly changed conformations around 2.5 ns into the simulation, as shown in the RMSD plot ( Figure 421). This change in binding mode also simultaneously increased the MM GBSA free energy of binding by 15 kcal/mol ( Figure 4 22). Visualization of the MD trajectory shows that the increase in binding free energy corresponds with a loss of proteinligand in teractions between ZINC04939766 and the arginine triad of TcTS, especially with Arg35 and Ar314 ( Figure 423 ). The hydrogen bond distances between the ZINC04939766 carboxylic acid and the closest hydrogen atoms on the arginine res idues increase from by over 2 for Arg35 and Arg314. However, the hydrogen bond distance did not change significantly for Arg245, as ZINC04939766 remains hydrogen bonded to it for 48% of the simulation, compared to just 18% and 12% for Arg314 and Arg35, r espectively. The loss of this important hydrogen bonding network between the ligand and receptor is the main culprit in the loss of 15 kcal/mol of binding free energy after the conformational change occurs around 2.5 ns.
1 39 Figure 421. The RMSD of TcTS and ZINC04939766 using the initial frame as reference. The RMSD was calculated using only the backbone atoms for TcTS, and all atoms for ZINC04939766. Figure 422. The MM GBSA binding free energy of ZI NC04939766 bound to TcTS as a function of time for a 10ns MD simulation.
140 Figure 423. The binding mode of ZINC04939766 with the arginine triad of TcTS. The conformations shown represent the distances between the carboxylic ac id of ZINC04939766 and the closest hydrogen atoms for the arginine residues at a) 0 ns and b) 2.5 ns, after the ligand undergoes a conformational change in the active site. The change in binding mode at 2.5 ns affected ligand interactions with the arginine triad, but the other proteinligand interactions remained relatively stable for the stacking interactions that occur between the naphthalene group of ZINC04939766 and both Tyr113 and Tyr119, which was also observed with Sialic Acid Rank 1. However, the prominent polar interactions occur between the ligand and the Asp96/Glu230 motif ( Figure 424 ). In fact, the four most persistent TcTS ZINC04939766 hydrogen bonds occurred in this area of the bound complex at the bottom of the TcTS active site. The formal positive charge on the ligand nitrogen atom is perfectly positioned to make sustained, favorable interactions with the negative charges of Asp96 and Glu230. The strength of thes e hydrogen bonds suggests they play a key role in making ZINC04939766 a strong potential inhibitor of TcTS.
141 Figure 424. The hydrogen bonding network between ZINC04939766 and the Asp96/Glu230 motif on TcTS. Unlike many of the potential inhibitors previously proposed, ZINC04939766 is commercially available and could be easily obtained for in vitro kinetic assays. Computationally, the original docked pose of ZINC04939766 had a MM GBSA free energy of binding near between 65 and 75 kcal/mol, but a shift in the binding mode of the ligand around 2.5 ns into the simulations resulted in a sharp increase to near 55 kcal/mol. ZINC04939766 consistently forms favorable interactions with residues stacking Tyr113 and Tyr119 a nd hydrogen bonding Asp96 and Glu230. However, the ligand is incapable of maintaining strong hydrogen bonds with the arginine triad, a fundamental region for enzyme inhibition because of its importance in substrate binding. Possible structural modifications should be addressed to improve the interactions of ZINC04939766 with the arginine triad, such as lengthening the connection between the carboxylic acid and the rest of the ligand. Ideally, any structural
142 changes would allow the carboxylic acid to hydrogen bond with the arginine triad without breaking the strong hydrogen bonding network between ZINC04939766 and the Asp96/Glu230 motif. 184.108.40.206 Zanamivir phosphonate Zanamivir phosphonate, proposed as a potent inhibitor of the neuraminidases for both avian and human influenza viruses by Shie et al .,213 was considered here for its potential to inhibit TcTS given the high structure and sequence similarity between neuraminidases and trans sialidase. Experimental details of the protonation states of the phosphonate and guanidinium groups were not discussed in the literature, thus multiple protonation states of Zanamivir phosphonate were created using Avogadro and those compounds were docked to the holo grid of TcTS using Glide SP and Glide XP. The various states all had similar Glide XP binding free energies ( 11.0 0.2 kcal/mol) so MD and MM G BSA calculations were ran on a protonated and deprotonated state. Furthermore, the deprotonated state resulted in three different relevant binding modes from Glide XP; so all three of these modes were also simulated. Zanamivir phosphonate deprotonated Rank 62 (ZP62) and Zanamivir phosphonate protonated Rank 1 (ZP1), the best two of those four structures, had MM GBSA free energies of binding better than sialyl lactose. Specifically, the MM GBSA binding free energy of ZP62 was 57.9 0.3 kcal/mol, while the free energy of binding for ZP1 was 55.8 0.3 kcal/mol ( Table 42 ). The binding modes for these Zanamivir phosphonate compounds would not have been discovered by screening the epharmacophore models in Chapter 3 because the aroma tic ring is not positioned properly to match the aromatic feature of the pharmacophore hypotheses.
143 Figure 425. The RMSD plots from TcTS ZP1 and TcTS ZP62 using the initial frame as reference. The RMSD was calculated using onl y the backbone atoms for the TcTS structures, and all atoms for the two ligands. Figure 426. The MM GBSA binding free energy of ZP1 (blue) and ZP62 (purple) bound to TcTS as a function of time for a 10ns MD simulation.
144 The R MSD plots showing the stability of the TcTS ZP1 and TcTS ZP62 structures during MD are shown in Figure 425 Overall, neither complex deviates too far from the initial docked structure since all RMSD values remain below 2.0 The enzyme structures appear to relax successfully into a local minimum and remain there for at least the last 5 ns. The ligands, however, both undergo a clear conformational change (or two) during the simulation. For ZP1, the RMSD increase from 0.6 to 1.3 at 3.2 ns corresponds to a rotation of the terminal hydroxide on the glycerol chain that results in the formation of a hydrogen bond between the hydroxyl group and the side chain of Arg93. This change in RMSD at 3.2 ns also coincides with an improvement in the free energy of binding for TcTS ZP1 resulting from this new hydrogen bond with Arg93 ( Figure 426). ZP62 appears to undergo two different conformational changes during the MD simulation one at 2.0 ns and the other at 7.8 ns. The increase in RMSD for ZP62 at 2.0 ns appears to correspond to a rotation of the terminal hydroxyl on the glycerol chain and a rotation of the guanidinium group on the ligand. This rotation places the hydroxyl group in position to hydrogen bond with the side chain of Arg53, but it just replaces one of the other hydroxyl groups that was interacting with Arg53, resulting in a nearly zero net change in the binding free energy ( Figure 426). Likewise, the rotation of the guanidinium group on ZP62 breaks three hydrogen bonds, but reforms three different hydrogen bonds similar in strength and atomic makeup, so there is still little overall change in the MM GBSA binding free energy. The sharp increase (and subsequent drop) in binding energy of TcTS ZP62 at 2.0 ns is likely a result of the ligand reorienting itself before the new hydrogen bonds are formed. The RMSD change in ZP62 from 1.2
145 to 1.8 at 7.8 ns corresponding to another rotation of the flexible glycerol chain also incurred no n et change in the binding free energy because the number of hydrogen bonds broken and formed were again the same. Figure 427. The hydrogen bonding network between the phosphonate group of ZP62 and the arginine triad of TcTS. T he percentages represent the fraction of time the hydrogen bond was present during a 10 ns MD simulation. The substitution of the carboxylic acid for a phosphonate on Zanamivir, which is unable to inhibit TcTS at 1 mM concentration,214 is designed to improve the proteinligand interactions with the arginine triad. The MD simulations suggest this phosphonatearginine triad interaction is strongly favorable, with a stable hydrogen bonding network as shown for TcTS ZP62 in Figure 427. The phosphonate oxygen atoms make seven significant hydrogen bonds with the arginine triad, which is more than any other potential ligand analyzed in this study. Given the arginine triads role in
146 substrate binding, it is likely important for an inhibitor to make strong interactions with this motif, as the substitution of a phosphonate group on Zanamivir would suggest here. Although simulations were performed on both the protonated and deprotonated guanidinium group of Zanamivir phosphonate, it is still unclear if a preference exists for one of the protonation states because both states obtained similar MM GBSA free energies of binding. For the deprotonated state, Glide XP found three dominant binding modes that were all simulated. The MM GBSA results suggest that the conformation ranked 62nd by Glide XP was actually the better inhibitor of TcTS, which shows the importance of performing MD and MM GBSA calculations to rerank potential inhibitors. Based on visual inspection prior to MD, conformation 62 was anticipated to give more reliable results because the binding mode was similar to other related compounds, such as DANA. Zanamivir phosphonate is a more potent inhibitor of neuraminidase than Zanamivir, and based on the MM GBSA res ults in this study Zanamivir phosphonate is expected to be an improved inhibitor of TcTS. Zanamivir does not inhibit TcTS, while Zanamivir phosphonate is calculated to have a stronger binding affinity than the natural enzyme substrate, sialyl lactose. The improved affinity for TcTS would be a direct result of the substitution of a carboxylic acid for a phosphonate group that interacts with the arginine triad. A synthetic route to make Zanamivir phosphonate has already been discussed in the literature,213 thus it could be synthesized and tested in vitro However, further structural modifications should be considered to make the potential inhibitor more effective, including the addition of an aromatic group designed to interact with Tyr119 and Trp312 in the acceptor binding site.
147 4.4.2 De novo Design Candidate Combining ZINC Fragments As m entioned in Chapter 3, we docked the Clean Fragments library of ZINC to the holo grid of TcTS using Glide XP. We selected the best nonoverlapping fragments that interacted with four distinct motifs of the TcTS active sitearginine triad, Asp96/Glu230, acc eptor binding site between Tyr119 and Trp312, and the hydrophobic pocket flanked by Val95 and Leu176. These four sites were all determined to be important for inhibitor binding by the epharmacophores described in Chapter 3. The four best fragments were combined together using LigBuilders link algorithm to make a single molecule we will call Super fragment. The Super fragment contained 60 total atoms and had a free energy of binding of 39.4 0.3 kcal/mol after 72 ns of unrestrained MD at constant temperature (300 K) and pressure (1 atm). The RMSD of both TcTS and the ligand are shown in Figure 4 28. The Super fragment seems stable (RMSD < 1.5 ) for nearly 40 ns, but then ligand clearly changes conformations illustrated by the elevated RMSD. Although more subtle, the increase in RMSD of the Super fragment also coincides with a noticeable raise in RMSD of TcTS by 0.5 Shortly after the noticeable RMSD change in the ligand, the free energy of binding also begins to increase and is about 40 kcal/mol worse at 72 ns than it was at 40 ns ( Figure 429). The MMGBSA binding energy begins the simulation around 90 kcal/mol, but progressively worsens to a final value of +8.7 kcal/mol. The positive binding energ y at the end of the simulation suggests the ligand likely dissociated from the enzyme.
148 Figure 428. The RMSD of TcTS and the Super fragment using the initial frame as reference. The RMSD was calculated using only the backbone atoms for TcTS, and all atoms for the Super fragment. Figure 429. The MM GBSA binding free energy of the Super fragment bound to TcTS as a function of time for a 72ns MD simulation.
149 Figure 430. The position of the Super fragment relative to the TcTS active site at 0 ns (red) and 72 ns (blue) showing the dissociation of the ligand during MD. The trajectory of TcTS and the Super fragment confirmed that the ligand dissociated from the enzyme. As Figure 430 shows, the Super fragment begins the simulation deep in the active site, but after 72 ns of unrestrained MD the ligand is positioned at the edge of the active site forming only minimal contact with TcTS. Dissociation beg ins around 48 ns, which corresponds to the sharp increase in binding free energy in Figure 429. Overall, the strong initial MM GBSA binding free energy suggests the Super fragment has the potential to inhibit TcTS, but the lack o f stability in the active site is concerning, and likely means that structural modifications of the Super fragment would be necessary to make it a viable drug candidate against Chagas disease. 4.5 Concluding Remarks In this study, we have used a combination o f molecular docking, MD, and MM GBSA binding free energy calculations to discover new potential inhibitor of TcTS.
150 Specifically, we docked over 35 million structures and ran MD and calculated binding free energies for over 100 different compounds. Four of the best five drug possibilities DANA Result 034, Oseltamivir Result 70, ZINC16247520 Rank 10, and Sialic Acid Rank 1 were designed using de novo software. Although these compounds appear to have very strong inhibitory activity of TcTS, there are serious c oncerns about their viability as drug treatments because they are large, potentially difficult to synthesize, and flexible. The best candidate for experimental testing is PubChem 54353125, which had a free energy of binding of 72.1 0.2 kcal/mol, because it could be synthesized or purchased. However, PubChem 54353125 is still large and flexible and may have difficulty working as an effective inhibitor of TcTS in vivo Finally, the sixth best potential inhibitor ZINC04939766had a MM GBSA binding free ener gy of 58.1 0.2 kcal/mol (nearly 4 kcal/mol better than the substrate, sialyl lactose) and is relatively small compared to some of the other top candidates and is also available from commercial vendors. Unfortunately, the calculated binding free energy of ZINC04939766 is significantly worse, decreasing the chance of it being a strong inhibitor of TcTS in vitro Overall, several molecules have been proposed ( Table 42 ) as novel inhibitors of TcTS that have the potential to develop into drugs in the treatment of Chagas disease.
151 CHAPTER 5 MMPBSA.py : AN EFFICIENT PROGRAM FOR ENDSTATE FREE ENERGY CALCULATIONS 5.1 Preliminaries Free energy calculations have proven useful for a number of topics in computational biology, such as drug design and pr otein structure determination.175,215 Several methods are available to calculate free energies, such as Free Energy Perturbation,216 Replica Exchange Free Energy Perturbation,217 and Thermodynamic Integration.218 These methods, though t heoretically rigorous, are computationally demanding and become prohibitively expensive as system size increases. These methods converge poorly for complex systems, so the reaction coordinate is often divided into intermediate states. Endstate free energy methods, on the other hand, reduce computational cost by eliminating the need for simulating these intermediate states. Modeling the solvent implicitly further reduces the computational cost by eliminating the noise caused by explicit solvent molecules. A s a result, endstate methods have been used extensively in computational studies.219 221 We will not discuss here the advantages and disadvantages of applying endstate methods for a recent review, see Refs. [ 175 ] and [ 222 ]. This chapter will focus on the implementation of endstate methods in MMPBSA.py, a program released with the open source AmberTools package.203 MMPBSA.py has already been applied to several systems, including calculating the binding free energies of DNA binding molecules223 and HIV protease inhibitors.224 Portions of this chapter are excerpted and adapted with permission from Miller, B. R.; McGee, T. D.; Swails, J. M.; Homeyer, N.; Gohlke, H.; Roitberg, A. E. J. Chem. Theory Comput. 2012, 8, 3314 3321. Copyrigh t 2012 American Chemical Society.
152 5.2 Theory and Methods Before describing the capabilities of MMPBSA.py we will briefly review the theory and calculations of the endstate methods that MMPBSA.py is capable of performing. Figure 51 Ther modynamic cycles for common free energy calculations. Example cycles are shown for a) stability free energy calculations for conformation A and B, and b) binding free energy calculations for a proteinligand complex. Solvated systems are shown in blue boxes, while systems in the gas phase are in white boxes. Free energies that are directly calculated are shown in black, while the free energy of interest is shown in red.
153 5.2.1 Stability and Binding Free Energy Calculations End state calculations are frequently used for two types of analyses calculating the relative stability of multiple conformations of a system and calculating the binding free energy in a noncovalently bound, receptor ligand complex,175 shown as thermodynam ic cycles in Figure 51 Stability calculations compare the free energies of multiple conformations to determine their relative stability. If we consider the process of a biomolecule changing conformations from state A to state B, then the free energy associated with that conformational change is calculated according to Equation 51. GAB,solvatedGB,solvatedGA,solvated (5 1 ) Similarly, binding free energies are calculated by subtracting the free energies of the unbound r eceptor and ligand from the free energy of the bound complex, shown in Equation 52. Gbinding,solvatedGcomplex,solvatedGreceptor,solvatedGligand,solvated (5 2 ) The free energy change associated with each term on the right hand side of Equation 51 and 52 is estimated according to Equation 53. GsolvatedEgasGsolvationTSsolute (5 3 ) In Equation 5Gsolvation represents a true free energy, since the solvent Gsolvated in Equation 51 and 52 for consistency, but it is important to note that at this point, we only have one structure for the solute. The gas phase energies ( Egas) are often the molecular mechanical (MM) energies from the force field, while the solvation free Gsolvation) are calculated using an implicit solvent model, and the entropi c contribution ( S ) is estimated using known approximations. The free energy of solvation
154 is further decomposed as the sum of electrostatic and nonpolar contributions. Several implicit solvent models are available to calculate the solvation free energies, i ncluding Generalized Born (GB),175 Poisson Boltzmann (PB),175 and Reference Interaction Site Model (RISM).176 The energies described in the equations above are single point energies of the system. However, in practice, endstate calculations estimate these energies according to averages from an ensemble of representative structures. For example, expressing Equation 53 in terms of averages yields Equation 54, Gsolvated Egas Gsolvation T Ssolute 1 N Ei gasi 1 N1 N Gi solvation i 1 N T N Si solute i 1 N (5 4 ) where i is the index of a particular frame and N is the total number of frames analyzed. There are two approaches to generating the necessary ensembles for the bound and unbound state of binding energy calculations all ensembles can be extracted from a single molecular dynamics (MD) or Monte Carlo (MC) trajectory of the bound complex, or trajectories can be generated for each state using separate simulations.2 25 These approaches are called the single trajectory protocol (STP) and multiple trajectory protocol (MTP), respectively, and each approach has distinct advantages and disadvantages. The STP is less computationally expensive than the MTP because only a si ngle trajectory is required to generate all three ensembles. Furthermore, the internal potential terms (e.g., bonds, angles, and dihedrals) cancel exactly in the STP because the conformations in the bound and unbound ensembles are the same, leading to lower fluctuations and easier convergence in the binding free energy. The STP is appropriate if the receptor and ligand ensembles are comparable in the bound and unbound states.
155 However, the conformations populating the unbound ensembles typically adopt strain ed configurations when extracted from the bound state ensemble, thereby over stabilizing the binding compared to the MTP. 5.2.2 Free Energy Decomposition Amber203 provides several schemes to decompose calculated free energies into specific residue contributions using either the GB or PB implicit solvent models,226 following the work of Gohlke et al.219,226 Interactions can be decomposed for each residue by including only those interactions in which one of the residue's atoms is involveda scheme called per residue decomposition. Alternatively, interactions can be decomposed by specific residue pairs by including only those interactions in which one atom from each of the analyzed residues is participating a scheme called pairwise decomposition. These decomposition schemes can provide useful insights into important interactions in free energy calculations.219,226 It is important to note, though, that s olvation free energies using GB and PB are not strictly pairwise decomposable since the dielectric boundary defined between the protein and the bulk solvent is inherently nonlocal and depends on the arrangement of all atoms in space. Thus, care must be taken when interpreting free energy decomposition results. An alternative way of decomposing free energies is to introduce specific mutations in the protein sequence and analyze how binding free energies or stabilities are affected.196 Alanine scanning, a technique in which an amino acid in the system is mutated to alanine, can highlight the importance of the electr ostatic and steric nature of the original sidechain.227 Assuming the mutation will have a negligible effect on protein conformation, we can incorporate the mutation directly into each member of the original
156 ensemble. This avoids the need to perform an additional MD or MC simulation to generate an ensemble for the mutant. 5.2.3 Entropy Calculations The implicit solvent models used to calculate relative stability and binding free energies in end state calculations often neglect some contributions to the solute entropy. If we assume that biological systems obey a rigid rotor model, we can calculate the translational and rotational entropies using standard statistical mechanical formulae,228 and we can approximate the vibrational entropy contribution using one of two methods. First, the vibrational frequencies of normal modes can be calculated at various local minima of the potential energy surfacea met hod we will refer to as nmode.228 Alternatively, the eigenvalues of the mass weighted covariance matrix constructed from every member of the ensemble can be approximated as frequencies of global, orthogonal moti ons a technique we will refer to as the quasi harmonic approximation.229 Using either the nmode or quasi harmonic frequenci es, we can sum the vibrational entropies of each mode calculated from standard formulae.228 Nmode calculations are typically computationally demanding for large systems because they require minimizing every fra me, building the Hessian matrix, and diagonalizing it to obtain the vibrational frequencies (eigenvalues). Because of the Hessian diagonalization, normal mode calculations scale as roughly (3N )3, where N is the number of atoms in the system. While the quas i harmonic approach is less computationally expensive, a large number of frames, or members of the ensemble, are typically needed to extrapolate the asymptotic limit of the total entropy for each ensemble, which increases the computational cost of the orig inal simulation.230
157 Relative free energy calculations between related systems often ass ume the solute entropy will be the same for each system being compared, thereby removing the need to explicitly calculate them.196 5.3 Results and Discussion Here we will demonstrate many of the calculations that MMPBSA.py can perform. First we will describe the general workflow for using MMPBSA.py to perform endstate free energy calculations, followed by specific ex amples. 5.3.1 General Workflow MMPBSA.py is a program written in Python and nab231 that streamlines the procedure of preparing and calculating free energies for an ensemble generated by MD or MC simulations whose general workflow is shown in Figure 52 MMPBSA.py builds upon original ideas by Irina Massova and Peter Kollman as described in Ref. [ 196 ]. The process of calculating binding free energies can be a tedious procedure that MMPBSA.py aims to shorten and simplify. Python is a useful programming language for performing tasks that are not numerically intensive, and because it is available on virtually every platform, Python programs are highly portable. Nucleic Acid Builder231 ( nab), a molecule based programming language included with AmberTools, contains functionality pertinent to building, manipulating, and performing energy calculations on biological systems, such as proteins and nucleic acids. End state calculations often require multiple topology files that contain the parameters corresponding to the force field. We recommend users run simulations using explicit solvent, which would require the user to provide both solvated and unsolvated topology files to MMPBSA.py It is necessary that all topology files have a
158 consistent set of parameters, especially for binding free energy calculations. MMPBSA.py checks the user's topology files prior to binding free energy calculations to prevent erroneous res ults due to inconsistencies that may not be immediately obvious. The Python utility anteMMPBSA.py (released alongside MMPBSA.py ) allows a user to easily create topology files with a consistent set of parameters, including changing the intrinsic implicit s olvent radius set to fit the desired solvent model. Figure 52 General workflow for performing endstate calculations with MMPBSA.py LEaP is a program in Amber used to create topology files for dynamics. The workflow shown i n step 3 is the series of steps that MMPBSA.py automates. 'Dry' topologies and ensembles are systems without explicit solvent that are subsequently treated using an implicit solvent model. 'External programs' refers to the executables that perform the ener gy calculations (e.g. sander ).
159 The use of MMPBSA.py is similar to that of Amber's MD engines sander and pmemd The command line flags common to both MMPBSA.py and the MD engines are identical, and input files contain input records separated with similar, F ortran style namelists, indicated with an ampersand ( &). The MMPBSA.py input file contains a &general namelist for variables that control general behavior. For example, variables that control the subset of frames analyzed ( startframe, endframe, and interval) and the amount of information printed in the output file ( verbose) are specified here. An example of this section is shown below. General MMPBSA.py input file &general startframe=1, endframe=100, interval=2, keep_files=0, verbose=1, strip_mask=:WAT:Cl-:Na+, / Users should avoid analyzing correlated structures from their simulations. Because the autocorrelation time of the free energy is system dependent, the autocorrelation function of the free energy can be calculated to determine the ideal frequenc y to extract snapshots to avoid analyzing correlated structures (e.g. 5 ps).220 The keep_files variable controls which temporary files remain after the calculations conclude successfully allowing users to optionally inspect the intermediate steps or regenerate the final output file using a different verbose value. By default, MMPBSA.py removes all known i ons from the original trajectory file via the strip_mask variable. This can be modified in the input file, however care must be taken if the user chooses to
160 retain particular ions or water molecules during the free energy calculations because the implicit solvent models utilized by MMPBSA.py are not parameterized to account for these species. Therefore, it is not recommended to include water or ions during the calculations. The following sections introduce the other namelists allowed in the MMPBSA.py input file and their functionality. 5.3.2 PoissonBoltzmann: MM PBSA Solvation free energies can be calculated using the PoissonBoltzmann (PB) implicit solvent method with a nonpolar solvation term based on the solvent accessible surface area (SASA)221 by including the &pb namelist in the MMPBSA.py input file, demonstrated below. MMPBSA.py input file for running PB &general startframe=1, endframe=100, interval=2, / &pb istrng=0.1, exdi=80, indi=1.0, inp=2, cavity_surften=0.0378, cavity_offset=-0.5692, fillratio=4, scale=2.0, linit=1000, prbrad=1.4, radiopt=1, / Users can specify external thermodynamic variables based on experimental conditions to control the calculation, such as the ionic strength ( istrng) and the ex ternal and internal dielectric constants ( exdi and indi, respectively). Variables
161 specific to the PB calculation are shown on the final three lines of the &pb section of the example input file above. The default method for calculating nonpolar solvation f ree energies ( inp) and its associated variables ( cavity_surften and cavity_offset) are recommended based on a previous optimization of these parameters.232 The default fillratio value of 4.0 is recommended to prevent errors for small molecules lying outside the focusing finitedifference grid that occurs when fillratio is set too low. Scale is the number of PB grid points per angstrom, and may be adjusted to obtain courser or finer resolution, although increasing the resolution comes at the cost of computational time. The default value of 2.0 for the scale control variable was determined as a compromise between speed and accuracy. The suggested value of 1000 for the number of iterations to perform of the linear PB equation ( linit) is usually sufficient to reach convergence. A 1.4 probe radius ( prbrad) is recommended for simulations performed with water as the solvent to model the size of a typical water molecule. The optimized radii set ( radiopt) described previously233 shows improved accuracy over other sets, and thus is recommended for most users. The PB solvation free energy can be calculated using two possible programs. The default solver is pbsa ,234 whose routines can be called from either a nab program (default) or sander although users can access the thirdparty Adaptive Poisson Boltzmann Solver ( apbs) solver235 by linking apbs with sander using iAPBS.236 5.3.3 Generalized Born: MM GBSA The Generalized Born (GB) implicit solvent method with a SASA term can be used to calculate the solvation free energy219 by including the &gb namelist in the MMPBSA.py input file, demonstrated below.
162 MMPBSA.py input file for running GB &general startframe=10, interval=2, endframe=2010, / &gb igb=5, saltcon=0.1, / The GB calculation has its own set of control variables, such as salt concentration ( saltcon) and the specific GB model ( igb). Amber provides five different GB models205,237 239 that can be used with MMPBSA.py depending on user preference. Similar to PB, the GB solvation free energies can be solved using either a nab program (defau lt) or sander For analysis of proteins, we recommend setting igb to 8 ( sander only) based on recent results showing an increased stability for salt bridges with this GB model.239 However, a value of 2 for igb is preferred for many users analyzing proteins without access to sander .188 MMPBSA.py can use sander to treat part of the system in a MM GBSA calculation quantum mechanically using a hybrid quantum mechanical/molecular mechanical (QM/MM) Hamiltonian240 with the ifqnt control variable. The residues treated quantum mechanically ( qm_residues) are defined in the &gb namelist of the MMPBSA.py input file, as demonstrated below. MMPBSA.py input file for running QM/MMGBSA &general startframe=7, interval=9, endframe=2011,
163 / &gb igb=5, saltcon=0.1, ifqnt=1, qmcharge_com=0, qmcharge_lig=0, qm_residues=241, qm_theory='PM3', / The charge of the region treated quantum mechanically must be specified for both the complex ( qmcharge_com) and ligand ( qmcharge_lig). Several semi empirical Hamiltonians are available in MMPBSA.py for the QM region,241 including AM1,242 MNDO,243 and PM3.244 5.3.4 Reference Interaction Site Model: MM/3D RISM MMPBSA.py can also use the Reference Interaction Site Model (RISM)176 to calculate solvation free energies through a nab program. Specifically, threedimensional RISM (3D RISM) determines the equilibrium distribution of solvent around the solute to calculate ther modynamic properties. The MM/3D RISM method has its own namelist in the MMPBSA.py input file with its own control variables. An example is shown below. MMPBSA.py input file for running MM/3D-RISM &general endframe=200, / &rism closure=kh, polardecomp=1, /
164 Three different closure relations ( closure) are available to approximate the RISM integrals: the hypernettedchain approximation (HNC), KovalenkoHirata (KH), and the partial series expansion of order n (PSE n ).245 While 3DR ISM can directly decompose the solvation free energy into the polar and nonpolar (cavity and dispersion) terms ( polardecomp), this decomposition nearly doubles the computational cost of the calculation, and 3D RISM calculations already take substantially longer than PB or GB. 5.3.5 Free Energy Decomposition Free energy decomposition can provide information about the local interactions of a system in addition to global free energies.219 MMPBSA.py can calculate per residue and pairwise energy decompositions with sander Decomposition must be combined with GB and/or PB in the MMPBSA.py input f ile, and control variables for both per residue and pairwise decomposition schemes are specified in the &decomp namelist, as shown below. MMPBSA.py input file for running per-residue decomp &general startframe=6, interval=26, endframe=2007, / &gb igb=5, saltcon=0.1, / &decomp idecomp=1, print_res='1-10,200-241', /
165 Per residue decomposition ( idecomp options 1 and 2) estimates the contribution of each residue to the total free energy. Pairwise energy decomposition ( idecomp options 3 and 4) estimates the interaction energy of specific residue pairs. By default, MMPBSA.py will print the decomposition results for all residues of the system, but a subset can be specified using print_res to reduce the amount of data printed. We recommend new users perform decomposition analysis with GB solvent models before attempting to use the PB models because of the difficulty and timeconsuming nature of the PB decomposition analysis. Furthermore, we reemphasize that decomposition analysis is not pairwise decomposable si nce the dielectric boundary defined between the protein and the bulk solvent is inherently nonlocal and depends on the arrangement of all atoms in space. 5.3.6 Alanine Scanning Alanine scanning is an alternative to decomposition analysis that involves mutating a single amino acid in the system to alanine. Alanine scanning is enabled by including the &alanine_scanning namelist in the MMPBSA.py input file and must be specified with at least one type of calculation to be performed on the mutated structure. An example of alanine scanning using GB is shown below. MMPBSA.py input file for running alanine scanning &general startframe=1, endframe=200, interval=5, / &gb igb=5, saltcon=0.1,
166 / &alanine_scanning mutant_only=1, / The free energy can be calculated for the mutant trajectory only ( mutant_only=1) or both the mutant and original trajectories ( mutant_only=0). When alanine scanning is enabled, MMPBSA.py determines the mutated residue by comparing the original and mutant topology files. Then, MMPBSA.py creates a mutant trajectory by modifying the original atomic coordinates of the mutated residue to the atomic positions of alanine, discarding extraneous atoms and adjusting bond lengths to their equilibrium values. Once the mutated trajectory is created, the normal workflow is resumed. 5.3.7 Solute Entropy Including solute entropy with the energies calculated in the sections above is necessary to obtain true free energies. MMPBSA.py assumes a rigid rotor model and calculates the rotational and translational solute entropies according to standard formulae. Vibrational entropies are calculated from the frequencies of global motions, which are obtained via normal mode analysis (nmode) or quasi harmonic analysis. 220.127.116.11 Normal Mode Analysis In nmode, MMPBSA.py uses a nab program to minimize frames to a local minimum, then constructs and diagonalizes the Hessian matrix to obtain the vibrational frequencies. Control variables for nmode calculations are specified in the &nmode namelist of the MMPBSA.py input file, as demonstrated below. MMPBSA.py input file for running normal mode analysis
167 &general startframe=1, endframe=200, interval=5, / &nmode maxcyc=10000, drms=0.001, nmode_igb=1, nmode_istrng=0.1, / Each frame is minimized to a local minimum using the xmin minimizer in nab pri or to building the Hessian matrix. Details of the minimization, such as the number of minimization cycles ( maxcyc) and the convergence criteria ( drms), can be adjusted in the input file, however the default values are sufficient for most systems that are able to be minimized. The normal modes can be calculated in the gas phase or in implicit solvent using a GB model ( nmode_igb). Nmode calculations can be timeconsuming, and they require large amounts of computational resources. For this reason, we advise us ing only a small number of frames for nmode analysis compared to typical PB and GB analyses. MMPBSA.py also provides control variables ( nmstartframe, nmendframe, and nminterval) in the &nmode section that allow users to define a subset of the frames analyz ed by PB or GB for nmode calculations. This provides the users with the ability to combine the PB and/or GB results with the entropic contributions using a different number of frames with only a single call to MMPBSA.py 18.104.22.168 Quasi harmonic Approximation Entropy can also be calculated using the quasi harmonic approximation in which all analyzed frames are aligned to the average structure of the ensemble. The mass -
168 weighted covariance matrix of all atoms is calculated from these frames and then diagonalized to obt ain pseudovibrational frequencies. Quasi harmonic calculations are run when the control variable entropy is set to 1 in the &general namelist of the MMPBSA.py input file. Because minimizations are not required and only one matrix diagonalization is necess ary, quasi harmonic calculations are faster than normal mode calculations. However, obtaining converged results can be difficult because the method depends on the number of frames used to calculate the covariance matrix. 5.3.8 Running in Parallel Figure 53 MMPBSA.py scaling comparison for MM PBSA and MM GBSA calculations on 200 frames of a 5910atom complex. Times shown are the times required for the calculation to finish. Note that MM GBSA calculations are approximately 5x faster than MM PBSA calculations. All calculations were performed on NICS Keeneland (2 Intel Westmere 6core CPUs per node, QDR infiniband interconnect).
169 MMPBSA.py is implemented in parallel so users with access to multiple processors can speed up their calculati ons. MMPBSA.py.MPI is the parallel implementation of MMPBSA.py that uses a Message Passing Interface (MPI) for Python ( mpi4py ). Since energy calculations for each frame are independent, the calculation can be trivially parallelized given enough available processors. MMPBSA.py.MPI divides frames evenly across all processors, which allows calculations using many frames to scale better than if MMPBSA.py invoked parallel executables to calculate free energies. However, perfect scaling is not attained because certain setups tasks and file input/output can only be done with a single processor. Figure 53 demonstrates scaling for a sample MM PBSA and MM GBSA calculation. 5.3.9 Differences to mm_pbsa.pl Prior to the development of MMPBSA.py end state free energy calculations could be performed in Amber using a script written in Perl called mm_pbsa.pl Both programs allow users to perform free energy calculations using the STP and MTP, although MMPBSA.py offers more flexibility when using the MTP. Both programs have the ability to use different PB and GB models contained within Amber and estimate entropic contributions. Finally, MMPBSA.py and mm_pbsa.pl can run free energy calculations in parallel, although only MMPBSA.py can run on distributed mem ory systems ( i.e. on multiple nodes connected over a network). Despite their obvious similarities, there are many differences that exist in their accessibility, implementation, and capabilities. MMPBSA.py is available freeof charge alongside AmberTools, while an Amber license is necessary to obtain mm_pbsa.pl The usage of MMPBSA.py is intended to resemble Ambers MD engines for ease of the user, while mm_pbsa.pl s input file and usage has its own syntax. Only MMPBSA.py
170 has an intuitive mechanism for gues sing the ligand and receptor masks of a complex based on the topology files provided and analyzes topology files for parameter consistency. Furthermore, only MMPBSA.py can calculate entropic contributions to the free energy using the quasi harmonic approxi mation. An interface to external PB solvers such as Delphi MEAD and UHBD is available with mm_pbsa.pl only, although both can use apbs Surface area nonpolar solvation free energies calculated using the molsurf program is also only available with mm_pbsa.pl MMPBSA.py allows users to provide their own input files for external programs, which gives users the ability to adjust all parameters, not just the variables described in the MMPBSA.py manual; in comparison, mm_pbsa.pl has no similar functionality wit hout directly altering the source code. Finally, QM/MM GBSA and MM/3D RISM calculations are only available through the MMPBSA.py implement ation. 5.4 Concluding Remarks MMPBSA.py is a program written in Python and nab that allows users to easily and efficiently calculate free energies from MD and MC simulations. Using MMPBSA.py simplifies the endstate free energy calculation workflow by automating many tasks and checking topology files for consistency to minimize user error. The command line syntax and input fi le structure mimics those of other Amber programs, thereby reducing the learning curve. Accompanying test cases provide users with example inputs for performing each supported type of calculation. MMPBSA.py.MPI can be run in parallel to increase the speed of end state free energy calculations. MMPBSA.py is flexible enough to accommodate the needs of most users. Several methods may be used to calculate the free energy of solvation, such as PB, GB, and RISM. Furthermore, when using a GB model, part of the sys tem can be treated
171 quantum mechanically. Specific interactions can be analyzed using alanine scanning or free energy decomposition. Solute entropy is approximated using a rigid rotor model in which the vibrational frequencies can be calculated using either normal mode or quasi harmonic approaches. For increased flexibility, advanced users can supply custom input files to control all variables, including those not available in the MMPBSA.py input file. MMPBSA.py is released with AmberTools under the GNU General Public License (version 2), available for download from http://ambermd.org/. Documentation and tutorials are also available on the Amber web site.
172 CHAPTER 6 CONCLUSIONS Chagas disease, often neglected because of its lack of presence in developed countries, w as discovered over 100 years ago and currently affects millions of people worldwide, but there is still no single effective drug to treat the lethal disease. Trypanosoma cruzi the causative agent of Chagas, expresses a trans sialidase (TcTS) enzyme that helps the parasite evade the host immune system and assists in host cell invasion. TcTS, which has no human analog, is an ideal drug target for the design and discovery of treatments against Chagas disease. Despite its high sequence and structural similar ity to neuraminidases, there are currently no known strong inhibitors of TcTS. The goal of this study was to design and discover new potential inhibitors that could be used to develop treatments for Chagas disease. Because of the lack of known actives, mo lecular docking results of compound fragments to three different TcTS conformations were used to develop energy optimized pharmacophore (epharmacophore) models. The epharmacophore models detailed the important proteinligand interactions that are hypothesized as vital to inhibiting the enzyme. Several motifs of TcTS arginine triad, Arg53, and acceptor binding sitewere already known to be important for catalytic activity from previous studies, but other enzyme residues Arg93, Asp96, Glu230, and the hydrophobic pocket created by Val95 and Leu176 are suggested as new regions of the active site to target for novel inhibitor design. ZINCs Leads database was screened for hit lead compounds using the epharmacophore models and their corresponding features. The virtual screening discovered 82 new potential inhibitors of TcTS, including two compounds ZINC13359679 and
173 ZINC02576132that were calculated to have significantly stronger free energies of binding than the natural TcTS substrate, sialyl lactose. Both compounds appear stable in the TcTS active site based on MD simulations during the time frame analyzed. Independent molecular docking of over 35 million compounds was also performed using Schrdingers Glide on a collection of molecular databases, as well as co mpounds designed using de novo software. The most promising compounds from docking results were further analyzed using MD simulations and MM GBSA binding free energy calculations. Several of the top compounds from MM GBSA calculations were designed using A utoGrow or LigBuilder, but these molecules are not necessarily ideal drugs because they are large, flexible, and potentially difficult to synthesize. Other compounds, such as PubChem 54353125 and ZINC04939766, are more easily obtainable for in vitro and in vivo assays, but their calculated binding free energies suggest they will be weaker inhibitors than those designed by de novo methods. Rational drug design was also performed on several potential inhibitors attempting to improve the binding of these lead compounds with mixed results. The use of endstate free energy calculations has become an important tool for in silico drug discovery methods. To aid in the discovery of novel inhibitors of TcTS, a new program MMPBSA.py was developed that streamlines the c alculation of endstate free energies, including binding free energy calculations of proteinligand complexes. MMPBSA.py is user friendly and provides many options for the calculation of solvation free energies and entropies. Residue interaction energies c an also be calculated using energy decomposition and alanine scanning techniques. A parallel implementation of MMPBSA.py is available that can significantly reduce the wall time of free energy
174 calculations when multiple processors are available. This softw are is freely distributed alongside AmberTools and can be downloaded from the Amber molecular dynamics website ( http://ambermd.org/ ). Drug design is a difficult process that involves the use of many tools with the inte ntion of discoveries new inhibitors of enzymes. Unfortunately, computational methods are not rigorous and include many uncertainties that often give spurious results. In this study, the ZINC Leads molecular database was screened for potential TcTS inhibito rs once using Glide docking and once using Phase epharmacophore screening. Although both programs are distributed with Schrdinger, they gave very different results. For example, of the two best compounds from epharmacophore screening ZINC02576132 and ZI NC13359679only ZINC02576132 made it through the initial Glide HTVS filtering, and docking of ZINC02576132 to TcTS with Glide XP gave the 7,056th best free energy of binding. These results suggest that given the uncertainty in results from drug design methods, multiple computational approaches should be utilized when attempting to discover novel inhibitors of enzymes. Furthermore, docking methods clearly have their own limitations that suggest the inclusion of more rigorous methods, such as MD and MM GBSA. As Figure 41 shows, there is little correlation between docking results and MM GBSA binding energies. MM GBSA calculations have multiple advantages over docking results (e.g. averaging over many conformations, results that are more comparable to experiments, etc.) that suggest the MM GBSA results are more dependable for drug discovery. The multiple computational strategies employed together in this study are unique in the pursuit of novel inhibitors of TcTS to treat Chagas diseas e.
175 APPENDIX: SUPPLEMENTARY MATERIAL A.1 Hit Compounds From e pharmacophore Screening Table A 1 All hit compounds from screening results of the 1S0I pharmacophore according to MM GBSA binding free energies. The Phase Fitness scores are unitless, while the MM GBSA energies have units of kcal/mol. The uncertainties reported represent the standard error of the mean. Rank ZINC code Structure Phase Fitness MM GBSA 1 ZINC13359679 0.184 76.3 0.4 2 ZINC23187566 0.173 58.0 0.7 3 Z INC20107259 0.187 56.4 0.8 4 ZINC73658621 0.177 53.5 0.6 5 ZINC06614928 0.199 49.2 1.1
176 Table A 1 Continued Rank ZINC code Structure Phase Fitness MM GBSA 6 ZINC65394612 0.136 47.2 0.3 7 ZINC49466481 0.226 45.2 0.4 8 ZINC69 491994 0.211 44.8 0.3 9 ZINC71416387 0.144 42.1 0.9 10 ZINC13259915 0.194 40.3 0.3
177 Table A 1 Continued Rank ZINC code Structure Phase Fitness MM GBSA 11 ZINC08132802 0.231 38.9 0.3 12 ZINC58184798 0.132 37.5 0.3 13 ZINC038 63603 0.212 36.7 0.4 14 ZINC65373523 0.121 36.5 0.4 15 ZINC13179775 0.191 34.9 0.7
178 Table A 1 Continued Rank ZINC code Structure Phase Fitness MM GBSA 16 ZINC25703562 0.165 34.3 0.3 17 ZINC65394611 0.133 34.2 0.4 18 ZINC581 84800 0.186 34.2 0.5 19 ZINC38192570 0.191 33.3 0.4 20 ZINC02004481 0.220 27.5 0.3
179 Table A 1 Continued Rank ZINC code Structure Phase Fitness MM GBSA 21 ZINC58184799 0.196 26.9 0.3 22 ZINC34349844 0.212 26.2 0.3 23 ZINC052 73977 0.217 21.0 0.2 24 ZINC02384820 0.212 16.6 0.4 25 ZINC30731228 0.153 4.6 0.4
180 Table A 2 All hit compounds from screening results of the holo pharmacophore according to MM GBSA binding free energies. The Phase Fitness scores are unitless, while the MM GBSA energies have units of kcal/mol. The uncertainties reported represent the standard error of the mean. Rank ZINC code Structure Phase Fitness MM GBSA 1 ZINC02576132 0.194 73.2 0.5 2 ZINC40351842 0.145 55.1 0.4 3 ZINC60181779 0.198 52.2 0.4 4 ZINC19795370 0.184 48.5 1.0
181 Table A 2. Continued Rank ZINC code Structure Phase Fitness MM GBSA 5 ZINC03406782 0.197 45.0 0.5 6 ZINC58184800 0.176 40.5 0.5 7 ZINC0040596 0 0.240 39.4 0.7 8 ZINC38192570 0.196 38.1 0.5 9 ZINC17903516 0.192 36.7 0.6
182 Table A 2. Continued Rank ZINC code Structure Phase Fitness MM GBSA 10 ZINC72334048 0.129 36.4 0.3 11 ZINC72417077 0.180 35.9 0.3 12 ZINC04545875 0.204 33.4 0.4 13 ZINC03168774 0.155 33.1 0.5 14 ZINC65395904 0.164 32.8 0.2
183 Table A 2. Continued Rank ZINC code Structure Phase Fitness MM GBSA 15 ZINC55211759 0.168 32.6 0.4 16 ZINC00550909 0.183 32.2 0.6 17 ZINC015 79638 0.157 31.8 0.5 18 ZINC02560943 0.204 29.8 0.5
184 Table A 2. Continued Rank ZINC code Structure Phase Fitness MM GBSA 19 ZINC02560953 0.213 29.8 0.8 20 ZINC02107849 0.176 29.4 0.4 21 ZINC55405006 0.176 29.4 0.3 22 ZINC02 512042 0.157 28.9 0.7
185 Table A 2. Continued Rank ZINC code Structure Phase Fitness MM GBSA 23 ZINC38580567 0.185 28.2 0.7 24 ZINC72320181 0.133 26.0 0.3 25 ZINC39715047 0.185 23.4 0.6 26 ZINC72154304 0.197 23.1 0.5 27 ZINC71788361 0.211 22.5 0.3
186 Table A 2. Continued Rank ZINC code Structure Phase Fitness MM GBSA 28 ZINC04016135 0.125 22.4 0.3 29 ZINC28631632 0.154 19.4 0.4 30 ZINC71769780 0.170 18.8 0.5
187 Table A 3 All hit compounds from screening results of the apo pharmacophore according to MM GBSA binding free energies. The Phase Fitness scores are unitless, while the MM GBSA energies have units of kcal/mol. The uncertainties reported represent the standard err or of the mean. Rank ZINC code Structure Phase Fitness MM GBSA 1 ZINC72447098 0.155 54.8 0.3 2 ZINC67946325 0.186 50.1 0.4 3 ZINC72145690 0.144 44.0 0.5 4 ZINC19850530 0.168 42.0 0.3 5 ZINC72432649 0.168 41.8 0.3 6 ZINC72477708 0.145 38.6 0.4 7 ZINC72407055 0.155 37.0 0.3
188 Table A 3. Continued Rank ZINC code Structure Phase Fitness MM GBSA 8 ZINC23379459 0.169 36.8 0.5 9 ZINC01432054 0.154 34.3 0.6 10 ZINC32919121 0.201 33.8 0.4 11 ZINC72429720 0.168 33.6 0.3 12 ZINC49453236 0.136 32.6 0.3 13 ZINC72173820 0.164 32.5 0.6
189 Table A 3. Continued Rank ZINC code Structure Phase Fitness MM GBSA 14 ZINC72419466 0.155 32.1 0.3 15 ZINC19874293 0.140 30.7 0.5 16 ZINC72433326 0.170 29.4 0.4 17 ZINC13123936 0.174 28.9 0.3 18 ZINC44551566 0.143 28.6 0.3 19 ZINC72431004 0.163 28.3 0.3
190 Table A 3. Continued Rank ZINC code Structure Phase Fitness MM GBSA 20 ZINC72369398 0.120 26.8 0.5 21 ZINC02384991 0 .234 24.3 0.5 22 ZINC72480550 0.121 22.5 0.5 23 ZINC65384219 0.178 22.2 0.5 24 ZINC71614084 0.152 19.6 0.4 25 ZINC71751320 0.196 15.5 0.2 26 ZINC72160907 0.189 15.0 0.3 27 ZINC07518893 0.108 11.1 0.4
191 A.2 Potential Inhibitors of TcTS From Molecular Docking and de novo Design Table A 4 Compounds from molecular docking and de novo design considered as potential inhibitors of TcTS. All compounds were subjected to at least 10 ns of MD and ranked ac cording to the corresponding MM GBSA free energies of binding. MMGBSA energies have units of kcal/mol, and t he uncertainties reported represent the standard error of the mean. Rank Ligand Name Structure Source MM GBSA 1 DANA Result 034 LigBuilder 91. 2 0.1 2 Oseltamivir Result 70 LigBuilder 72.5 0.2 3 PubChem 54353125 PubChem 72.1 0.2
192 Table A 4. Continued Rank Ligand Name Structure Source MM GBSA 4 ZINC16247520 Rank 10 AutoGrow 62.0 0.2 5 Sialic Acid Rank 1 AutoGrow 61.5 0.3 6 ZINC04939766 ZINC Leads 58.5 0.2 7 Zanamivir phosphonateRank 62 Literature 57.9 0.3
193 Table A 4. Continued Rank Ligand Name Structure Source MM GBSA 8 Zanamivir phosophonate+ Rank 1 Literature 55.8 0.3 9 ZINC34334192 ZINC Leads 54.7 0.2 10 ZINC04280204 ZINC Leads 54.6 0.2 11 Asinex 380341 Asinex 54.3 0.4
194 Table A 4. Continued Rank Ligand Name Structure Source MM GBSA 12 PubChem 12000964 PubChem 54.1 0.2 13 ZINC02044476 ZINC Leads 53.0 0.3 14 PubChem 444507 PubChem 51.3 0.3 15 PDB 1W7X ligand 1W7X PDB 50.8 0.2
195 Table A 4. Continued. Rank Ligand Name Structure Source MM GBSA 16 ZINC20472820 ZINC ChemBridge 50.1 0.1 17 ZINC31467426 ZINC DrugLike 49.5 0.5 18 ZINC59351056 ZIN C Leads 46.9 0.3 19 ZINC16247520 Rank 5 AutoGrow 46.8 0.6
196 Table A 4. Continued. Rank Ligand Name Structure Source MM GBSA 20 ZINC16247520 ZINC ChemBridge 46.6 0.3 21 ZINC00195758 ZINC Leads 45.0 0.2 22 ZINC24812755 ZINC DrugLike 44.9 0.3 23 4 amino DANA phosphonate Literature 44.2 0.4 24 ZINC16247520 Rank 1 AutoGrow 43.7 0.2
197 Table A 4. Continued. Rank Ligand Name Structure Source MM GBSA 25 ZINC35112851 ZINC Leads 43.6 0.2 26 ZINC39394607 ZINC Leads 43. 4 0.2 27 ZINC38577973 ZINC PubChem 42.2 0.4 28 Ligand 7442 Katritzky Set 42.9 0.3
198 Table A 4. Continued. Rank Ligand Name Structure Source MM GBSA 29 277 NCI Diversity Set II 42.8 0.1 30 ZINC38455214 ZINC PubChem 42.2 0.2 31 Z INC28232161 ZINC Leads 42.1 0.2 32 ZINC13298421 ZINC PubChem 41.7 0.8
199 Table A 4. Continued. Rank Ligand Name Structure Source MM GBSA 33 ZINC21151234 ZINC DrugLike 41.2 0.2 34 486 NCI Diversity Set II 40.4 0.1 35 Super Fragment 4 ZINC Fragments Combined 39.4 0.3 36 Zanamivir phosphonateRank 3 Literature 39.3 0.5
200 Table A 4. Continued. Rank Ligand Name Structure Source MM GBSA 37 ZINC16247520 CH2ArOH CH2 ZINC ChemBridge Modified 38.8 0.3 38 ZINC31471796 ZI NC DrugLike 38.4 0.6 39 ZINC35444554 ZINC PubChem 38.0 0.4 40 ZINC48745899 ZINC Enamine 37.8 0.2 41 Fragments Combo ZINC Fragments + LigBuilder 37.7 0.2
201 Table A 4. Continued. Rank Ligand Name Structure Source MM GBSA 42 Zanamivir phosphonateRank 1 Literature 37.6 0.2 43 ZINC19328706 ZINC DrugBank Experimental 37.5 0.2 44 ZINC14676974 ZINC PubChem 37.4 0.2 45 571 NCI Diversity Set II 36.7 0.2
202 Table A 4. Continued. Rank Ligand Name Structure Source MM GBSA 46 ZINC62001775 ZINC Leads 36.1 0.2 47 451 NCI Diversity Set II 35.5 0.1 48 Ligand 6400 Katritzky Set 35.3 0.4 49 ZINC17192809 ZINC Leads 35.2 0.2 50 ZINC18202350 ZINC Pharmeks 34.6 0.3
203 Table A 4. Continued. Rank Ligand N ame Structure Source MM GBSA 51 ZINC35183118 ZINC Leads 34.2 0.1 52 ZINC31572053 ZINC DrugLike 33.9 0.2 53 ZINC41540804 ZINC PBMR Labs 33.9 0.2 54 ZINC00119770 ZINC Leads 32.9 0.2 55 PubChem 44428315 PubChem 32.9 0.2
204 Table A 4. Continued. Rank Ligand Name Structure Source MM GBSA 56 1385 NCI Diversity Set II 32.2 0.2 57 ZINC29022478 ZINC DrugLike 31.8 0.4 58 ZINC16247520 Rank 2 AutoGrow 31.3 0.2 59 Oseltamivir phosphonate Literature 31.0 0.2
205 Tabl e A 4. Continued. Rank Ligand Name Structure Source MM GBSA 60 ZINC48736239 ZINC Enamine 30.4 0.3 61 639 NCI Diversity Set II 30.0 0.1 62 ZINC01675031 ZINC Leads 29.2 0.2 63 ZINC07057776 ZINC DrugLike 28.5 0.3 64 Oseltamivir phosphonate Rank 6 LigBuilder 27.8 0.2
206 Table A 4. Continued. Rank Ligand Name Structure Source MM GBSA 65 ZINC03945038 ZINC Pharmeks 27.8 0.3 66 ZINC29020993 ZINC DrugLike 27.5 0.5 67 ZINC08995828 ZINC Pharmeks 26.9 0.2 68 ZINC05819288 ZINC Leads 26.2 0.1 69 ZINC31000655 ZINC DrugLike 23.6 0.1 70 ZINC50027772 ZINC Leads 20.7 0.2
207 Table A 4. Continued. Rank Ligand Name Structure Source MM GBSA 71 572 NCI Diversity Set II 19.6 0.1 72 ZINC30600361 ZINC DrugLike 19.5 0.2 73 ZINC33015084 ZINC DrugLike 19.4 0.4 74 ZINC09915898 ZINC Fragments 18.9 0.1 75 449 NCI Diversity Set II 18.8 0.3 76 chEMBL 198898 chEMBL 18.1 0.2
208 Table A 4. Continued. Rank Ligand Name Structure Source MM GBSA 7 7 ZINC17001340 ZINC Leads 17.9 0.2 78 ZINC34312910 ZINC PubChem 16.4 0.4 79 ZINC17043337 ZINC DrugLike 14.8 0.1 80 ZINC05144331 ZINC DrugLike 12.7 0.3 81 ZINC20555579 ZINC DrugLike 10.2 0.1
209 Table A 4. Continued. Rank Ligand Name Structure Source MM GBSA 82 ZINC08655738 ZINC DrugLike 8.8 0.6
210 LIST OF REFERENCES (1) Chagas, C. Memrias Inst. Oswaldo Cruz 1909, 1 159 218. (2) Moncayo, .; Silveira, A. C. Memrias Inst. Oswaldo Cruz 2009, 104, 17 30. (3) Organization, W. H. Control of Chagas Disease: Second Report of the WHO Expert Committee [on the Control of Chagas Disease, Brasilia, 2028 November 2000]. ; World Health Organization, 2002. (4) Dodd, R. Y.; Leiby, D. A. Annu. Rev. Med. 2004 55, 191 207. (5) Gascon, J.; Bern, C.; Pinazo, M.J. Acta Trop. 2010, 115 22 27. (6) Crompton, D. W. D. W. T.; Peters, P. Working to Overcome the Global Impact of Neglected Tropical Diseases: Fir st WHO Report on Neglected Tropical Diseases 2010; World Health Organization, 2010. (7) Hotez, P. J.; Dumonteil, E.; Woc Colburn, L.; Serpa, J. A.; Bezek, S.; Edwards, M. S.; Hallmark, C. J.; Musselwhite, L. W.; Flink, B. J.; Bottazzi, M. E. PLoS Negl Tro p Dis 2012 6 e1498. (8) Leiby, D. A.; Herron, R. M.; Read, E. J.; Lenes, B. A.; Stumpf, R. J. Transfusion (Paris) 2002, 42 549 555. (9) Nowicki, M. J.; Chinchilla, C.; Corado, L.; Matsuoka, L.; Selby, R.; Steurer, F.; Mone, T.; Mendez, R.; Aswad, S. T ransplantation 2006 81 477 479. (10) Kun, H.; Moore, A.; Mascola, L.; Frank, S.; Gena, L.; Kubak, B.; Radhakrishna, S.; Leiby, D.; Herron, R.; Mone, T.; Hunter, R.; Kuehnert, M. Clin. Infect. Dis. 2009, 48 1534 1540. (11) Riera, C.; Guarro, A.; Kassab H. E.; Jorba, J. M.; Castro, M.; Angrill, R.; Gllego, M.; Fisa, R.; Martin, C.; Lobato, A.; Ports, M. Am. J. Trop. Med. Hyg. 2006 75 1078 1081. (12) Pereira, K. S.; Schmidt, F. L.; Guaraldo, A. M. A.; Franco, R. M. B.; Dias, V. L.; Passos, L. A. C. J. Food Prot. 2009, 72 441 446. (13) Pereira, K. S.; Schmidt, F. L.; Barbosa, R. L.; Guaraldo, A. M. A.; Franco, R. M. B.; Dias, V. L.; Passos, L. A. C. In Advances in Food and Nutrition Research; Steve L. Taylor, Ed.; Academic Press, 2010; Vol. Volume 59, pp. 63 85. (14) Chagas, C.; Chagas, C. Memrias Inst. Oswaldo Cruz 1916, 8 37 60. (15) Chagas, C.; Villela, E. Inst Osw Cruz 14 1922, 5 61. (16) Dawes, B. Advances in Parasitology Volume 6 APL; Academic Press, 1968.
211 (17) Barrett, M. P.; Burchmore, R. J.; Stich, A.; Lazzari, J. O.; Frasch, A. C.; Cazzulo, J. J.; Krishna, S. The Lancet 2003, 362 1469 1480. (18) Zhang, L.; Tarleton, R. L. J. Infect. Dis. 1999, 180, 480 486. (19) A ez, N.; Carrasco, H.; Parada, H.; Crisante, G.; Rojas, A.; Fuenmayor, C.; Gonzalez, N.; Percoco, G.; Borges, R.; Guevara, P. Am. J. Trop. Med. Hyg. 1999 60 726 732. (20) Garcia, S.; Ramos, C. O.; Senra, J. F. V.; Vilas Boas, F.; Rodrigues, M. M.; Campos deCarvalho, A. C.; Ribeiro dos Santos, R.; Soares, M. B. P. Antimic rob. Agents Chemother. 2005, 49, 1521 1528. (21) Jackson, Y.; Chatelain, E.; Mauris, A.; Holst, M.; Miao, Q.; Chappuis, F.; Ndao, M. BMC Infect. Dis. 2013 13, 85. (22) Castro, J. A.; deMecca, M. M.; Bartel, L. C. Hum Exp Toxicol 2006, 25, 471 479. (23) Filardi, L. S.; Brener, Z. Trans. R. Soc. Trop. Med. Hyg. 1987, 81, 755 759. (24) Docampo, R.; Schmuis, G. A. Parasitol. Today 1997, 13 129 130. (25) Engel, J. C.; Doyle, P. S.; Hsieh, I.; McKerrow, J. H. J. Exp. Med. 1998, 188 725 734. (26) McKerro w, J. H. Int. J. Parasitol. 1999, 29, 833 837. (27) Bond, C. S.; Zhang, Y.; Berriman, M.; Cunningham, M. L.; Fairlamb, A. H.; Hunter, W. N. Structure 1999, 7 81 89. (28) Freymann, D. M.; Wenck, M. A.; Engel, J. C.; Feng, J.; Focia, P. J.; Eakin, A. E.; Craig III, S. P. Chem. Biol. 2000, 7 957 968. (29) Lira, R.; Contreras, L. M.; Rita, R. M. S.; Urbina, J. A. J. Antimicrob. Chemother. 2001, 47 537 546. (30) Martin, M. B.; Grimley, J. S.; Lewis, J. C.; Heath, H. T.; Bailey, B. N.; Kendrick, H.; Yardle y, V.; Caldera, A.; Lira, R.; Urbina, J. A.; Moreno, S. N. J.; Docampo, R.; Croft, S. L.; Oldfield, E. J. Med. Chem. 2001, 44, 909 916. (31) Oli, M. M.; Einicker Lamas, M. An. Acad. Bras. Cincias 2000 72 413 419. (32) Marini, V.; Moretti, E.; Bermejo, D.; Basso, B. Memrias Inst. Oswaldo Cruz 2011, 106 32 37. (33) Ramsey, J. M.; Cruz Celis, A.; Salgado, L.; Espinosa, L.; Ordoez, R.; Lopez, R.; Schofield, C. J. J. Med. Entomol. 2003, 40 912 920. (34) FriedmanRudovsky, J. Science 2012 336 666 667.
212 (35) Bern, C.; Montgomery, S. P.; Katz, L.; Caglioti, S.; Stramer, S. L. Curr. Opin. Infect. Dis. 2008 21 476 482. (36) Andrews, N. W.; Hong, K.; Robbins, E. S.; Nussenzweig, V. Exp. Parasitol. 1987, 64, 474 484. (37) Ley, V.; Andrews, N. W.; Robbins, E. S.; Nussenzweig, V. J. Exp. Med. 1988 168, 649 659. (38) Bonaldo, M. C.; SoutoPadron, T.; Souza, W. de; Goldenberg, S. J. Cell Biol. 1988, 106 1349 1358. (39) Kleffmann, T.; Schmidt, J.; Schaub, G. A. J. Eukaryot. Microbiol. 1998 45 548 555. ( 40) Andrews, N. W. Biol. Res. 1993 26 65 67. (41) Schauer, R. Zoology 2004, 107 49 64. (42) Pilatte, Y.; Bignon, J.; Lambr, C. R. Glycobiology 1993, 3 201 218. (43) Previato, J.; Andrade, A. F. B.; Pessolani, M. C. V.; Mendona Previato, L. Mol. B iochem. Parasitol. 1985, 16, 85 96. (44) Schenkman, S.; Eichinger, D.; Pereira, M. E. A.; Nussenzweig, V. Annu. Rev. Microbiol. 1994, 48, 499 523. (45) Todeschini, A. R.; MendoncaPreviato, L.; Previato, J. O.; Varki, A.; Halbeek, H. van Glycobiology 200 0 10, 213 221. (46) Buschiazzo, A.; Amaya, M. F.; Amaya, M. L.; Frasch, A. C.; Alzari, P. M. Mol. Cell 2002 10 757 768. (47) Buscaglia, C. A.; Alfonso, J.; Campetella, O.; Frasch, A. C. C. Blood 1999, 93 2025 2032. (48) Crennell, S. J.; Garman, E. F .; Laver, W. G.; Vimr, E. R.; Taylor, G. L. Proc. Natl. Acad. Sci. U. S. A. 1993 90 9852 9856. (49) Luo, Y.; Li, S. C.; Li, Y. T.; Luo, M. J. Mol. Biol. 1999, 285 323 332. (50) Taylor, G. Curr. Opin. Struct. Biol. 1996 6 830 837. (51) Horenstein, B A.; Yang, J.; Bruner, M. NUKLEONIKA 2002, 47, S25 S28. (52) Watts, A. G.; Damager, I.; Amaya, M. L.; Buschiazzo, A.; Alzari, P.; Frasch, A. C.; Withers, S. G. J Am Chem Soc 2003, 125 7532 7533.
213 (53) Amaya, M. F.; Watts, A. G.; Damager, I.; Wehenkel, A .; Nguyen, T.; Buschiazzo, A.; Paris, G.; Frasch, A. C.; Withers, S. G.; Alzari, P. M. Structure 2004, 12, 775 784. (54 ) PierdominiciSottile, G.; Roitberg, A. E. Biochem. 2011, 50, 836 842. (55) Mitchell, F. L.; Miles, S. M.; Neres, J.; Bichenkova, E. V .; Bryce, R. A. Biophys. J. 2010, 98 L38 L40. (56 ) PierdominiciSottile, G.; Horenstein, N. A.; Roitberg, A. E. Biochem. 2011, 50 10150 10158. (57) Pauling, L. Chem. Eng. News Arch. 1946, 24, 1375 1377. (58) Pauling, L. Nature 1948 161 707 709. (59 ) Demir, O.; Roitberg, A. E. Biochem. 2009, 48, 3398 3406. (60) Amaro, R. E.; Schnaufer, A.; Interthal, H.; Hol, W.; Stuart, K. D.; McCammon, J. A. Proc. Natl. Acad. Sci. 2008, 105 17278 17283. (61) Damm, K. L.; Ung, P. M. U.; Quintero, J. J.; Gestwicki J. E.; Carlson, H. A. Biopolymers 2008 89 643 652. (62) Meindl, P.; Bodo, G.; Palese, P.; Schulman, J.; Tuppy, H. Virology 1974 58, 457 463. (63) Vandekerckhove, F.; Schenkman, S.; Carvalho, L. P. de; Tomlinson, S.; Kiso, M.; Yoshida, M.; Hasegawa, A.; Nussenzweig, V. Glycobiology 1992 2 541 548. (64) Paris, G.; Ratier, L.; Amaya, M. F.; Nguyen, T.; Alzari, P. M.; Frasch, A. C. C. J. Mol. Biol. 2005 345 923 934. (65) Streicher H. Curr. Med. Chem. Anti Infect. Agents 2004, 3 149 161. (66) Ha rrison, J. A.; Kartha, K. P. R.; Turnbull, W. B.; Scheuerl, S. L.; Naismith, J. H.; Schenkman, S.; Field, R. A. Bioorg. Med. Chem. Lett. 2001, 11 141 144. (67) Buchini, S.; Buschiazzo, A.; Withers, S. G. Angew. Chem. 2008, 120 2740 2743. (68) Carvalho, S. T.; Sola Penna, M.; Oliveira, I. A.; Pita, S.; Goncalves, A. S.; Neves, B. C.; Sousa, F. R.; de Lima, L. F. ; Kurogochi, M.; Hinou, H.; Nishimura, S. I.; MendoncaPreviato, L.; Previato, J. O.; Todeschini, A. R. Glycobiology 2010, 20 1034 1045. (69) Agusti, R.; Paris, G.; Ratier, L.; Frasch, A. C. C.; de Lederkremer, R. M. Glycobiology 2004, 14 659 670.
214 (70) Giorgi, M. E.; Ratier, L.; Agusti, R.; Frasch, A. C. C.; Lederkremer, R. M. de Glycoconj. J. 2010, 27 549 559. (71) Giorgi, M. E.; Piuselli, D.; Agusti, R.; de Lederkremer, R. M.; Giordano, S. ARKIVOC 2011 7 260 271. (72) Kolb, H. C.; Finn, M. G.; Sharpless, K. B. Angew. Chem. Int. Ed. 2001 40 2004 2021. (73) Carvalho, I.; Andrade, P.; Campo, V. L.; Guedes, P. M. M.; Sesti Costa, R.; Silv a, J. S.; Schenkman, S.; Dedola, S.; Hill, L.; Rejzek, M.; Nepogodiev, S. A.; Field, R. A. Bioorg. Med. Chem. 2010, 18 2412 2427. (74) Harrison, J. A.; Kartha, K. P. R.; Fournier, E. J. L.; Lowary, T. L.; Malet, C.; Nilsson, U. J.; Hindsgaul, O.; Schenkm an, S.; Naismith, J. H.; Field, R. A. Org. Biomol. Chem. 2011, 9 1653. (75) Meinke, S.; Schroven, A.; Thiem, J. Org. Biomol. Chem. 2011, 9 4487 4497. (76) Neres, J.; Bonnet, P.; Edwards, P. N.; Kotian, P. L.; Buschiazzo, A.; Alzari, P. M.; Bryce, R. A.; Douglas, K. T. Bioorg. Med. Chem. 2007, 15 2106 2119. (77) Neres, J.; Brewer, M. L.; Ratier, L.; Botti, H.; Buschiazzo, A.; Edwards, P. N.; Mortenson, P. N.; Charlton, M. H.; Alzari, P. M.; Frasch, A. C.; Bryce, R. A.; Douglas, K. T. Bioorg. Med. Chem. Lett. 2009 19 589 596. (78) Kim, J. H.; Ryu, H. W.; Shim, J. H.; Park, K. H.; Withers, S. G. ChemBioChem 2009, 10 2475 2479. (79) Lima, A. H.; Lameira, J.; Alves, C. N. Struct. Chem. 2012, 23 147 152. (80) Arioka, S.; Sakagami, M.; Uematsu, R.; Yam aguchi, H.; Togame, H.; Takemoto, H.; Hinou, H.; Nishimura, S. I. Bioorg. Med. Chem. 2010 18 1633 1640. (81) Fischer, E. Berichte Dtsch. Chem. Ges. 1894, 27, 2985 2993. (82) Levinthal, C.; Wodak, S. J.; Kahn, P.; Dadivanian, A. K. Proc. Natl. Acad. Sci 1975, 72 1330 1334. (83) Salemme, F. R. J. Mol. Biol. 1976 102 563 568. (84) Wodak, S. J.; Janin, J. J. Mol. Biol. 1978, 124 323 342. (85) Kuntz, I. D.; Blaney, J. M.; Oatley, S. J.; Langridge, R.; Ferrin, T. E. J. Mol. Biol. 1982, 161 269 288. ( 86) Nicklaus, M. C.; Wang, S.; Driscoll, J. S.; Milne, G. W. A. Bioorg. Med. Chem. 1995, 3 411 428.
215 (87) Koshland, D. E. Proc. Natl. Acad. Sci. U. S. A. 1958, 44 98 104. (88) DesJarlais, R. L.; Sheridan, R. P.; Dixon, J. S.; Kuntz, I. D.; Venkataraghavan, R. J. Med. Chem. 1986 29 2149 2153. (89) Busetta, B.; Tickle, I. J.; Blundell, T. L. J. Appl. Crystallogr. 1983 16 432 437. (90) Goodford, P. J. J. Med. Chem. 1985, 28 849 857. (91) Pattabiraman, N.; Levitt, M.; Ferrin, T. E.; Langridge, R. J. Comput. Chem. 1985, 6 432 436. (92) Tomioka, N.; Itai, A.; Iitaka, Y. J. Comput. Aided Mol. Des. 1987 1 197 210. (93) Laurie, A. T. R.; Jackson, R. M. Bioinformatics 2005, 21, 1908 1916. (94) Neuvirth, H.; Raz, R.; Schreiber, G. J. Mol. Biol. 2004, 338 181 199. (95) Liu, X. S.; Brutlag, D. L.; Liu, J. S. Nat. Biotechnol. 2002, 20, 835 839. (96) Singh, T.; Biswas, D.; Jayaram, B. J. Chem. Inf. Model. 2011 51 2515 2527. (97) Jiang, F.; Kim, S.H. J. Mol. Biol. 1991, 219 79 102. (98) Sherman, W. ; Day, T.; Jacobson, M. P.; Friesner, R. A.; Farid, R. J. Med. Chem. 2006, 49 534 553. (99) Ferrari, A. M.; Wei, B. Q.; Costantino, L.; Shoichet, B. K. J. Med. Chem. 2004, 47, 5076 5084. (100) Leach, A. J. Mol. Biol. 1994, 235 345 356. (101) Anderson, A. C.; ONeil, R. H.; Surti, T. S.; Stroud, R. M. Chem. Biol. 2001, 8 445 457. (102) Yang, A. Y. C.; Kllblad, P.; Mancera, R. L. J. Comput. Aided Mol. Des. 2004 18, 235 250. (103) Cavasotto, C. N.; Abagyan, R. A. J. Mol. Biol. 2004, 337 209 225. (10 4) Totrov, M.; Abagyan, R. Proteins 1997, Suppl 1 215 220. (105) Meiler, J.; Baker, D. Proteins Struct. Funct. Bioinforma. 2006, 65, 538 548. (106) Schrdinger http://www.schrodinger.com/ (accessed Jul 5, 2013). (107) Alonso, H.; Bliznyuk, A. A.; Grea dy, J. E. Med. Res. Rev. 2006, 26 531 568.
216 (108) Lin, J. H.; Perryman, A. L.; Schames, J. R.; McCammon, J. A. J. Am. Chem. Soc. 2002, 124 5632 5633. (109) Lin, J. H.; Perryman, A. L.; Schames, J. R.; McCammon, J. A. Biopolymers 2003, 68, 47 62. (110) Amaro, R. E.; Baron, R.; McCammon, J. A. J. Comput. Aided Mol. Des. 2008 22 693 705. (111) Goodsell, D. S.; Morris, G. M.; Olson, A. J. J. Mol. Recognit. JMR 1996, 9 1 5. (112) Fernndez Recio, J.; Totrov, M.; Abagyan, R. Proteins 2003, 52, 113 117. ( 113) Judson, R. S.; Jaeger, E. P.; Treasurywala, A. M. J. Mol. Struct. THEOCHEM 1994, 308 191 206. (114) Morris, G. M.; Goodsell, D. S.; Halliday, R. S.; Huey, R.; Hart, W. E.; Belew, R. K.; Olson, A. J. J Comp Chem 1998, 19 1639 1662. (115) Taylor, J S.; Burnett, R. M. Proteins 2000, 41 173 191. (116) Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R. J. Mol. Biol. 1997, 267 727 748. (117) Oshiro, C. M.; Kuntz, I. D.; Dixon, J. S. J. Comput. Aided Mol. Des. 1995, 9 113 130. (118) Hou T.; Wang, J.; Chen, L.; Xu, X. Protein Eng. 1999 12 639 648. (119) Yang, J. M.; Kao, C. Y. IEEE Trans. Inf. Technol. Biomed. Publ. IEEE Eng. Med. Biol. Soc. 2000 4 225 237. (120) Feher, M.; Williams, C. I. J. Chem. Inf. Model. 2009, 49, 1704 1714. (121) Williams, C. I.; Feher, M. J. Comput. Aided Mol. Des. 2008, 22, 39 51. (122) Goodsell, D. S.; Olson, A. J. Proteins Struct. Funct. Bioinforma. 1990, 8 195 202. (123) Metropolis, N.; Ulam, S. J. Am. Stat. Assoc. 1949, 44 335 341. (124) Metropoli s, N.; Rosenbluth, A. W.; Rosenbluth, M. N.; Teller, A. H.; Teller, E. J. Chem. Phys. 1953, 21 1087 1092. (125) Trosset, J. Y.; Scheraga, H. A. Proc. Natl. Acad. Sci. 1998, 95 8011 8015. (126) Pak, Y.; Wang, S. J. Phys. Chem. B 2000 104 354 359.
217 (127) Nakajima, N.; Higo, J.; Kidera, A.; Nakamura, H. Chem. Phys. Lett. 1997, 278 297 301. (128) Nakajima, N.; Nakamura, H.; Kidera, A. J. Phys. Chem. B 1997, 101, 817 824. (129) Di Nola, A.; Roccatano, D.; Berendsen, H. J. C. Proteins Struct. Funct. Bioinforma. 1994, 19, 174 182. (130) Mangoni, M.; Roccatano, D.; Di Nola, A. Proteins Struct. Funct. Bioinforma. 1999, 35 153 162. (131) Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K.; Shaw, D. E.; Francis, P.; Shenkin, P. S. J. Med. Chem. 2004, 47 1739 1749. (132) Jorgensen, W. L.; Maxwell, D. S.; TiradoRives, J. J. Am. Chem. Soc. 1996, 118 11225 11236. (133) Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G J. Mol. Biol. 1996 261 470 489. (134) Leach, A. R.; Kuntz, I. D. J. Comput. Chem. 1992, 13 730 748. (135) Ewing, T. J.; Makino, S.; Skillman, A. G.; Kuntz, I. D. J Comput Aided Mol 2001, 15, 411 428. (136) Miranker, A.; Karplus, M. Proteins Struct. Funct. Genet. 1991, 11 29 34. (137) Bohm, H. J. J. Comput. Aided Mol. Des. 1992 6 593 606. (138) Durrant, J. D.; Amaro, R. E.; McCammon, J. A. Chem. Biol. Drug Des. 2009 73 168 178. (139) Wang, R.; Gao, Y.; Lai, L. Mol. Model. Annu. 2000, 6 498 5 16. (140) Yuan, Y.; Pei, J.; Lai, L. J. Chem. Inf. Model. 2011, 51 1083 1091. (141) Judson, R. In Reviews in Computational Chemistry ; Lipkowitz, K. B.; Boyd, D. B., Eds.; John Wiley & Sons, Inc., 2007; pp. 1 73. (142) Kollman, P. Chem. Rev. 1993, 93, 2 395 2417. (143) Bash, P. A.; Field, M. J.; Karplus, M. J. Am. Chem. Soc. 1987 109 8092 8094. (144) Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Nat Rev Drug Discov 2004, 3 935 949. (145) Jones, J. E. Proc. R. Soc. Lond. Ser. Contain. Pap. Math. Phys. Character 1924, 106, 441 462.
218 (146) Verdonk, M. L.; Cole, J. C.; Hartshorn, M. J.; Murray, C. W.; Taylor, R. D. Proteins Struct. Funct. Bioinforma. 2003, 52, 609 623. (147) Eldridge, M. D.; Murray, C. W.; Auton, T. R.; Paolini, G. V.; Mee, R. P. J. Comput. Aided Mol. Des. 1997 11 425 445. (148) Muegge, I.; Martin, Y. C. J. Med. Chem. 1999, 42 791 804. (149) Gohlke, H.; Hendlich, M.; Klebe, G. J. Mol. Biol. 2000, 295 337 356. (150) DeWitte, R. S.; Shakhnovich, E. I. J. Am. Chem. Soc. 199 6 118 11733 11744. (151) Friesner, R. A.; Murphy, R. B.; Repasky, M. P.; Frye, L. L.; Greenwood, J. R.; Halgren, T. A.; Sanschagrin, P. C.; Mainz, D. T. J. Med. Chem. 2006 49, 6177 6196. (152) Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. J. Med. Chem. 2004, 47, 1750 1759. (153) J. Comput. Chem. 2011, 32 742 755. (154) Kier, L. B. Mol. Pharmacol. 1967 3 487 494. (155) Kier, L. B. Molecular orbital theory in drug research; Academic Press, 1971. (156) Gner, O. F. Pharmacophore: Perception, Development, and Use in Drug Design ; Internatl University Line, 2000. (157) Barnum, D.; Greene, J.; Smellie, A.; Sprague, P. J. Chem. Inf. Comput. Sci. 1996, 36 563 571. (158) Chen, J.; Lai, L. J. Chem. Inf. Model. 2006, 46 2684 2691. (159) Dixon, S. L.; Smondyrev, A. M.; Rao, S. N. Chem. Biol. Drug Des. 2006, 67 370 372. (160) Dixon, S. L.; Smondyrev, A. M.; Knoll, E. H.; Rao, S. N.; S haw, D. E.; Friesner, R. A. J. Comput. Aided Mol. Des. 2006 20 647 671. (161) Yang, S. Y. Drug Discov. Today 2010 15 444 450. (162) Loving, K.; Salam, N. K.; Sherman, W. J. Comput. Aided Mol. Des. 2009, 23 541 554. (163) Salam, N. K.; Nuti, R.; She rman, W. J. Chem. Inf. Model. 2009, 49, 2356 2368. (164) Wang, H.; Duffy, R. A.; Boykow, G. C.; Chackalamannil, S.; Madison, V. S. J. Med. Chem. 2008, 51, 2439 2446.
219 (165) Schuster, D.; Nashev, L. G.; Kirchmair, J.; Laggner, C.; Wolber, G.; Langer, T.; O dermatt, A. J. Med. Chem. 2008 51 4188 4199. (166) Neves, M. A. C.; Dinis, T. C. P.; Colombo, G.; S e Melo, M. L. J. Med. Chem. 2009, 52 143 150. (167) Alder, B. J.; Wainwright, T. E. J. Chem. Phys. 1957, 27, 1208 1209. (168) Alder, B. J.; Wainwrig ht, T. E. J. Chem. Phys. 1959, 31, 459 466. (169) Verlet, L. Phys. Rev. 1967, 159 98 103. (170) Hockney, R. W.; Eastwood, J. W. Computer Simulation Using Particles ; CRC Press, 2010. (171) Leach, A. R. Molecular Modelling: Principles and Applications ; P earson Prentice Hall, 2001. (172) Ryckaert, J. P.; Ciccotti, G.; Berendsen, H. J. J. Comput. Phys. 1977, 23 327 341. (173) Andrea, T. A.; Swope, W. C.; Andersen, H. C. J. Chem. Phys. 1983, 79, 4576 4584. (174) Sindhikara, D. J.; Kim, S.; Voter, A. F.; Roitberg, A. E. J. Chem. Theory Comput. 2009, 5 1624 1631. (175) Homeyer, N.; Gohlke, H. Mol. Informatics 2012, 31, 114 122. (176) Genheden, S.; Luchko, T.; Gusarov, S.; Kovalenko, A.; Ryde, U. J. Phys. Chem. B 2010, 114 8505 8516. (177) Price, D. J .; Brooks, C. L. J. Chem. Phys. 2004, 121 10096 10103. (178) Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. J. Chem. Phys. 1983, 79 926 935. (179) Darden, T.; York, D.; Pedersen, L. J. Chem. Phys. 1993 98 10089. (180) Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Proteins 2006, 65, 712 725. (181) Prez, A.; Marchn, I.; Svozil, D.; Sponer, J.; Cheatham III, T. E.; Laughton, C. A.; Orozco, M. Biophys. J. 2007 92 3817 3829. (182) Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. J. Comput. Chem. 2004, 25 1157 1174. (183) Foloppe, N.; MacKerell, Jr., A. D. J. Comput. Chem. 2000, 21 86 104.
220 (184) Oostenbrink, C.; Villa, A.; Mark, A. E.; Van Gunsteren, W. F. J. Comput. Chem. 2004, 25 1656 1676. (185) Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. J. Comput. Chem. 2004, 25 1157 1174. (186) Lyne, P. D.; Lamb, M. L.; Saeh, J. C. J. Med. Chem. 2006, 49 4805 4808. (187) Rastelli, G.; Rio, A. D.; De gliesposti, G.; Sgobba, M. J. Comput. Chem. 2010, 31 797 810. (188) Hou, T.; Wang, J.; Li, Y.; Wang, W. J. Chem. Inf. Model. 2011, 51, 69 82. (189) Miller, B. R.; McGee, T. D.; Swails, J. M.; Homeyer, N.; Gohlke, H.; Roitberg, A. E. J. Chem. Theory Comput. 2012, 8 3314 3321. (190) Meng, X. Y.; Zhang, H.X.; Mezei, M.; Cui, M. Curr. Comput. Aided Drug Des. 2011, 7 146 157. (191) Chen, Y.; Shoichet, B. K. Nat. Chem. Biol. 2009 5 358 364. (192) Babaoglu, K.; Shoichet, B. K. Nat. Chem. Biol. 2006, 2 720 723. (193) Marcou, G.; Rognan, D. J. Chem. Inf. Model. 2007, 47, 195 207. (194) Wolber, G.; Seidel, T.; Bendix, F.; Langer, T. Drug Discov. Today 2008 13, 23 29. (195) Guner, O. F. Curr. Top. Med. Chem. 2002 2 1321 1332. (196) Massova, I.; Kol lman, P. Perspect. Drug Discov. Des. 2000, 18, 113 135. (197) Okimoto, N.; Futatsugi, N.; Fuji, H.; Suenaga, A.; Morimoto, G.; Yanai, R.; Ohno, Y.; Narumi, T.; Taiji, M. Biophys. J. 2010, 98, 460a. (198) Irwin, J. J.; Sterling, T.; Mysinger, M. M.; Bolst ad, E. S.; Coleman, R. G. J. Chem. Inf. Model. 2012, 52, 1757 1768. (199) Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A. E.; Berendsen, H. J. C. J. Comput. Chem. 2005, 26, 1701 1718. (200) Sastry, G. M.; Adzhigirey, M.; Day, T.; Annabhi moju, R.; Sherman, W. J. Comput. Aided Mol. Des. 1 14. (201) LigPrep v2.5 ; Schrodinger, Inc.: Portland, OR. (202) Gtz, A. W.; Williamson, M. J.; Xu, D.; Poole, D.; Le Grand, S.; Walker, R. C. J. Chem. Theory Comput. 2012, 8 1542 1555.
221 (203) Case, D. A .; Darden, T. A.; Cheatham, T. E. I.; Simmerling, C. L.; Wang, J.; Duke, R. E.; Luo, R.; Walker, R. C.; Zhang, W.; Merz, K. M.; Roberts, B. P.; Wang, B.; Hayik, S.; Roitberg, A. E.; Seabra, G.; Kolossvry, I.; Wong, K. F.; Paesani, F.; Vanicek, J.; Wu, X.; Brozell, S. R.; Steinbrecher, T.; Gohlke, H.; Cai, Q.; Ye, X.; Wang, J.; Hsieh, M. J.; Cui, G.; Roe, D. R.; Mathews, D. H.; Seetin, M. G.; Sagui, C.; Babin, V.; Luchko, T.; Gusarov, S.; Kovalenko, A.; Kollman, P. A. AMBER 12 ; AMBER; University of Californ ia, San Francisco, 2012. (204) Humphrey, W.; Dalke, A.; Schulten, K. J. Mol. Graph. 1996, 14 33 38. (205) Onufriev, A.; Bashford, D.; Case, D. A. Proteins 2004, 55 383 394. (206) Zhang, L. Y.; Gallicchio, E.; Friesner, R. A.; Levy, R. M. J. Comput. Ch em. 2001, 22, 591 607. (207) Perola, E.; Xu, K.; Kollmeyer, T. M.; Kaufmann, S. H.; Prendergast, F. G.; Pang, Y. P. J. Med. Chem. 2000, 43 401 408. (208) Grneberg, S.; Stubbs, M. T.; Klebe, G. J. Med. Chem. 2002, 45, 3588 3602. (209) Desai, P. V.; Pat ny, A.; Sabnis, Y.; Tekwani, B.; Gut, J.; Rosenthal, P.; Srivastava, A.; Avery, M. J. Med. Chem. 2004, 47 6609 6615. (210) Brunskole vegelj, M.; Turk, S.; Brus, B.; Laninik Riner, T.; Stojan, J.; Gobec, S. J. Chem. Inf. Model. 2011, 51, 1716 1724. (211) Hanwell, M. D.; Curtis, D. E.; Lonie, D. C.; Vandermeersch, T.; Zurek, E.; Hutchison, G. R. J. Cheminformatics 2012 4 17. (212) Trott, O.; Olson, A. J. J Comp Chem 2010, 31, 455 461. (213) Shie, J. J.; Fang, J.M.; Lai, P.T.; Wen, W.H.; Wang, S.Y.; Cheng, Y.S. E.; Tsai, K. C.; Yang, A.S.; Wong, C.H. J. Am. Chem. Soc. 2011, 133 17959 17965. (214) Neres, J.; Buschiazzo, A. ; Alzari, P. M.; Walsh, L.; Douglas, K. T. Anal. Biochem. 2006, 357 302 304. (215) De Ruiter, A.; Oostenbrink, C. Curr. Opin. Chem. Biol. 2011, 15, 547 552. (216) Zwanzig, R. W. J. Chem. Phys. 1954, 22, 1420 1426. (217) Meng, Y.; Sabri Dashti, D.; Roitberg, A. E. J. Chem. Theory Comput. 2011, 7 2721 2727. (218) McQuarrie, D. A.; McQuarrie, D. A.; McQuarrie, D. A.; McQuarrie, D. A. Statistical thermodynamics ; Harper and Row New York, 1973. (219) Gohlke, H.; Kiel, C.; Case, D. A. J. Mol. Biol. 2003, 33 0 891 914.
222 (220) Genheden, S.; Ryde, U. J. Comput. Chem. 2010, 31 837 846. (221) Srinivasan, J.; Cheatham, T. E.; Cieplak, P.; Kollman, P. A.; Case, D. A. J. Am. Chem. Soc. 1998, 120 9401 9409. (222) Steinbrecher, T.; Labahn, A. Curr. Med. Chem. 2010, 17 767 785. (223) Vargiu, A. V.; Magistrato, A. Inorg Chem 2012, 51 2046 2057. (224) Kar, P.; Knecht, V. J Phys Chem B 2012, 116, 2605 2614. (225) Wang, J.; Hou, T.; Xu, X. Curr. Comput. Aided Drug Des. 2006, 2 287 306. (226) Metz, A.; Pfleger, C.; Kopitz, H.; Pfeiffer Marek, S.; Baringhaus, K. H.; Gohlke, H. J Chem Inf Model 2011 52 120 133. (227) Massova, I.; Kollman, P. A. J Am Chem Soc 1999, 121 8133 8143. (228) McQuarrie, D. A. Statistical Mechanics ; University Science Books, 2000. (229) J. Comput. Chem. 1995, 16 1522 1542. (230) Wang, J.; Morin, P.; Wang, W.; Kollman, P. A. J. Am. Chem. Soc. 2001, 123 5221 5230. (231) Macke Thomas J.; Case David A. In Molecular Modeling of Nucleic Acids ; ACS Sy mposium Series; American Chemical Society, 1997; Vol. 682, pp. 379 393. (232) Tan, C.; Tan, Y. H.; Luo, R. J. Phys. Chem. B 2007, 111 12263 12274. (233) Tan, C.; Yang, L.; Luo, R. J. Phys. Chem. B 2006, 110 18680 18687. (234) Luo, R.; David, L.; Gilson, M. K. J. Comput. Chem. 2002 23 1244 1253. (235) Baker, N. A.; Sept, D.; Joseph, S.; Holst, M. J.; McCammon, J. A. Proc. Natl. Acad. Sci. 2001 98 10037 10041. (236) Konecny, R.; Baker, N. A.; McCammon, J. A. Comput. Sci. Discov. 2012, 5 015005. (2 37) Mongan, J.; Simmerling, C.; McCammon, J. A.; Case, D. A.; Onufriev, A. J. Chem. Theory Comput. 2007, 3 156 169. (238) Hawkins, G. D.; Cramer, C. J.; Truhlar, D. G. Chem. Phys. Lett. 1995 246 122 129. (239) Nguyen, H.; Roe, D. R.; Simmerling, C. J. Chem. Theory Comput. 2013, 9 2020 2034.
223 (240) Gleeson, M. P.; Gleeson, D. J. Chem. Inf. Model. 2009, 49, 1437 1448. (241) Walker, R. C.; Crowley, M. F.; Case, D. A. J. Comput. Chem. 2008 29, 1019 1031. (242) Dewar, M. J. S.; Zoebisch, E. G.; Healy, E. F.; Stewart, J. J. P. J. Am. Chem. Soc. 1985 107 3902 3909. (243) Dewar, M. J. S.; Thiel, W. J. Am. Chem. Soc. 1977, 99 4899 4907. (244) Stewart, J. J. P. J. Comput. Chem. 1989, 10 209 220. (245) Hansen, J. P.; McDonald, I. R. Theory of Simple Li quids ; Academic Press, 2006.
224 BIOGRAPHICAL SKETCH Bill Miller III was born in Fort Scott, Kansas in 1985 to Bill and Robin Miller, Jr. He has one sibling, his sister Crystal. He grew up in Nevada, a small town in southwest Missouri. He attended Nevada High School where Mr. Brian Norton first introduced him to chemistry during his junior year. He graduated valedictorian from Nevada High in 2004, and proceeded to Truman State University, a small liberal arts university in Kirksville, Missouri. At Truman S tate, he became active in computational chemistry research working under the advisement of Dr. Maria Nagan. He graduated summa cum laude from Truman State in 2008, and then moved to Gainesville, Florida to work with Dr. Adrian Roitberg at the University of Florida. He received his Ph.D. from the University of Florida in the summer of 2013. In June 2007, he married his loving wife, Ryan. They were blessed with the arrival of their daughter, Rylee Ann Miller, in June 2010. And they recently had their second c hild, Billy Ray Miller IV, in December 2012.
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