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Intrinsic Disorder In Proteins

Permanent Link: http://ufdc.ufl.edu/UFE0021412/00001

Material Information

Title: Intrinsic Disorder In Proteins Folding of the Yeast Proteinase Inhibitor IA3
Physical Description: 1 online resource (201 p.)
Language: english
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: calorimetry, disordered, floccinaucinihilipilification, ia3, idp, inhibitor, intrinsic, itc, nmr, protein, proteinase, tjump, unstructured, ypra
Biochemistry and Molecular Biology (IDP) -- Dissertations, Academic -- UF
Genre: Medical Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Intrinsic disorder is a common feature in proteins yet is not completely understood. Analysis of the folding of simple proteins may hold the key to understanding the benefits of intrinsic disorder. IA3 is a 68 amino acid endogenous inhibitor of yeast aspartic proteinase A (YPrA). In solution IA3 can be found as an unstructured protein; however in the presence of YPrA, the N-terminus of IA3 can be found as a helical structure bound to YPrA. This transition can be induced by the addition of the alcohol cosolvent 2,2,2-trifluoroethanol (TFE). Circular dichroism studies of IA3 in the presence of TFE indicated that the refolding midpoint between the coil and helix state is 16.3% TFE. However, these studies cannot conclusively state which residues are involved in the folding. Comparison of the 2D 15N-HSQC spectra of IA3 in water and in 23% TFE indicated that the protein undergoes an unstructured to structured transition in the presence of TFE. The refolding midpoint found by NMR is 18.3%, in agreement with the CD studies. The NMR studies allowed a residue level look how individual amino acids move toward a helical structure, with a greater helical tendency at the N-terminus. Inhibition assays of YPrA with several IA3 constructs show that only the N-terminal half of the protein is necessary to inhibit YPrA with the same specificity as the wild type protein. Isothermal titration calorimetry was employed to understand the function of the C-terminal portion of the molecule within its biological context. These studies demonstrated a marked difference in interactions between the wild type protein and the N-terminal half of IA3 with YPrA, suggesting that the C-terminus plays a role in the interaction between the two proteins apart from inhibition. Assessment and comparison of the folding rates within a biological context and in TFE may shed light on the validity of the models used to understand such a simple system. Laser-induced temperature jump experiments were used to determine if the folding rate of IA3 changes in the presence of YPrA. This research demonstrates that the intrinsically disordered IA3 displays a rich set of interactions with YPrA.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Edison, Arthur S.
Local: Co-adviser: Hagen, Stephen J.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2008-11-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0021412:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021412/00001

Material Information

Title: Intrinsic Disorder In Proteins Folding of the Yeast Proteinase Inhibitor IA3
Physical Description: 1 online resource (201 p.)
Language: english
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: calorimetry, disordered, floccinaucinihilipilification, ia3, idp, inhibitor, intrinsic, itc, nmr, protein, proteinase, tjump, unstructured, ypra
Biochemistry and Molecular Biology (IDP) -- Dissertations, Academic -- UF
Genre: Medical Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Intrinsic disorder is a common feature in proteins yet is not completely understood. Analysis of the folding of simple proteins may hold the key to understanding the benefits of intrinsic disorder. IA3 is a 68 amino acid endogenous inhibitor of yeast aspartic proteinase A (YPrA). In solution IA3 can be found as an unstructured protein; however in the presence of YPrA, the N-terminus of IA3 can be found as a helical structure bound to YPrA. This transition can be induced by the addition of the alcohol cosolvent 2,2,2-trifluoroethanol (TFE). Circular dichroism studies of IA3 in the presence of TFE indicated that the refolding midpoint between the coil and helix state is 16.3% TFE. However, these studies cannot conclusively state which residues are involved in the folding. Comparison of the 2D 15N-HSQC spectra of IA3 in water and in 23% TFE indicated that the protein undergoes an unstructured to structured transition in the presence of TFE. The refolding midpoint found by NMR is 18.3%, in agreement with the CD studies. The NMR studies allowed a residue level look how individual amino acids move toward a helical structure, with a greater helical tendency at the N-terminus. Inhibition assays of YPrA with several IA3 constructs show that only the N-terminal half of the protein is necessary to inhibit YPrA with the same specificity as the wild type protein. Isothermal titration calorimetry was employed to understand the function of the C-terminal portion of the molecule within its biological context. These studies demonstrated a marked difference in interactions between the wild type protein and the N-terminal half of IA3 with YPrA, suggesting that the C-terminus plays a role in the interaction between the two proteins apart from inhibition. Assessment and comparison of the folding rates within a biological context and in TFE may shed light on the validity of the models used to understand such a simple system. Laser-induced temperature jump experiments were used to determine if the folding rate of IA3 changes in the presence of YPrA. This research demonstrates that the intrinsically disordered IA3 displays a rich set of interactions with YPrA.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Edison, Arthur S.
Local: Co-adviser: Hagen, Stephen J.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2008-11-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0021412:00001


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1 INTRINSIC DISORDER IN PROTEINS: FOLDING OF THE YEAST PROTEINASE INHIBITOR IA3 By OMJOY KUMAR GANESH A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

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2 2008 Omjoy Kumar Ganesh

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3 To God through whom all things are possible, my wife, father and mother, who constantly provided care and patience along my journey, and to the Edison, Hagen and Long labs w ho have provided a scientific community in which to grow

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4 ACKNOWLEDGMENTS My journey through graduate sc hool has not been an easy one. Through the years, several people have not only watched or com mented on the struggle, but helped me in various ways. I genuinely feel that I would not have made it through this ordeal wit hout this assistance. My first acknowledgement (which is more than simple thanks) is to God. Without his guidance, my life would not be what it is t oday. Any achievements I have, any awards or acknowledgements that I ha ve gained, are all due to his hand in my life. During my time in graduate school I gained a companion in life through marriage to Katia T.C. Ganesh. Her constant love and support have kept me afloat even through extremely trying times. Through marriage, I also became the pr oud owner of two peculiar cats who unknowingly have aided in keeping me sane. My parents, Dr. T.S. Ganesh, and Dr. Lalitha Ganesh provided support and bits of wisdom along with the perp etual, "When will you gr aduate?" and "Why are they keeping you so long?" They instilled me w ith the idea that I could accomplish great things. Additionally, I wish to thank my sisters, Shan ti and Geeta Ganesh, who have gone on to become doctors of a different sort. I am exceedingly grateful to Dr. Arthur Edison and Dr. Stephen Hagen for guiding me through my graduate career. Both of them ope ned their wealth of knowledge to me, providing me with space to grow and learn scientificall y. Their perpetual pushing has made me into a better scientist, sharpening my abilities and firming the foundation of my scien tific career. I feel that I have been under th e tutelage of two top-notch, talent ed scientists who have given me priceless instruction. I thank the members of my committee, Dr s. Ben Dunn, Joanna Long, Linda Bloom, and Adrian Roitberg. Their suggestions and guida nce and encouragement have greatly aided my graduate studies. I have been very fortunate to have a talented committee to guide me.

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5 Additionally, I thank th e entire Biochemistry and Mol ecular Biology department and the Interdisciplinary Program in Biomedical Sciences I have had the pleasur e of interacting with various members of the faculty and staff throughout the years. I wish to especially note Pat Jones, Regina Corns, Terry Rickey, Denise Me sa, Brad Moore, Valerie Cloud-Driver, and Susan Gardner who have provided me with a great deal of assistance. The friendship of the many people who have come through the Edison, Hagen and Long laboratories made my time at UF not only enjoya ble, but fun and exciting. Camaraderie between everyone really does help make tough days and long hours more worthwhile. There are almost too many people to list here, but some of the mo st important are: Cher ian Zachariah, Ramazan Ajredini, Iman Al-Naggar, Aaron Dossey, Heat her Cornnell, Seth McNeill, Vijay Antharam, Ranjani Narayanan, and Pablo Perez. Previous members of all the labs, such as Suzette Pabit, LinLin Qiu, Terry Green, Jim Smith, Julie Vanni, Jonathan Lane, and Mini Samuel-Lantiser also made my time here much more enjoyable. There are other people who also deserve ac knowledgement such Alfred Chung and the staff at the Protein Core, Jim Rocca, Al Guar ino, many undergrads and high school students, and many others. Frankly, it is impossible for me to li st all the people who have helped me, or all the ways in which people have assisted me over the years. I also need to thank my church family. Through the years I have become synonymous with the perpetual student: always here and always working on a mysterious project that involves proteins. Many people ask me wh at I study, but my church family showed real interest and concern. To list everyone in my church fam ily who has helped me would require another chapter in this dissertation; instead, I want to than k them all, and let each of them know that they have made a difference in my life.

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6 Finally I need to point out that graduate school is not as bad as it seems. While my trials may have been exceedingly tough, it is worthwhile to recognize not all things put into fires turn to ash. Fires are also used to strengthen and pur ify, thereby raising the va lue and rarity of an object. So, I must thank the University of Flor ida for providing a place where I could be tossed into a fire, yet come out stronger and better.

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7 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ........11 LIST OF FIGURES.......................................................................................................................12 LIST OF ABBREVIATIONS........................................................................................................ 15 ABSTRACT...................................................................................................................................16 CHAP TER 1 INTRODUCTION..................................................................................................................18 Introduction................................................................................................................... ..........18 Defining Intrinsic Disorder.....................................................................................................18 Biological Importance of Disorder......................................................................................... 19 Disorder Is a Common Feature in Bi ological Processes: HMGA Proteins ..................... 20 Disorder Found in Disease: CVD....................................................................................20 Tools of the Trade: Investigating Disorder and Disorder to Ordered Transitions in Proteins ....................................................................................................................... .........21 Bulk Properties................................................................................................................21 Susceptibility to proteases........................................................................................21 Gel filtration............................................................................................................. 22 Circular Dichroism spectroscopy.................................................................................... 22 Isothermal Titration Calorimetry..................................................................................... 23 Monitoring Changes Through Fluorescence................................................................... 23 Intrinsic fluorescence............................................................................................... 23 Fluorescence resonance energy transfer................................................................... 24 Structure at the Molecular Level..................................................................................... 25 X-Ray crystallography.............................................................................................25 Nuclear magnetic resonance.....................................................................................26 Other Tools.................................................................................................................... ..27 Understanding Disorder......................................................................................................... .27 The IA3YPrA System............................................................................................................28 General Overview of Aspartic Proteinases......................................................................29 Yeast Proteinase A..........................................................................................................29 Yeast Proteinase A Inhibitor: IA3....................................................................................31 Primary Goals of This Dissertation........................................................................................ 32

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8 2 CHARACTERIZING THE RESIDUE LEVEL FOLDING OF THE INTRINSICALLY UNST RUCTURED IA3..........................................................................................................38 Introduction................................................................................................................... ..........38 Experimental Procedures........................................................................................................ 40 Sample Preparation..........................................................................................................40 Nuclear Magnetic Resona nce Data Acquisition .............................................................. 41 Processing and Referencing of NMR Data..................................................................... 42 Assignments and Cross Peak Tracking........................................................................... 42 SVD Analysis.................................................................................................................. 43 Fitting and Rotation Matrices.......................................................................................... 44 Results.....................................................................................................................................46 Initial NMR Analysis...................................................................................................... 46 Evaluation of SVD Analysis...........................................................................................46 TFE Dependence.............................................................................................................47 Residue Dependence of Chemical Shift.......................................................................... 48 The Third SVD Component............................................................................................49 Discussion...............................................................................................................................49 TFE Driven Transition in the NMR Data........................................................................50 Unraveling the Transition................................................................................................ 51 Different Behavior in the Two Halves of IA3.................................................................52 Additional Folding Phenomena?..................................................................................... 54 Conclusion..............................................................................................................................56 3 KINETICS OF IA3 FOLDING...............................................................................................67 Introduction................................................................................................................... ..........67 Methods..................................................................................................................................68 Choice of Donor and Acceptor Fluorophores................................................................. 68 Choice of Fluorescent Dye Position................................................................................ 70 Inhibition Constant ( Ki) Determination........................................................................... 71 Florescence Resonance Energy Transfer Determ ination................................................ 71 Circular Dichroism Data Analysis................................................................................... 71 Temperature Jump Spectroscopy.................................................................................... 73 Results.....................................................................................................................................74 Inhibition of the IA3 Mutants..........................................................................................74 Circular Dichroism Analysis........................................................................................... 75 Assessing Temperature Dependent Changes................................................................... 75 Folding Rates...................................................................................................................76 Discussion...............................................................................................................................76 Investigation of the E quilib rium Structure...................................................................... 77 Observing Fluorescence.................................................................................................. 78 Folding Kinetics of IA3...................................................................................................79 Conclusions.............................................................................................................................80

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9 4 ISOTHERMAL TITRATION CALORIMETRY OF IA3 PROTEINS IN THE PRESENCE OF YPrA............................................................................................................94 Isothermal Titration Calo rim etry of the YPrAIA3 system..................................................... 94 Introduction................................................................................................................... ..94 Choice of IA3 Proteins..................................................................................................... 95 Methods..................................................................................................................................96 Protein Preparation..........................................................................................................96 Isothermal Titration Calorimetry Experiments............................................................... 96 Isothermal Titration Calorimetry Analysis...................................................................... 97 Heat Capacity ( Cp) Determination................................................................................ 97 Circular Dichroism Experiments..................................................................................... 98 Results.....................................................................................................................................98 Optimizing the ITC Curves............................................................................................. 98 Investigating the Heat Capacity....................................................................................... 99 Examining the Stoichiometry........................................................................................ 100 Discussion.............................................................................................................................100 IA3 Structure Before Binding to the Protease................................................................ 100 IA3 Structure After Binding to YPrA............................................................................ 100 Potency of WT, Nterm, WT(his)................................................................................... 101 Heat Capacity ( Cp) Value Comparison....................................................................... 101 N -value Comparisons ....................................................................................................103 N-terminus Versus C-terminus...................................................................................... 104 Conclusion............................................................................................................................105 5 METHODS AND PR OT OCOLS......................................................................................... 118 IA3 Purification.....................................................................................................................118 Purifying IA3 Histidine Tagged Proteins.......................................................................118 Working with IA3...................................................................................................118 Purifying IA3..........................................................................................................119 Purification protocol............................................................................................... 119 Purifying WT IA3..........................................................................................................120 WT histidine tag free IA3.......................................................................................120 The protocol...........................................................................................................121 Cysteine mutants and dimer formation.................................................................. 122 Dansyl Labeling IA3.............................................................................................................124 Specifics of the Reaction............................................................................................... 124 The Protocol..................................................................................................................124 Working with YPrA..............................................................................................................125 Purifying YPrA..............................................................................................................125 Why is purification necessary?.............................................................................. 125 Purification concerns.............................................................................................. 125 The protocol...........................................................................................................126 The Stability of YPrA....................................................................................................126 Temperature stability of YPrA............................................................................... 126 Stability of the YPrA and IA3 complex..................................................................127

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10 Circular Dichroism experiments........................................................................................... 127 What Is CD Used For?..................................................................................................127 Experimental Setup....................................................................................................... 128 ITC experiments...................................................................................................................128 What Is ITC?.................................................................................................................128 The ITC Instrument.......................................................................................................129 An ITC Experiment.......................................................................................................129 Analyzing ITC Data......................................................................................................131 6 FUTURE DIRECTIONS AND CONCLUSIONS............................................................... 148 Future Directions..................................................................................................................148 N-terminal IA3 Kinetics................................................................................................. 148 Calorimetry.................................................................................................................... 148 Inactive YPrA................................................................................................................149 C-terminal EPR Spectroscopy....................................................................................... 149 Conclusions...........................................................................................................................150 APPENDIX PROGRAMMING CODE...........................................................................................................154 Why Programming?..............................................................................................................154 One Program to Rule Them All?.......................................................................................... 155 The Whole Shebang.............................................................................................................. 155 bash Scripting................................................................................................................155 NMR indirect referencing......................................................................................155 Web Scraping Using Ruby (via bash)....................................................................158 Perl Programming..........................................................................................................159 Sequence dependent chemical shifts...................................................................... 159 Database of random coil values............................................................................. 163 Tcl/Tk............................................................................................................................174 NMRView panel placement script......................................................................... 174 NMRView assignment helper script...................................................................... 175 MATLAB Scripts.......................................................................................................... 177 Curve fitting........................................................................................................... 177 Bootstrapping code.................................................................................................177 LIST OF REFERENCES.............................................................................................................179 BIOGRAPHICAL SKETCH.......................................................................................................200

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11 LIST OF TABLES Table page 2-1 Computed G0, m -values, and TFEmidpoint.........................................................................57 3-1 Distances from tryptophan residues in YPrA to Lys16 in IA3...........................................81 3-2 Binding constant values of IA3 to YPrA............................................................................82 3-3 The TFE folding midpoint values at 25C......................................................................... 83 3-4 Folding and unfolding rates for the IA3 helix.................................................................... 84 4-1 Properties of the proteins in this study.............................................................................107 4-2 Heat capacity of binding and n-valu es for IA3 derived from ITC data............................ 108

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12 LIST OF FIGURES Figure page 1-1 Ramachandran diagram of the N-terminal residues of IA3................................................33 1-2 Proton transfer 'push-pull' mechan ism used in aspartic proteinases ..................................34 1-3 Activation of ProYPrA to mature active YPrA................................................................. 35 1-4 Interplay between YPrA, YPrB and YPrC........................................................................ 36 1-5 Views of IA3 bound into YPrA from the 1DPJ crystal structure.......................................37 2-1 Changes in the chemical shifts, in response to increasing TFE concentration.................. 58 2-2 Relative weighting of the SVD components obtained from the 1HN and 15N chemical shifts...................................................................................................................................60 2-3 Comparison of the first three components of the SVD to the higher order com ponents...61 2-4 First three components of the TFE dependence of the chem ical shifts............................. 62 2-5 Computed high and low TFE spectra................................................................................. 63 2-6 Experimental and reconstructed 15N-HSQC spectra..........................................................64 2-7 Analysis of the third SVD component...............................................................................65 3-1 Simple folding and binding m odels f or the interaction of IA3 with YPrA........................ 85 3-2 Several views of the IA3YPrA complex illustrating the placement of IA3 residues within 'pockets' in YPrA.................................................................................................... 86 3-3 Fluorescence in the presence or absence of the d onor or acceptor ....................................87 3-4 CD spectra gathered as a f unction of TFE and temperature ..............................................88 3-5 Typical fit to the far-UV CD spect ra using the global two-state fit ................................... 89 3-6 Folded and unfolded CD spectra of IA3 as obtained from a global two-state fit............... 90 3-7 Contour plots of the free energy of unf olding and fraction of molecules th at are unfolded as a function of temperature and percentage of TFE.......................................... 91 3-8 Plot of the observed relaxation rate, fold ing rate and unfolding rate as a function of TFE ....................................................................................................................................92

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13 3-9 Plot of the relaxation rates at vari ous TFE concentrations as a function of tem perature.................................................................................................................... ....93 4-1 Schematic representations of the IA3 proteins.................................................................109 4-2 Several views of YPrA illustrating the surface charge on the protease........................... 110 4-3 Representative ITC result for 13 M WT IA3 titration into 1 M YPrA at pH 4.5...........111 4-4 Typical ITC result of 13 M W T(his) IA3 titration into 1 M YPrA at pH 4.5................112 4-5 Typical ITC result for the 13 M N-term inal IA3 titration into 1 M YPrA at pH 4.5..... 113 4-6 Change in enthalpy of binding values for the three proteins...........................................114 4-7 Plots of the stoichiometry value for IA3 protein to YPrA................................................ 115 4-8 Circular dichroism spectra for WT(his) IA3....................................................................116 4-9 Possible models for IA3 binding...................................................................................... 117 5-1 Representative 10% SDS-PAGE protein gel for several of th e histidine tagged IA3 proteins.............................................................................................................................133 5-2 Anion exchange chromatography of WT IA3 (without histidine tag).............................. 134 5-3 Purified IA3 WT(his) protein elution pr ofile from an S75 column.................................. 135 5-4 Elution profile of a cysteine mutant of IA3......................................................................136 5-5 A 7% native gel of several IA3 proteins illustrating the differences between cysteine mutants and WT(his) IA3.................................................................................................137 5-6 SDS-PAGE and native gel resu lts for several IA3 mutants............................................. 138 5-7 Nucleophilic substitution reaction which labels cysteine with a dansyl m oiety.............. 139 5-8 Purifying labeled IA3.......................................................................................................140 5-9 Gel analysis of YPrA te mperature stability..................................................................... 141 5-10 Densitometry results........................................................................................................142 5-11 YPrAIA3 complex stability............................................................................................. 143 5-12 Typical CD spectra for various secondary structures......................................................144 5-13 CD wavelength spectra of WT(his) IA3 in 15% TFE...................................................... 145

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14 5-14 CD temperature scan at a wa velength of 222 nm of WT(his) IA3 in 15% TFE..............146 5-15 An ITC instrument......................................................................................................... ..147

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15 LIST OF ABBREVIATIONS 1,5-IAEDANS 5((((2iodoacetyl)am ino)et hyl)amino)naphthalene1sulfonic acid CD Circular dichroism DSC Differential scanning calorimetry EPR Electron paramagnetic resonance ER Endoplasmic reticulum his6 tag C-terminal addition to IA3 with the amino acid sequence LEHHHHHH HSQC Heteronuclear single quantum coherence IA3 Inhibitor of YPrA IAEDANS 1,5-IAEDANS IDP Intrinsically disordered protein ITC Isothermal titration calorimetry NMR Nuclear magnetic resonance PMSF phenylmethanesulphonylfluoride or phenylmethylsulphonyl fluoride (a serine protease inhibitor) RF Radiofrequency SVD Singular value decomposition TFE 2,2,2-trifluoroethanol UF University of Florida YPrA Yeast proteinase A, Yeast protease A

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16 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy INTRINSIC DISORDER IN PROTEINS: FOLDING OF THE YEAST PROTEINASE INHIBITOR IA3 By Omjoy Kumar Ganesh May 2008 Chair: Arthur S. Edison Cochair: Stephen J. Hagen Major: Medical Sciences -Biochemisty and Molecular Biology Intrinsic disorder is a comm on feature in proteins yet is not completely understood. Analysis of the folding of simple proteins ma y hold the key to understanding the benefits of intrinsic disorder. IA3 is a 68 amino acid endogenous inhibito r of yeast aspartic proteinase A (YPrA). In solution IA3 can be found as an unstructured pr otein; however in the presence of YPrA, the N-terminus of IA3 can be found as a helical structur e bound to YPrA. This transition can be induced by the addition of the alc ohol cosolvent 2,2,2-trifluoroethanol (TFE). Circular dichroism studies of IA3 in the presence of TFE indicated that the refolding midpoint between the coil and helix state is 16.3% TFE. However, these studies cannot conclusively state which resi dues are involved in the foldi ng. Comparison of the 2D 15N-HSQC spectra of IA3 in water and in 23% TFE indicated that the protein undergoes an unstructured to structured transition in the pr esence of TFE. The refold ing midpoint found by NMR is 18.3%, in agreement with the CD st udies. The NMR studies allo wed a residue level look how individual amino acids move toward a helical structure, with a greater helical tendency at the Nterminus.

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17 Inhibition assays of YPrA with several IA3 constructs show that only the N-terminal half of the protein is necessary to inhibit YPrA with the same specificity as the wild type protein. Isothermal titration calorimetry was employed to understand the function of the C-terminal portion of the molecule within its biological cont ext. These studies demonstrated a marked difference in interactions between the wild type protein and the Nterminal half of IA3 with YPrA, suggesting that the C-termi nus plays a role in the interaction between the two proteins apart from inhibition. Assessment and comparison of the folding rate s within a biological context and in TFE may shed light on the validity of the models us ed to understand such a simple system. Laserinduced temperature jump experiments were us ed to determine if the folding rate of IA3 changes in the presence of YPrA. This research demonstrates that the intrinsically disordered IA3 displays a rich set of interactions with YPrA.

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18 CHAPTER 1 INTRODUCTION Introduction A m odern understanding of the protein sequence-structure relationship was first formally described by Christian Anfinsen in the 1960s ( 1). In his experiments, he noted that an unfolded protein chain can refold to its unique 3D structure spontaneously and reproducibly ( 2, 3). This has led to a dogma in molecular biology: sequence begets structur e and structure begets function ( 4-6 ). Since then, vast strides have been made in our understanding of protein function and structure. However, this viewpoint has led to a greater focus on globular or well-structured proteins, progress in the study of unstructured or intrinsically disordered proteins (IDPs) has been much more gradual ( 7). In this dissertation, I will focus on th e intrinsically disordered protein IA3. This protein is an endogenous inhibitor of the aspartic protei nase, YPrA, and binds to YPrA with nanomolar potency, with the N-terminus of IA3 transitioning from disordered to a helical structure on binding ( 8-11 ). Before continuing with the IA3 story, it is important to define what intrinsic disorder is. Additionally, I will describe two ex amples of intrinsic disorder, illustrating the importance of disorder in biological systems, and will briefly describe a few tools that were used to study IA3. Finally I will present some background on the aspartic proteinases, YPrA, and IA3. Defining Intrinsic Disorder In their landm ark 1963 paper, Gopalasa mudram Narayana Iyer Ramachandran, Chandrasekaran Ramakrishnan and Viswanatha n Sasisekharan proposed the plotting of the backbone dihedral angles versus for each residue in a protein as a way to assess protein structure (now commonly known as a Ramach andran plot, Ramachandran diagram or Ramachandran map) ( 12). In well-ordered proteins, and angles (and backbone atoms) are

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19 distinctly affected by local p acking environment and consequently exhibit small amplitude motions relative to neig hboring atoms/angles ( 4, 12-14). Secondary structures formed in wellordered proteins tend to fall in several specific regions of the Ramachandran plot. Also, the ensemble of structures measured for a well-struct ured protein tends to cl uster in certain regions of the Ramachandran plot (Figure 1-1). In trinsically disordered proteins, however, are characterized by a distribution of dihedral angles that are unhindered by pack ing interactions and can vary significantly over time for e ach amino acid residue, giving rise to a dynamic ensemble of interconverting structures (15-17 ). A Ramachandran plot of a disordered protein would show a diverse distribution of and angle pairs which would change over time ( 18, 19). Intrinsic disorder has been referred to by several different terms in literature: "natively/intrinsically disordered" ( 20, 21 ), "natively/intrinsically unfolded" ( 22), "natively/intrinsically unstructured" ( 5, 23), "floppy structure" ( 24), "loopy structure" ( 25), and "random coil", "coil" or "coil-like" ( 24). All of these terms are in contrast to a stable threedimensionally well-ordered protein, which Linding et al. defines as "typically contain[ing] regular secondary structures" ( 26). Comparison to well-structured proteins can lead to a simple definition of intrinsic disorder: no regular secondary structure and potentially a large amount of flexibility in the backbone and angles ( 7, 22, 26). Biological Importance of Disorder Despite a lack of three-dim ensional structure, the importance of diso rdered protein states has been gaining recognition in biochemistry. Several authors have noted that unstructured proteins play a large role in normal and a bnormal biological functions, such as protein transcription (27, 28), translation (29, 30 ), translocation ( 31), degradation ( 32), assembly ( 33, 34) and aggregation ( 32, 35 ). Additionally, many authors have no ted that disordered proteins can commonly be found in regulation, signaling and control pathways ( 36-40 ). To illustrate the

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20 commonness of disorder normally and in disease, I will focus on two examples, High Mobility Group (HMG) proteins and cardi ovascular disease (CVD). Disorder Is a Common Feature in Biol ogical Processes: HMGA Proteins The High Mobility Grou p proteins (HMG proteins) are a group of multifunctional chromosomal proteins. These proteins have been known to aid in transc ription, in addition to replication, recombination, and DNA repair ( 41 ). The high degree of fl exibility implied in their name is due to the distinguishing structural char acteristic that members of this group have very little detectable secondary structure ( 42). Binding to a substrate, however, causes HMG proteins to undergo an unstructured to structured transition, which is similar to the transition necessary for inhibitory action of IA3. HMG proteins have been shown to interact with DNA and a variety of transcription factors ( 43). An important example member of this group of proteins is HMGA, with the C-terminal tail of HMGA presenting an interesting story. The C-terminus of HMGA is not necessary for binding to transcription factors and other proteins, however it has been found to enhance transcription factor activity ( 44, 45). This effect may be similar to the C-terminus of IA3, which is not needed to inhibit YPrA, but ma y enhance the interactio ns between YPrA and the inhibitory N-terminus of IA3. This will be further touched upon in Chapter 4. Disorder Found in Disease: CVD Diseases of the heart and arteries are ge nerally grouped under the term Cardiovascular Disease (CVD). Among the diseases known as CVD are heart attack, stroke, heart disease, high blood pressure, heart failure, and atherosclerosis ( 46). CVD is currently the leading cause of death for individuals in the United States, affecting more that 910,000 people each year ( 46, 47). It was estimated that in 2005 around $450 billion was spent on CVD treatment and therapy ( 46 ). Currently there are around 400 prescription drugs for treatment of CVD targeting various risk factors associated with the disease ( 48).

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21 Several authors have shown that amino acid sequences for CVD related proteins that encode for disordered regions in a protein are co nsiderably different from those that encode for ordered regions ( 6, 20, 22, 49 ). The differences lie in severa l factors, including local amino acid composition, flexibility, hydropathy, and charge ( 6, 20, 22, 49-51 ). Disordered regions in proteins are characterized by low complexity primary sequences which are biased towards disorder-promoting residues, and depleted in order-promoting residues. Cheng et al. analyzed 487 CVD-related proteins and was able to show that there exists a high degree of intrinsic disorder among CVD proteins ( 50, 52 ). Their analysis suggests th at this set of proteins was enriched in disorder-promoting residues, and de pleted in order-produci ng residues, suggesting that CVD proteins have an unstructured nature which is similar to IA3. It has been postulated that this intrinsic disorder could be important for control and re gulation of processes associated with CVD via molecular recognition ( 53). Tools of the Trade: Investigating Disorder and Disorder to Ordered Transitions in Proteins The intrinsic lack of fixed 3-D structure makes studying IDPs challenging. Many different experimental methods have been used to detect, investigate and characterize the disorder in IDPs. Several of these methods are discussed in this section. Bulk Properties Susceptibility to proteases It has long been understood that disordered regions of proteins becom e targets for proteolysis more readily th an structured regions ( 54-58). Studies of proteolytic activity have established that proteolytic site s are usually located within flex ible loop or disordered regions, are typically solvent exposed, and are ge nerally devoid of s econdary structure ( 54-62 ). Disordered regions in proteins tend to jut outward from the protein surface into the solvent environment making them more open and accessible targets for proteases as compared to the

PAGE 22

22 compactness of well-folded regions ( 56). Consensus in the literature indicates that a protein that is folded into a complex structure tends to be resistant to proteo lysis (see for example ( 54, 55, 58, 63)). In this manner, susceptibil ity to proteolysis can crudely distinguish between ordered and disordered proteins ( 54, 57, 60, 64, 65). Gel filtration Size exclusion chrom atography (also called gel fi ltration chromatography) is often said to separate proteins based on 'size'. For a gl obular protein, an increased 'size' can roughly correspond to an increased molecular mass ( 66). However, this correlation does not hold with intrinsically disordered proteins. In actuality, size exclusion chromatogr aphy separates proteins based on hydrodynamic volume ( 67, 68). Several authors have noted that disordered proteins possess hydrodynamic properties which are significantly different than globular proteins. Due to their extended conformations, the hydrodynamic radius of disordered proteins is generally larger than the hydrodynamic radius of globular prot eins. This extended hydrodynamic volume leads to abnormal movement through a gel filtration colum n, and results in a disordered protein eluting at an apparent molecular mass which is larger than what would be expected. This is examined in Chapter 5. Circular Dichroism spectroscopy Circular dichroism (CD) spectroscopy can be used to monitor secondary structure through the differential absorption of right and left hande d circularly polarized li ght. Chiral compounds will interact with circul arly polarized light (69). In a protein, there are two optically active centers: side groups of aromatic amino residues, and peptide bonds Far-UV CD (wavelengths of 180 nm to 250 nm) highlights the symmetry acr oss the peptide bond a nd is indicative of secondary structure within the protein ( 70). Distinct CD spectra can be obtained for the alphahelix, beta-sheet, and random coil secondary structures ( 71). Disordered pr oteins generally

PAGE 23

23 exhibit a large negative ellipticity below 200 nm, however the spectra can also display features characteristic of other sec ondary structural elements (70). CD spectroscopy of IA3 was used extensively throughout the many experimental studies detailed in this dissertation. CD has been used as an indicator of the unstructured nature of IA3 exhibiting a negative elli pticity below 200 nm, as in Chapter 4. The unstructured to structured transition of IA3 by NMR is compared to CD results and discussed in Chapter 2. Finally, these structural changes were investigat ed using CD and correlated to te mporal events as discussed in Chapter 3. Isothermal Titration Calorimetry Isotherm al titration calorimetry (ITC) measur es the heat change upon binding of two (or more) reactants ( 72). While ITC cannot directly measure disorder within a protein, it can be used to report on the interactions between two proteins. The rele ase or absorption of heat in a chemical reaction can be an indication that the reactants are interacting ( 72 ). Nonspecific interactions between disordered proteins or disordered regions of proteins and other proteins would result in a heat change in the system which can be monitored by ITC ( 72, 73). The Nterminus of the intrinsically disordered IA3 does bind to YPrA, while the C-terminus appears unstructured and does not seem necessary for inhi bition. The energetics of this interaction are discussed in Chapter 4. Add itionally, a more detailed descri ption of ITC studies of IA3 can be found in Chapter 5. Monitoring Changes Through Fluorescence Intrinsic fluorescence Fluorescence spectroscopy techniques have b een widely used to m onitor structural transitions within proteins. Aromatic am ino acids are the main intrinsic fluorescent chromophores within a protein ( 74). Tryptophan is more ofte n used as a fluorescent

PAGE 24

24 chromophore (rather than other aromatic amino aci ds), since the quantum yield of phenylalanine is low in comparison and tyrosine fluorescence can be quenched due to many factors, including its ability to ionize or its proximity to carboxyl groups ( 75). The emission characteristics of tryptophan ar e highly dependent on the polarity of the residue's local environment. In a nonpolar environment, tryptophan has an emission maximum around 320 nm; in a polar environment the emission maximum is close to 350 nm ( 75-77). While this wavelength change does not demonstrate that a protein is disord ered, it can yield basic information about when the residue is in different local environment conditions ( 75-79 ). IA3 does gain structure upon the addition of the alcohol cosolvent trif luoroethanol (TFE), which can be monitored by intrinsic fluorescence. In Chapter 3, the in trinsic fluorescence of tryptophan as well as the adde d dansyl fluorophore are used to monitor thes e equilibrium changes. Fluorescence resonance energy transfer Fluorescen ce resonance energy transfer (FRET) ha s been a very useful tool in the study of protein behavior ( 80 ). FRET describes energy transfer between two ch romophores through resonance (79-81 ). The basic idea is the interaction is similar to two oscillators that are separated by a small distance ( 79 ). One oscillator is in an ex cited state, which results in an electromagnetic field. This field can induce os cillation with the same frequency in the nonexcited oscillator. This idea forms the basis of energy transfer between the donor and acceptor chromophores, however in FRET the chromopho res are dipoles with matching resonance frequencies. The transfer of energy between the donor and acceptor chromophore in a dipoledipole interaction is a resonance phenomenon. FRET has been called a molecular ruler, since it is sensitive to distance as efficiency of energy transfer depends on the distance between the donor and acceptor chromophore ( 79). The

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25 efficiency of energy transfer is proportional to the inverse sixth power of the distance between the donor and acceptor chromophore. FRET is routinely used to measure distances on the order of 10 to 100 FRET can be used to monitor the distance betw een residues in a disordered protein under different solvent environments, or through a temperature change. These changes can be followed as a function of time yielding rate inform ation about local structure formation. This is useful in the comparison of the folding of IA3 in TFE or in the presence of YPrA as discussed in Chapter 3. Structure at the Molecular Level X-Ray crystallography X-ray crystallography can be us ed on protein crystals to determ ine the arrangement of atoms within the protein. The technique require s that a protein sample can be crystallized. However, disordered regions in proteins represent imperfecti ons to the regular repeating structural units within a protein crystal. Processing crystallographic data and assigning atoms in space requires similarity/regularity in the cr ystallographic units within the crystal ( 82 ). Regions with high similarity between crystallographic units reveal an average high electron density aiding in molecular assignments. It is believed that disordered regions with in a protein can adopt conformations which are different between crysta llographic units, and leads to a diffuse electron density ( 83). Diffraction methods to obtain high resolution structural in formation on disordered or partially disordered proteins often utilize antibodies or binding partners ( 84 ). Structures derived from these techniques are still suspect, however, since molecu lar interactions between the antibody or binding partners and the protein of inte rest are necessary to 'freeze out' a particular member of the conformational ensemble ( 85). The crystal structures of IA3 are all in the

PAGE 26

26 presence of YPrA. In these structures only the N-terminus of the protein is determined indicating that the C-terminus may still be unstructured. Nuclear magnetic resonance The nuclei of atom s with spin angular moment um can be affected by magnetic fields; if they are placed in a magnetic field, they will align to that field ( 86). In an NMR experiment, pulses of radio frequency (RF) energy are used to 'knock' the nuclei from the aligned equilibrium positions in the field ( 86 ). The nuclei respond by precessi ng around the axis of the magnetic field, and relaxing back toward that equilibrium (87). The frequency of this precession is characteristic of the physical and chemical enviro nment for each particular atom. Using different NMR experiments, with different RF pulses, para meters such as distances between atoms, the local chemical environment of an atom and th e number of bonds between two atoms can be assessed ( 86, 87 ). This information can be used to de termine the placement of atoms within a protein structure or how a pr otein structure is changing w ith changes to the solution environment. In their 2005 paper on the Database of Protein Disorder, Vucetic et al. stated: "NMR spectroscopy provides the most di rect estimation of the internal motions that characterize disordered ensembles and can be used to identif y each residue lacking fixed structure if peak assignments are made."( 88, 89) Characterization of residue m ovements can still be made even without clear assignments of all residues. Both global analysis and individual residue characterization of IA3 in the presence of TFE was carried out and is discussed in Chapter 2. The advantage to using NMR ove r other techniques such as CD is that NMR can provide residue level information th at other techniques cannot ( 88-90 ), and can also be used to measure dynamic rates. CD and related t echniques rely on datasets that represent an average of the whole protein structure, and may miss sma ll variations in structure, as they may represent only a small

PAGE 27

27 fraction of the total dataset ( 88 ). In comparison to X-ray crysta llography, NMR is more suitable for investigating the relationship between structure and dynamics within a protein. Other Tools There are many other techniques an d tools which can be used to investigate disorder such as radius of gyration, optical ro tary dispersion (ORD), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and others. Each of these techniques has certain positives and negatives, exploiting different propert ies to probe disordered proteins. Understanding Disorder In the 1970s, Peter Chou and Gerald Fasm an ex amined the occurrence of particular amino acids in secondary structures solved by x-ray crystallography ( 91-93). This led to the empirical observation that certain amino acids have a propensity for certain secondary structures. This analysis can now be used to aid in secondary structure identifica tion, but does little to identify disordered regions in proteins ( 94, 95 ). Several authors had noted that disordered regions in proteins tend to be enriched or depl eted in certain amino acids as well ( 96-98 ). Disordered regions are known to be enriched in disorder-promoting amino acids (alanine, arginine, aspartic acid, glutamic acid, glutamine, glycine, lysine, ph enylalanine, and serine) while they are depleted in order-promoting amino acids (asparagine, cysteine, isoleucine, leucine, phenylalanine, tryptophan, tyrosine, valine) ( 96, 99, 100). Other properties have been used to identif y disordered regions in proteins also ( 101 ). Uversky et al. have noted that a characteristic of disordered proteins is a low overall hydrophobicity and large net charge ( 22, 102). Additionally, it has been noted that disordered regions have a high degree of flexibility and are found only within looping or coil regions of a protein (however these regions do not n ecessarily have to be disordered) ( 103). Prediction of these regions can aid in identifying disorder (although it is not an ab solute requirement).

PAGE 28

28 Complicating a physical understanding of disorder ed proteins is the definition of disorder itself. One would expect that a disordered protein could explore all degrees of freedom, essentially spanning all conformatio ns available. However, this is not the case as studies under very strong denaturing conditions (such as 8 M urea) have failed to observe this ( 104-106). Instead, the disordered ensemble for a particul ar IDP may contain a cer tain amount of residual structure, even under strongl y denaturing conditions ( 104, 106). Dyson et al. have postulated that due to its disorder, an IDP ma y have an advantage in making nonspecific, promiscuous contacts with other proteins which may aid in further interactions ( 107). Consider an unstructured protein as a fishing line that can cast out and explore its immediate environment (108). A structured protein woul d not be able to explore its environment as readily simply because it must diffuse through this space more slowly ( 108, 109). The center of mass of an IDP may diffuse slower due to a larger hydrodynamic radius, however this can be an advantage since an IDP can explore its environment over a shorter time than a well structured protein ( 108). This ability to explore its environm ent in combination with its ability to make nonspecific contacts with other prot eins is known as the fly-casting model ( 108-111). This model, postulated by Shoemaker et al. holds that an unstructure d protein can act much like fishing line in that the unstructured nature enha nces its ability to move through its environment to find a target protein ( 108). Initial nonspecific contacts can also be an enhancement over a rigid globular protein making specific binding contacts ( 108 ). The IA3YPrA System The focus of this dissertation is on th e intrinsically unstructured protein IA3. IA3 displays an amazing nanomolar potency towards its cogna te protease, in the process undergoing an unstructured to structured transi tion. Here I wish to touch on th e general aspects of aspartic proteinases, and then focus on specific prope rties of YPrA, and fi nally the inhibitor IA3.

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29 General Overview of Aspartic Proteinases Aspartic proteinases are im portant enzymes, characterized by an aspartic dyad that is necessary for proteolytic action ( 112). Structural studies of these enzymes show a bilobed molecule with an opening or cleft located at the interface of the two lobes ( 112, 113). Within this cleft, each lobe contains an invariant As p-Thr/Ser-Gly sequence. The Asp residues of both lobes are positioned close to each other, and both are necessary for proteolytic cleavage of a substrate. The cleft is thought to stabilize and po sition a substrate within a particular distance of the aspartic dyad ( 114). The steps involved in proteolytic cleavage by aspartic proteinases ar e known as the "pushpull proton transfer mechanism" ( 113, 115-117) (Figure 1-2). At an acidic pH, both aspartate residues form an extensive hydrogen bonding network with the solution and to each other, leaving one aspartate charged, and the other ava ilable to be deprotonated. In a single step, a proton from water can transfer to the carboxyl end of an aspart ate residue causing a proton from the carboxyl group of the neutral aspartic acid to transfer to a carbonyl gr oup in the backbone of the substrate. This protonation breaks the double bond of the carbonyl allowing a hydroxyl molecule to bond to carbon which previously had a double bond to oxygen, and now forming a tetrahedral intermediate ( 118). In a reverse step, the prot on gained by the carboxyl group of the aspartate residue in the proteas e is transferred to the backbone amide group causing a partial charge imbalance. This forces a proton tran sfer from the hydroxyl of the carbon back to the aspartic acid of the protease, allowing a carbonyl to reform and breaking th e scissile bond of the substrate. Yeast Proteinase A YPrA is synthesized from the ri bosom e as a 405 amino acid protein ( 119, 120 ). In this initial state it contains a propept ide segment that stabilizes the pr otease and inactivates the active

PAGE 30

30 site, leaving the protease inactive; this inactive form is known as a zymogen ( 121). In addition to the propeptide, there is a si gnal sequence that targets the prot ein to the endoplasmic reticulum (ER) for extensive glycosylation ( 119 ). This full sequence is refe rred to as PreProYPrA, since it contains the signal sequence (the Pr e portion), and the prosegment. Within the ER, the 22 amino acid signal seque nce is removed, and oligosaccharides are added to the protein through several post-translational modifications ( 122 ). These oligosaccharides are processed as the protein moves through the ER into the golgi complex and is shuttled into an acidic vacuole ( 119, 122 ). Two processes could o ccur within this acidic environment to activate the protease. Interact ion with YPrB could cleave the 54 amino acid prosegment, or autoactivation of YPrA could resu lt in a pseudo-YPrA which is able to cleave the prosegment from other YPrA molecules ( 123, 124) (Figure 1-3). Both pr ocesses will result in the same outcome, a mature and active 42 kDa pr otein (of 329 amino acids) which is localized to an acidic vesicle ( 125). In the vacuole, YPrA could serve several diffe rent roles. There does exist some interplay between several yeast proteina ses including YPrA, YPrB and YPrC (Figure 1-4). These proteinases can interact to activate one another by cleaving prosegments from inactive forms of the 'partner' protease. It has also been s hown that YPrA can activate phosphofructokinase and aminopeptidase 1, as well as inte ract with other hydrolases ( 126-129). The activity of YPrA with these hydrolases does not stop at activation, however, since prolonged incubation of these proteins with YPrA will result in their degrad ation. Density centrifugation studies indicate that YPrA can be found bound to many di fferent cellular proteins ( 126). Although the general action of YPrA does seem to be proteo lytic degradation of pr otein substrates, it is conceivable that it

PAGE 31

31 could interact with, activate, or inactivate other proteins th rough a scheme similar to its interaction with YPrB and YPrC. Yeast Proteinase A Inhibitor: IA3 IA3 is a 7.7 kDa protein of S. cerevisiae ( 10, 130 ). The wild type protein is 68 amino acids, consisting of 47 polar residues (~70%), and six aromatic residues (~9%). The polar character of the molecule is due to thirteen lysi ne, seven aspartate, six glutamate and six serine residues. Within the sequence there are fifteen basic residues and thirteen acidic residues. While YPrA is found within the acidic vacuole, IA3 is found in the cytosol ( 131). This sequestration of the inhibitor from the protease suggests that molecules of IA3 will not encounter YPrA unless the vacuole is ruptured. If the vacuole is disrupted then formation of an IA3YprA complex could prevent cell degradation by in hibiting the proteoly tic action of YPrA. Purified IA3 is unstructured in solution ( 10, 11). Circular dichroism experiments indicate that the protein has a bulk unstructured nature ( 9). NMR experiments confirm that the general nature of the protein is unstructured with little cooperativity between residues to form secondary structure; however individual residues may ha ve a tendency towards certain structure ( 8). The IA3 protein has the distinction of being the first specific inhibitor of an aspartic proteinase to be discovered ( 130, 132). IA3 displays a remarkable specificity for YPrA, although YPrA is similiar in st ructure and function to ma ny aspartic proteinases ( 11, 133, 134 ). While the inhibitor acts as a substrate for other similar aspartic proteases, it displays a sub-nanomolar potency towards YPrA ( 11, 133, 134). Both the wild type IA3 and the truncated 2-32 N-terminus of IA3 display nanomolar potency toward YPrA, st rongly suggesting that only the N-terminal residues are needed for inhibition of the protease ( 11). The crystal structure of the IA3YprA complex shows residues 2-32 of IA3 bound as a helix to YPrA ( 10) (Figure 1-5). The IA3 helix binds to the flap region of YPrA, with several specific

PAGE 32

32 contacts forming between the protease and the inhibitor ( 10 ). The helix also displays an amphipathic character with hydrophobic resi dues facing into the active site cleft ( 10). By binding to the flap region of YPrA, IA3 effectively blockades entry of substrates into the active site cleft, thereby in hibiting the protease ( 10, 11). Additionally, it has been suggested that binding to the flap region keeps IA3 itself far enough away from th e catalytic dyad of YPrA to keep the inhibitor from being a substrate (10, 11 ). It has also been noted that IA3 could change the hydrogen bonding network ar ound the catalytic dyad ( 132 ). Finally, while there is strong evidence for the role of the N-terminus in the bi nding of the inhibitor to the protease, there is possibly also evidence that the C-terminus of IA3 could play a role in the binding affinity of IA3 to YPrA ( 135). Primary Goals of This Dissertation The unstructured nature of IA3, and the unstructured to stru ctured transition that is necessary for its mode of inhibition provides a useful system to investigate intrinsically disordered proteins and their interactions with partner proteins. Through the use of several different biophysical techniques I will follow th e structural and functional transition in the disordered IA3 protein and its binding to YPrA. Chapte r 2 of this dissertation will discuss the individual residue movements and the tr ansition involved in the folding of IA3 in the presence of the renaturant TFE using CD and NMR techniqu es. The kinetics and time resolution of the folding events of IA3 in the presence of TFE or YPrA will be examined in Chapter 3 using CD, temperature jump, and fluorescence techniques. Fi nally, the energetics of the interaction of IA3 with the protease will be expl ored in Chapter 4 using ITC.

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33 Figure 1-1: Ramachandran diagram of the N-terminal residues of IA3 (residues 3-31) showing various secondary structures. The gradations on the plot were generated from data from Richardson et al. and represent the 98% (fa vored) and 99.98% (allowed) countours for all amino acids, and for glycine separately ( 136 ). The red circles indicate clustered regions of certain secondary structures. Red dots indicate the residues of IA3 which are found in the 1DPJ crystal structure of the IA3YPrA complex and plotted onto the Ramachandran diagram. Several of the non-clustered residues are identified.

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34 Figure 1-2: Proton transfer 'pushpull' mechanism used in aspartic proteinases to cleave peptide bonds. Illustrated here is the movement of protons within this mechanism. Hydrogen bonds are shown as yellow lines.

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35 Figure 1-3: Activation of ProYPr A to mature active YPrA. Illu strated here are two pathways by which YPrA could become active: by cleav age of the prosegment by YPrB, or by the formation of PseudoYPrA which could then cleave the prosegment from ProYPrA resulting in mature, active YPrA.

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36 Figure 1-4: Interplay between YPrA, YPrB and YPrC. Inhib itors of these proteases are symbolized by black arrows. Red arrows indi cate inactivation of the inhibitor as it can be a substrate for another protease. Green arrows indicate a role of the proteases in the processing and maturation of each other.

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37 Figure 1-5: Views of IA3 bound into YPrA from the 1DPJ crystal structure. YPrA in cyan is wrapped around the IA3 N-terminal helix in yellow and green. Hydrophobic residues in the IA3 helix are indicated as green st ick projections. These views of YPrA and IA3 were generated using PyMOL.

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38 CHAPTER 2 CHARACTERIZING THE RE SIDUE LEVEL FOLDING OF THE INTRINSICALLY UNSTRUCTURED IA3 1 Introduction IA3 is a 7.7 kDa protein that is found in the cytoplasm of Saccharomyces cerevisiae and is an endogenous inhibitor of the yeast aspartic proteinase, YPrA Previous biophysical studies have shown that in the absence of YPrA, IA3 exists as an unstructured protein in solution ( 9, 10 ). In the X-ray crystal structure of the YPrAIA3 complex (PDB accession number 1DPJ), only the N-terminal residues Thr 3 through Lys 31 of the si xty-eight amino acid protein are seen; this Nterminus forms an -helix in the active site cleft of YP rA. The remaining C-terminal residues are believed to be disordered sin ce electron density from this region is not observed in the crystal structure (10 ). Only the thirty-four amino acid N-terminus of IA3 is necessary to inhibit YPrA with the same nanomolar potency as the full length wild-type IA3 ( 11). Using far-UV circular dichroism (CD) we have previously shown that 2,2,2trifluoroethanol (TFE) facilita tes a two-state transition in IA3 from unstructured protein to helix with a transition mi dpoint around 16.3% v/v TFE ( 9). Due to the global nature of CD, these studies could not establ ish which residues in the IA3 sequence became helical, or whether the transition is uniformly tw o-state for all the residues. NMR chemical shift data from 15N heteronuclear single quantum correlation (15N-HSQC) experiments on proteins contain important stru ctural information ( 137 ). Folded proteins show greater distribution of chemical shift dispersion, while spectral cr oss peaks of unfolded proteins are less dispersed with possibl e overlap in NMR frequencies ( 138, 139). Here we use chemical 1 This chapter is a published work ( Characterizing the residue level folding of the intrinsically unstructured IA3. Ganesh OK, Green TB, Edison AS, Hagen SJ.; Biochemistry. 2006 Nov 14;45(45):13585-96.), and is republished here with permission from the American Chemical Society.

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39 shift data from 15N-HSQC TFE titrations in conjunction with singular value decomposition (SVD) to monitor the transition of IA3 in the presence of TFE ( 140, 141). Assignment of the protein through triple-resonance methods at intermed iate TFE concentrations was used to aid in tracking the cross peaks as the TF E concentration varies. We also use parameters derived from a two-state fit to the TFE-driven transition to extr apolate to the high and lo w TFE states to extend our current understanding of how the protein folds. The tracks taken by individual chem ical shifts, in response to the addition of TFE contain a large amount of information, and it can be difficult to extract a clear pictur e of either the general trends or the individual, residue-l evel departures from the general trends. One approach to this problem could be to use chemical shift mapping (CSM) to identify residues that are undergoing large changes. CSM is also generally used to identify interactions betw een proteins and nucleic acids, other proteins or membranes ( 142-145). In this method, the chemical shift of each residue is assessed and mapped onto an image of the prim ary structure or the folded structure of the protein ( 142 ). In the present case, we do not have a structure for the high -TFE state, and we cannot assume that this structure is identical to the crystal structure obtained for the biologically active complex. More importantly, CSM identifies the residues involved in an interaction but does not reveal the nature of th e transition or indicate how closel y an individual residue does or does not follow the chemical shif t of other assigned residues. As an alternative, we used singular value decomposition (SVD). SVD is a mathematical tool for identifying the most impor tant patterns in a dataset ( 146). It breaks apart a twodimensional matrix of data (in this case, chem ical shifts recorded for an array of sequence positions and TFE concentrations) into a sum of simpler matrices. Each of those component matrices contains a single two-dimensional pattern, i.e. a particular pattern of chemical shifts

PAGE 40

40 versus sequence position, which varies with a pa rticular TFE dependence. The original data matrix, with all of its sequence-dependent and TFE-dependent variations, can be reconstituted exactly by adding together all of the component matrices. The primary benefit of applying SVD to a complex data set is that it essentially identif ies all the major patterns that are present in the data, and ranks them in order of descending strength or significance. SVD has been an invaluable tool in other areas of biophysical spectroscopy, such as transient absorbance spectroscopy ( 146). By applying SVD to the chemical shift data, we show that the major transition is two-state, and that it occurs in the same residues neces sary to inhibit YPrA. Additionally, another component to the folding is shown to be sensitiv e to changes in the amide proton chemical shift, and may be indicative of changes to the hydrogen bonding of backbone amide protons. A biochemical interpretation of th e residue interactions, combined with the global description provided by SVD, results in a relatively complete residue-level picture of the structural changes induced by TFE. Our results demonstrate how the application of SVD to a two-dimensional NMR dataset can efficiently extract both a detailed description of the global structural changes in a molecule and the rather small, residue-spec ific deviations from th at global behavior. Experimental Procedures Sample Preparation IA3 was expressed in E. coli and purified as previ ously described by Phylip et al. ( 11). IA3 samples were prepared at a concentration of 1 mM sample in 250 L of 50 mM PO4 buffer, pH 5.6 as described previously ( 9), with the inclusion of increasing volumes of TFE. A TFE titration from 0% to 23% v/v was made by the addi tion of constant volumes of 5 L of TFE to the IA3 sample. In addition, a fresh IA3 sample was prepared at the endpoints and middle of the TFE titration for triple resonance experiments.

PAGE 41

41 Nuclear Magnetic Resonance Data Acquisition All NMR data were co llected on Bruker Avan ce spectrometers in the Advanced Magnetic Resonance Imaging and Spectroscop y (AMRIS) facility at the Univ ersity of Florida's McKnight Brain Institute. 1H data were referenced to DSS (2,2-di methyl-2-silapentane-5-sulfonic acid) (0.0 ppm). 15N and 13C 0.0 Hz frequencies were obtained through indirect referencing to DSS using the chemical shift ratios provided by the BMRB database ( 9, 147). All NMR experiments were run at 20 C. Two-dimensional 15N-HSQC ( 148, 149) spectra of the TFE titratio ns were collected at 750 MHz on a Bruker Avance (DRX)-750 console in a 17.6 Tesla magnet using either a 2.5 mm or 5 mm TXI probe. The 1H carrier frequency was centered on water which was reduced using a 3-919 WATERGATE sequence ( 148 ). 15N-HSQC spectra were collected with 2048 complex points in the 1H dimension, and 256 complex points in the 15N dimension. Quadrature detection in the indirect dimension was achieved using the States-TPPI method ( 150). All 2D spectra were collected with a 10.69 ppm (8012.82 Hz) spectral width in the proton dimension, and 30.0 ppm (2280.30 Hz) in the nitrogen dimensi on. The carrier frequencies for 1H and 15N were centered on 4.86 ppm and 116.68 ppm respectively. Triple resonance data were collected at 600 MHz on a Bruker Avance (DRX)-600 console in a 14.1 Tesla magnet equipped with a 5 mm TXI probe. Spectra were collected with a 11.02 ppm (6613.76 Hz) spectral width in the prot on dimension, and 21.7 ppm (1315.79 Hz) in the nitrogen dimension. For the HNCACB ( 151) and CBCA(CO)NH ( 152 ) the spectral widths were 50.9 ppm (7692.31 Hz) and carrier frequencies were 42.51 ppm for the carbon nuclei. The HNCO ( 153, 154) carbonyl spectral width was 9.5 ppm (1428.57 Hz) with a carrier frequency of 176.09 ppm. Quadrature detection in the 15N dimension was achieved using the Echo-AntiEcho method ( 149, 155, 156), and in the 13C dimension using the States-TPPI method. All three-

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42 dimensional spectra were collected with 1024 complex points in the 1HN dimension. HNCO spectra were collected with 32 complex points in the 15N dimension and 30 complex points in the 13C dimension. HNCACB spectra were collected with 32 complex points in the 15N dimension and 50 complex points in the 13C dimension. CBCA(CO)NH spect ra were collected with 18 complex points in the 15N dimension and 50 complex points in the 13C dimension. Processing and Referencing of NMR Data Multi-dim ensional spectra were processed using NMRPipe ( 157 ) and the resulting spectra were analyzed with NMRView software (O ne Moon Scientific, Tr enton, New Jersey) ( 158). We applied cosine-squared apodization functions in the direct and th e indirect dimensions during processing of the 2D 15N-HSQC spectra. To increase the digital resolution, spectra were zerofilled to 4096 points in the proton dimension, a nd to 1024 points in the indirect dimension. Polynomial baseline correction was applied in both dimensions. For the CBCA(CO)NH, HNCACB, and HNCO trip le resonance spectra, the direct dimension was zero-filled to 2048 points. Cosine-squared apodization and zero-filling were applied in all dimensions. Linear prediction wa s used to expand the data in the indirect carbon dimension only. We used model-free baseline correction to remove noise from the resulting 3D spectra ( 157, 159 ). Assignments and Cross Peak Tracking Resonance assignm ents were made manually usi ng standard triple resonance methods. We tracked individual residue ch emical shifts in the 2D 15N-HSQC as a coordinate from the centroid of the cross peak. The cross peak 1HN and 15N coordinates of individual residues were tracked as they varied with TFE concentration, and displaye d as two-dimensional tracks. We tracked only cross peaks for which an unambiguous assignmen t was possible and where the cross peak could

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43 be followed through the whole TFE titration. Of the 68 residues in IA3, 40 cross peaks were assigned and tracked in this study. SVD Analysis Random -coil chemical shifts and sequence-dependent correction values were obtained from methods developed by Schwaringer et al. ( 160, 161), using a program written in PERL by the authors. The chemical shift data, after th is correction, can be represented by two separate matrices containing the 1HN and 15N values respectively. Each matrix has 40 rows corresponding to the 40 different residues tracked, and 16 co lumns corresponding to the 16 different TFE concentrations studied; the element in row i and column j then gives the value of the NMR chemical shift for residue i at TFE concentration j We then decomposed each matrix by singular value decomposition (SVD). SVD is a method fo r producing a unique fact orization of a given matrix as the product of three basis matrices ( 137, 162-164): it extracts the essential features of a data matrix, preserving the significant informa tion in the matrix while sharply reducing the number of parameters needed to represent that information. As SVD itself is a purely mathematical and model-independent procedure, it lacks intrinsic bias toward any particular interpretation of the data, and as such reliably id entifies interesting features in the data that rise above the noise background. SVD decomposes a data matrix A into the matrix product, TVSUA (2-1) where, for a matrix A of size 40 16, U is a matrix of size 40 40, V is a matrix of size 16 16, and S is a matrix of size 40 16. The 16 diagonal elements of S are the only nonzero elements and are known as the singular values, sn (with n = 1 to 16). The columns of U comprise an

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44 orthonormal set of 40-element vectors, un, (n = 1 to 40) and the columns of V comprise an orthonormal set of 16-element vectors vn (n = 1 to 16). From Equation 2-1, the data matrix A can be reconstructed exactly as a sum. 16 1 n T nnnvsuA (2-2) In our case the vectors un may be interpreted as functions of sequence position (residue number), and the vectors vn as functions of TFE concentration. The dataset can then be reconstructed from the series: ... )()()()(),(22 2 11 1 T TTFEvsresidueuTFEvsresidueuTFE residueA (2-3) Since the singular values are ordered from largest (s1) to smallest (s16), and the u and v vectors are normalized, the weight or importance of each su ccessive term in this series is smaller than that of the preceding term. More precisely, the sum of the first n terms in the series provides the best (in the least-squares sense) n-component representation of the complete dataset A (164). Fitting and Rotation Matrices If the respon se to TFE is primarily the inducti on of a two-state foldi ng transition, then this transition should be represented in the V(TFE) data and can be described by the fraction of molecules folded, Ff (165). 1exp1 RTG Ff (2-4) where G, the free energy of unfolding, is given by TFEmGG 0 in the conventional case of a linear relationshi p between folding free energy and TFE concentration (166). This twostate model requires only two free parameters, m and 0G to fit the data. A two-state transition will be represented by two SVD components. Therefore we expect the v1 and v2 vectors obtained from both the 1HN and 15N datasets to have the TFE dependence of Equation 2-4. We fit these

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45 two vectors in both datasets simultaneously in order to find the parameters. The residue dependence should then follow fnn nFbaTFEvs for the constants an, and bn, n = 1, 2. Extrapolation to TFE + or TFE should then result in limiting spectra. TFE + would correspond to a high TFE state with a large population of folded molecules (essentially 1 fF ); TFE would correspond to a low TFE state with a high population of unfolded molecules (0fF). The limiting values of v1 and v2 can be calculated as nnnbasv for the high TFE state and nnasv for the low TFE state. Combining the limiting TFE dependence with the u1 and u2 vectors produces the high and low limiting spectra. limiting spectra 2 1 21sv sv uu (2-5) The first two V(TFE) components were fit extremel y well by a two-state function (Equation 2-4). The vectors of each of the basis sets U and V produced from the SVD are orthogonal, therefore the physical phe nomena of interest in the data are most likely spread across several of the SVD components. Ac cordingly it was noted that rota tion of the components of the 1HN and 15N basis sets would minimize the deviations between the two-state fit and the first two SVD components, resulting in more accurate 0G and m-values. A rotation of the basis vectors can preserve the orthogonality of the set, while ensuring that separate physical phenomena are maximally represented in the minimal number of components (one or few). The rotation, however, is not a fit or approxi mation and does not change or reduce the information contained in the data. Rotation of V requires us to find an optimal rotation matrix R (164). To find R, we rotated the dataset while simultaneously fitting to the twostate equation (Equation 2-4). In doing so, we were able to find a new set of basis vectors that spanned the same vector space, yet also provided

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46 the best fit to the two-state equation for v1 and v2, while preserving the orthonormailty of v3. The third component would then also contain deviatio ns which did not conform to a two-state fit. Results Initial NMR Analysis Using the 15N-HSQC, individual residue chemical shifts were tracked as a 1HN or 15N coordinate and as a function of TFE. We subtracted random coil values (160) from all chemical shift values and corrected the resulting values for sequence dependent effects (161). Several residues could not be tracked thr oughout the titration, owing to ove rlap of cross peaks at some concentrations, or due to the difficulty of completely assigning all peaks in the TFE titration. However, 40 of the 68 residues in IA3 were fully tracked through the t itration. Initial analysis of these residue tracks indicates that th ere is a general tendency towards an -helical structure according to cut-off values given by Wishart and Sykes (137). As shown in Figure 2-1, the tracks of many of the residues throughout the prot ein exhibit a large curvature while moving in a generally -helical direction with the addition of TFE. 13C chemical shifts at a few TFE concentrations verify the helical transition (dat a not shown). However, several residues do not follow this general trend. Additionally, residues such as Asp 22, Asp 28, Phe 30, Ala 34, Ala 45, Glu 49, Tyr 53, and Asn 58 move in a manner contra ry to the general moti on of the other tracked residues of the protein. Evaluation of SVD Analysis The 15N and 1HN coordinates of the chemical shift tr acks were collected as separate data matrices as described in the Experimental Proced ures section. We used SVD to decompose the chemical shift data into three matrices, U, S, and V. The V matrix describes the TFE dependence of the chemical shift, with the columns of V representing the TFE dependence. The columns of the U matrix represent the residue dependence of the chemical shift. S is a diagonal matrix of

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47 singular values, with diagonal elements representing the weights for th e corresponding columns of the U and V matrices. Hence the nth column of the V matrix, together with the nth column of U and the nth diagonal element of S, together comprise the nth SVD component of the data (Equation 2-2). The weights of the first three components from the SVD describe more than 96% of the information in the chemical shift data matrix (Figure 2-2). All remaining higher order SVD components are less than 1% of th e total chemical shift data. Further, the TFE dependence and residue dependence of the hi gher components appeared rand omly distributed around zero, indicating that these components esse ntially reflect noise in the data (Figure 2-3). Therefore, in the discussion that follows we focus only on th e first three components of the SVD analysis. TFE Dependence The overall effect of TFE on th e coil to helix transition of IA3 can be evaluated from Figure 2-4. It shows the TFE dependence of the chemical shifts as a function of increasing TFE concentration for the first three components of the SVD analysis. Plots of the first three components of this TFE dependence revealed that both the 1HN and 15N exhibited similar responses to TFE. The first two components of the TFE dependence of both the 1HN and 15N chemical shifts exhibit sigmoida l trends with increasing amounts of TFE. These movements are strongly indicative of a two-state transition (167), where one state is progressively depopulated in favor of a second state as the TFE concentrat ion rises. The third co mponent in each figure exhibits a weak maximum near 15% v/v TFE. This third component corresponds to only a small portion of the signal, around 4.1% of the 1HN and 2.6% of the 15N chemical shifts (Figure 2-2). We fit the first two components of both the 1HN and 15N simultaneously to a two-state folding-unfolding model. This model implicitly assumes that the free energy of unfolding is linear in the TFE concentration. The f it yielded a unfolding free energy in water, G0, of -21.2

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48 kJ/mol with an m-value, which measures the helix stabi lizing effect of TFE, of 1.2 (kJ mol)-1 at 20C. Simultaneous fitting of the first and s econd components of the TFE dependence of the chemical shift for both 1HN and 15N cross peak movements yielde d a TFE refolding midpoint of 18.3%, which was in agreement with the TFE re folding midpoint obtained from far-UV circular dichroism (CD) studies of IA3 in increasing concentrations of TFE (9). Fitting of 1HN and 15N datasets separately yielded TFE refolding midpoi nts of 18.4% and 18.1% respectively, indicating that the model provides a robus t fit to the data (Table 1). Residue Dependence of Chemical Shift Plotting the residue dependence of the chemi cal shifts versus the amino acid sequence provides insight into the folding of IA3 at the residue level (Figure 2-4). Bar plots of the first through third components of the residue dependen ce on the chemical shift proved to be very revealing. The first component of the 1HN residue dependence indicates that IA3 exhibits a large tendency towards -helical structure throughout the length of the sequence. This tendency is present, albeit not as clearly, in the first component of the 15N residue dependence. The second component of the residue dependence for the 1HN and 15N can be broken down into distinct regions. In 1HN the N-terminal half of the protein (residue s 1-28) exhibits a negative trend. This trend appears to be minimized or reversed in th e C-terminal portion of the protein. A central region of positive values, with flanking negative va lues, can also be seen around residues 28-31. The second component of the 15N residue dependence also shows th is trend with the N-terminus showing generally larger negativ e changes as compared to the C-terminus, with residues 29 and 30 being positive. The third component residue dependence indicates negative deviations in both 1H and 15N. The limiting high and low TFE spectra obtained from the fit and the SVD components are shown in Figure 2-5. Figure 2-6 shows the 0% and 23% 15N-HSQC TFE spectra, along with the

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49 same spectra as reconstructed from just the first three SVD components (i.e. omitting all higher order components). The original and reconstructe d spectra agree well, illustrating that discarding the higher order components of the SVD has a neg ligible effect on the initial NMR data, and that the rotation included in our SVD treatment preserves all the information in the data. The Third SVD Component Panels G and H of Figure 2-4 illustrate the devi ations seen in the th ird component. All the deviations seen in this component are small in both the 1HN and 15N dimension. Residues such as Thr 3, Gly 40, Glu 49, and Tyr 53 show bars which are very small or nearly zero. A very small third component, or the apparent lack of a third component, indicates that the chemical shift tracks of these residues can be described primarily by the first and second components, both of which were well fit by a two-state transition. Ser 9, Val 26, Gly 62 and other residues which are influenced by this third co mponent are indicated by very la rge bars in panels G and H. Reconstruction of the NMR chemical shift tracking spectra based only on the third SVD component and residuals allowed us to evaluate the third component (Figure 2-7). Residues which have no third component in 1HN and 15N would be represented here as a track in which all the points lie on top of each other. Closer inspec tion of such a track would reveal that the points have small deviations from one another and are randomly distributed. Figure 2-7 provides several examples of this: Ala 23, Phe 30, Gly 40, Thr 43, Glu 49 and Tyr 53. Residues such as Ser 9, Val 26, Lys 32, Met 33, Ala 34, and Gly 62 in Figure 2-7 are examples of large deviations of the third component seen in Figure 2-4. Fr om Figure 2-7 we can see that deviations in the third component primarily manifest themselv es as chemical shift movements along the 1HN axis. Discussion The native IA3 provides an ideal model system to study the conformational features of intrinsically unstructured protei ns. It is known to be unfolde d in solution, however the N-

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50 terminus of the protein forms an -helix upon binding to YPrA (10). We have previously shown by far-UV CD that TFE can induce an -helical structure in IA3 (9), and here we expand this work using NMR spectroscopy along with SVD analys is to understand changes in chemical shift behavior to gain an insight into the folding of IA3. TFE Driven Transition in the NMR Data It is well understood that 1HN shifts are influenced by lo cal electronic environment (168) and 15N shifts are sensitive to amino acid type and the effect of neighboring amino acids. Both 1HN and 15N shifts are influenced by hydrogen bonding and the / i-1 backbone dihedral angles (169-171). In general, cross peaks which move to lower frequencies are indicative of helical structure, while cross peaks which move to higher frequencies indicate beta structure (168, 170). Subtraction of random coil and sequence depend ent effects from the chemical shift data allowed us to evaluate the change in secondary st ructure of a given residue due to the increase of TFE. According to the analysis of Wishart et al., a residue track starting near the origin and ending in the upper-right of each graph in Figure 21 would clearly indicate a transition from coil to helix. Titration tracks generated by followi ng residue chemical shifts as a function of TFE (Figure 2-1) indicate that the general chemical shift movements of most of the residues of IA3 appear to be consistent with the formation of an -helical structure as the concentration of TFE in solution is increased. Most of the paths of the individual residue 1HN and 15N cross peak titration tracks contain this positive slope movement in a 1HN and 15N plot. The residue tracks of Lys 18, Gly 21, Lys 24, Val 26, Ser 27, Lys 32, Ser 35, Lys 41, Gly 62 and Gly 64 exemplify this behavior. However, this type of analysis does not cove r the large variation in chemical shift tracks when we consider all of the tracked residues. Many of the residue chemical shift tracks maintain some degree of a positive slope as the TFE concentration is increased. This indicates that there

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51 is a large degree of helical tendency (170, 172), yet very few of these tracks have a strictly positive linear slope. In Figure 21, the residue tracks of Asp 4, Glu 10, Ser 15, Lys 16, Ala 29, and Ala 63 have slopes which are nearly horizonta l, showing that the chemical shift movement occurred in the 1HN dimension only. Val 8, Ser 9, Gln 13, and Met 33 have residue tracks which bend towards the vertical axis at later points in the titration. Mo re extreme cases of this bending can be seen in the tracks of Ala 34, Asp 45, Se r 48, and Asn 58 which bend past the vertical and turn downfield on the 1HN axis. The track of Glu 17 is nearly vertical. Also the residue tracks of Thr 3, Ser 14, Ala 23, Asp 37, Asp 39, Thr 42, Thr 43, Asp 44, and Asp 46 show positive linear slopes, yet start from positions father away from the 1HN and 15N origin thereby confusing the standard coil to helix argument. A few residue tracks are not consistent with the conventiona l coil to helix transition. Residues Asp 22, Asp 28, Phe 30, Ala 34, Ala 45, Ser 48, Glu 49, Tyr 53, and Asn 58 move in a manner completely contrary to what is expected fo r an ideal coil to helix transition. Asp 22, Asp 28, Ala 34, and Asn 58 make drastic hook-like turns as the TFE concentration increases. Ser 48, Glu 49, Tyr 53 do not have long tracks however they move downfield in 1HN and upfield in 15N. Finally, the residue track of Ph e 30 is linear but moves downfie ld as the TFE concentration increases, and is the only residue tr ack to have this kind of movement. Qualitatively, many of the chemical shifts move in a manner consistent with a coil to helix transition. Following the chemical shift tracks of each individual residue, however, it is unclear whether these shifts constitute a uniform or global, two-state transition. This led us to use SVD on the chemical shift tracking data. Unraveling the Transition In our previous study, SVD analysis of the far-UV circular dichroism data yielded basis states consistent with a high TFE folded state an d a low TFE unfolded state. In the presence of

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52 TFE, IA3 transitions from an initial disordered stat e to a helical conformation through a two-state transition, with the midpoint of this transition observed at 16.3% TFE (9). From the individual residue chemical shift titrati on curves in Figure 2-1 it is difficult to see the two-state transition. Using SVD allowed us to focus on the important cooperative transitions inherent in the chemical shift data without bei ng misled by the difference s between the chemical shift tracks of individual residues. This approach is fundamentally different from chemical shift mapping in that it allows us to identify and quantify the transition that IA3 is undergoing in increasing amounts of TFE. Additionally, the us e of SVD allowed us to compare and analyze the TFE and residue dependence inherent to the residue chemical shift movements separately. SVD analysis of the 2D 15N-HSQC data indicated that the first two components of the TFE dependence of both the 1HN and 15N chemical shifts could be f it to a two-state thermodynamic transition, as in the earlier CD study (9). We simultaneously fit thes e four datasets to yield a TFE folding midpoint of 18.3%. This value is in close agreement with th e value obtained from earlier CD studies. In Green, et al. (9) the far-UV CD studies of IA3 in increasing concentrations of TFE exhibited very broad transition s which have been known to influence G0 and m-values (173). This, coupled with differences in sample preparation, can explain the small difference in the TFE folding midpoint found be tween these two techniques. From Figure 2-2, we can see that the bulk of the TFE dependence can be represented by the first two TFE components from the SVD analysis This indicates that, to a large extent, most residues can be described as participating in a global, two-state transition. Different Behavior in the Two Halves of IA3 Our previous study of IA3 had shown that at 0% TFE the bulk of the residues in IA3 had a small tendency towards -helical structure in 15N, 13C, 13C, 1H, and 1HN chemical shifts (9). From panels C and D in Figure 2-4, we see that the first component of the 1HN residue

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53 dependence of the chemical shift from the SVD analysis does indeed ag ree with our previous assessment (9). The first component of the 15N residue dependence on the chemical shift qualitatively agrees as well, however the SVD analysis indicates that se veral residues around the middle of the sequence may have a propensity towards a different structure. The data presented by Green et al. indicated that at 0% TFE, there was a moderately populated -helix in the C-terminus of the protein (9). The amplitudes of second components of both the 1HN and 15N residue dependence over the whole TF E range separate the N-terminus from the C-terminus of the sequence (panels E and F in Figure 2-4). In both panels the Nterminus of the sequence shows la rger negative bars than the C-terminus, indicating that the Nterminus shows a larger propensity towards a he lical structure, with residues around the middle of the sequence exhibiting a positive trend. As the percentage of TFE is increased, the 15N HSQC chemical shift cross peaks of these residues move downfield along the 1HN and 15N frequency axes. However, the difference spectrum of the lim iting high and low TFE states (Figure 2-5) shows larger negative bars in the N-terminus of the protein. The first implication of this is that the N-terminal residues are undergoing a more pronounced TFE induced transition than residues in the C-terminus. Secondly, since the bars are predominantly negative in the N-terminus, this suggests that the N-terminus shows a larger overall helical change than the C-terminus as the TFE concentration is increased. Comparisons of the frequency shifts of these residues with limiting values from Wishart and Sykes (170) indicate that this region is largely -helical. The crystal structure of the YPrAIA3 complex shows only residues 3-31. Our analysis shows helical propensity throughout the sequence with the region around the middle acting as a separator between the two halves of the protein. Some difference in structure appears to exist in

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54 this region separating the N and C termini; this is exactly the boundary found in the crystal structure. Phylip et al. (11) had previously shown that the inhib itory action of the protein could be induced with only the first 34 residues of the N-terminus, with a Ki of less than 0.1 nM at pH 4.5. From the crystal structure of YPrAIA3, it is understood that formation of an -helix in the Nterminus of the protein is necessary for i nhibition of YPrA. The folded state of IA3 induced by TFE may not be identical with th e structure found in the biologi cally active complex. However, this analysis shows that the TFE induced two-st ate transition from a coil to a helical structure occurs in the same residues seen in the YPrAIA3 crystal structure, and is consistent with the formation of an -helix in the N-terminus of IA3. The predisposition of the N-terminus of IA3 to form an -helix, which is indicated by our analysis, may be a direct consequence of its sequence and is necessary for its function (174). Results of this study indicate that TFE is driving the same kind of structural transition that occurs during the inhibition of the protease. Additional Folding Phenomena? While the main transition seen in the chemical shifts of the residues of IA3 appears to be two-state, a third component did become appare nt from the SVD analysis. This component represented less than 5% of the observable transition in the da ta, yet stood out from the analysis since the first three singu lar values were much larger than th e subsequent singular values (Figure 2-2). The third SVD component of the TFE dependence of the NMR chemical shift showed a weak positive peak, as if an additional state becomes populated up to 15% TFE, then depopulates as the concentration of TFE increases further (Figure 2-4). This component contains all deviations from perfect two-state folding si nce the rotation procedur e (see Experimental Procedures) optimized the first and second components for their fit to a two-state transition. Any

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55 trend in the TFE-dependence of the data that does not match up with two-state behavior will appear in this third component (or else in a higher order component). Several factors could contribute to this third component including differe ntial line broadening (175), direct interactions with carboxylate moieties on acidic side chains (176, 177), and any other deviation from a linear two-state transition. It is well-known that 2,2,2-trifluoroethanol (TFE) stabilizes sec ondary structures in peptides (178-181). While TFE has been shown to stab ilize many different types of secondary structures, several authors have demonstrated th at there exists a good corr elation between helical propensity and primary sequence (175, 182, 183). Although the exact mechanism of action of TFE in the inducement of helical structure is not fully understood, several theories of how TFE stabilizes helical structures have been proposed and continue to be studied. TFE may induce secondary structure simply by offering a more favorable environment for secondary structure formation, possibly by reducing the hydrophobic e ffect or decreasing the affinity of an unstructured polypeptide to hydrogen bondi ng with the surrounding solvent (173). However, TFE is also known to be a better hydrogen bond donor and a poorer acce ptor than water (173, 184). These effects may assist or detract fr om folding by stabilizing transient structures. One possibility to explain the third TFE-depe ndent component of the SVD analysis could be regarded as a low populated intermediate prior to the two-state folding in which the carboxylate moieties shift their transient hydrogen bonds from the amide protons along the backbone to TFE. This frees the amide protons, allowing them to proceed with i+4 hydrogen bonding found in the -helix. The weak peak at 15% in both the 1HN and 15N could represent the limit at which the amide proton is most free of the transient hydrogen bonds, just before it

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56 participates in cooperative hydrogen bonding of the -helix. This could be an interesting area for future research on this system. Conclusion We have presented a method to identify fold ing behaviors using residue level information on IA3. In this study, we have shown that the use of 2D 15N-HSQC spectroscopy combined with an SVD analysis can be used to identify fo lding behaviors and is an excellent method for following individual residue movements as a function of perturbant. The addition of TFE enhances the inherent propensity of IA3 to form a helical structure. From the above analysis, we determ ined that the N-terminus of IA3 has a greater transition towards an -helical structure than the C-terminus of the protein as the TFE concentration is increased. Using SVD of the 15N-HSQC NMR data, we have shown that a two-state coil to helix transition occurs in the same residues seen in the -helical structure of IA3 found in the crystal structure of the YPrAIA3 complex. The folding of IA3 derived from this study indicates seve ral features that were determined by using SVD analysis of the NMR data. We also show that to a large degree the transition of each individual residue in the protein is two-stat e like. However, the presence of a weak third component in the analysis indicates that a two-state description of IA3 folding is not entirely complete.

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57 Table 2-1: Computed G0, m-values, and TFEmidpoint from separate 1HN, 15N, datasets and the combined 1HN and 15N dataset. Dataset G0 (kJ/mol) m-value (kJ (mol % TFE)-1) TFEmidpoint (% TFE) 1HN -21.2 7.3 1.15 0.21 18.4 15N -16.6 6.6 0.92 0.30 18.1 1HN and 15N combined -20.4 4.1 1.11 0.12 18.3

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58 Figure 2-1: Changes in the chemical shifts (1HN, x-axis; 15N, y-axis), in response to increasing TFE concentration, for the 40 residues of the IA3 sequence followed in this study. Random coil values have been subtracted from the 1HN and 15N chemical shifts (160, 161). Individual residue chemical shifts were tracked as a function of TFE concentration from 0% TFE (v/v) to 23% TFE, via analysis of sequential 15NHSQC spectra. Red dots indicate data point s, arrowhead indicates direction of track as TFE concentration rises.

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59 Figure 2-1 continued

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60 Figure 2-2: Relative weighting of the SVD components obtained from the 1HN ( ) and 15N ( ) chemical shifts. The relative weight ing of component i is defined as weighting = si/s1, where si is the ith element along the diagonal of the weighting matrix S obtained by SVD of the data matrix A (see Equation 2-1). The first three components of the SVD analysis constitute 96.4% and 96.7% of the 1HN and 15N matrices, respectively. Each of the remaining components of the 1HN and 15N decompositions is under 1%. This analysis indi cates that the 1HN and 15N chemical shift matrices can be represented by the fi rst three components of the SVD analysis.

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61 Figure 2-3: Comparison of th e first three components of the SVD to the higher order components indicating that th e fourth and higher order components could be noise and play a small role in the signal. Residual ( ) is the difference between the complete dataset and the reconstructi on of the first three SVD components ( ). Panel A is at 0% TFE, while panel B is at 22% TFE. The insets give an expanded view of the residual.

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62 Figure 2-4: First three com ponents of the TFE dependence (u1, u2, and u3 from Equation 2-3) of the chemical shifts for 1HN and 15N resonances are represen ted in panels A and B respectively. The first an d second components of the TFE dependence are shown as open and filled circles respect ively, while the third component is shown as a dashed curve. The solid lines show a two-state m odel, simultaneously fit to the first two TFE components of both the 1HN and 15N together. The dashed vertical line indicates the TFE refolding midpoint which was found to be 18.3%, in agreement with the TFE refolding midpoint derived from CD (9). Panels C H illustrate the residue dependence of the chemical shift (the v components from Equation 2-3). Panels C and D show the first component of the residue depende nce shifts for 1HN and 15N resonances associated with the first TFE de pendent curve in panels A and B, panels E and F show the second component, and pane ls G and H show the third component. Open circles in panels C-H indicate residue s not tracked. The arrow indicates helical tendency as given by Wishart and Sykes (170).

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63 Figure 2-5: Computed high and low TFE spectra. Panels A and B indicate the computed residue dependence in the limit of low a nd high TFE concentrations for 1HN; panel C shows the difference between the high and low TFE spectra. Panels D F are the 15N analogues to Panels A C. Residues that were not tracked are indicated by open circles.

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64 1HN (ppm)15N (ppm)8.58.38.17.9 8.1 8.3 8.5 107 109 111 113 115 117 119 121 123 1250%TFE 23%TFE Figure 2-6: Experimental and reconstructed (red x) 15N-HSQC spectra. Panel A shows the low TFE spectrum and Panel B shows the high TFE spectrum.

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65 Figure 2-7: Analysis of the th ird SVD component. Each panel shows the reconstruction of the third component and residuals plotted for th e residues tracked in this study. All panels are on the same scale. Each re sidue track illustrates the chemical shift movement of the third component from th e SVD analysis for that residue as a function of TFE. Residues that do not have an appreciable third component appear to be randomly distributed around the origin with no discernable trend (e.g. Phe 30, Gly 40, Tyr 53). Residues with large third components show an outward looping track with beginning and ending points close to ea ch other, and are ve ry extended along the 1HN axis (e.g. Ser 9, Val 26, Met 33). The beginning of the TFE titration (0%) is indicated by a filled diamond.

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66 Figure 2-7 continued

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67 CHAPTER 3 KINETICS OF IA3 FOLDING Introduction A central notion of molecular biology is that a protein must have a defined structure to have a function (185). However, for many proteins this is not the case, as intrinsically disordered proteins (IDPs) defy this dogma (186). An intrinsically disordered protein can be found with very little well ordered structure in a biological context (20, 103). The action of an IDP occurs upon binding to target molecules (40). Crystal structures of IDPs bound to their partner molecules generally show structure of th e IDP within the bindi ng region of the complex (7, 174). There are several reasons why disorder ma y benefit a protein. For example, enhanced flexibility may allow an IDP to interact with more target proteins, or to assist in bringing an IDP and its target together (7, 108, 110). This raises some interest ing questions: what is the method of folding and binding for an IDP, and are these processes coupled? Chapters 1 and 2 explained that IA3 is a small protein that is unstructured in solution. Crystal structures of the IA3YPrA complex indicate that bi ologically the N-terminus of IA3 is found as a near perfect -helix bound to YPrA (10, 11). We have demonstrated by CD and NMR that the addition of the alcohol cosolvent TFE folds IA3 into a helical structure (8, 9). Using NMR we showed that while the whole pr otein transitions from unfolded to folded upon the addition of TFE, the N-terminus of IA3 displays more of an -helical character than the Cterminus of the protein. The c ontrollable induced folding of IA3 both in the presence of YPrA or TFE lends itself to investigation of the interactions between an intrinsically disordered protein and its target protease. Kinetics of the folding and binding in IA3 have not been studied prior to this work. One can imagine two simple models for the intera ctions between the inhibitor and the protease

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68 (Figure 3-1). In one model the unstructured IA3 molecule nonspecifically interacts with the surface of YPrA. This nonspecifi c interaction then aids in br inging the N-terminus of the inhibitor closer to active site, allowing a residu e within the inhibitor an increased chance to find the target site within the proteas e and make a specific contact. In this scenario folding of IA3 occurs upon binding to YPrA. A second m odel is that the N-terminus of IA3 first folds into a helix, and this preformed helix then makes specifi c contacts and binds to YPrA. Folding of the helix in the second model occurs before binding of the inhibitor to the protease. The folding kinetics of IA3 in these two models could be detectably different since in the first model folding is affected by contacts with YPrA. These contacts raise the local concentration of the Nterminus around the binding site and hold the pos ition of the N-terminus near the binding site. Measurement of the folding rate of IA3 in water was obtained at increasing temperatures, and will be correlated with the folding rate of IA3 in the presence of YPrA. To accomplish this, we used equilibrium far-UV CD measurements to verify structure and characterize the folding thermodynamics of several IA3 mutants as a function of TFE c oncentration and temperature. A FRET probe was attached to IA3 to allow us to detect the unfolding kinetically by fluorescence spectroscopy. Folding kinetics of IA3 were then directly probed by temperature jump spectroscopy. This allowed us to measure the relaxation times for unfolding of structure within IA3 as induced by TFE. Our ultimate goal is to constrain the folding and binding interaction models of IA3 with YPrA by the comparison of IA3 folding kinetics in the presence or absence of YPrA. Methods Choice of Donor and Acceptor Fluorophores Fluorescence resonance energy transfer is a distance sensitive phenomenon (187). In FRET, a donor chromophore is excited, and tran sfers energy from this excited state to an

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69 acceptor fluorophore through a nonradiative mechanism (187, 188). The mechanism is dipoledipole coupling between the donor and acceptor chromophores (187, 188). In FRET, the efficiency of energy transfer be tween the two chromophores can be written in terms of distance between the donor and the acceptor, r. 6 01 1 R r E (3-1) In Equation 3-1, R0 is called the Frster distance (or Frster radius) ( 188, 189). At E = 1 the efficiency of transfer is 100%, meaning that 100% of the donor energy is transferred to the acceptor and the donor's own emission is quenched. This occurs when r = 0, indicating that the donor and acceptor fluorophores are in near contact. As r the efficiency of transfer approaches 0% and the acceptor emi ssion is diminished. The distance R0 is characteristic for a donor acceptor pair. It is the distance at which th ere is a 50% probability of energy transfer for each donor excitation event ( 188, 189). The Frster dye pair used in this study is tryptophan (donor) and 5-((((2-iodoacetyl)amino)ethyl)amino)-naphthalene-1-sulphonic acid (1,5IAEDANS or IAEDANS) (acceptor). Tryptophan is a naturally occurr ing amino acid with a indole moiety. The absorbance of this moiety is very broad, extending from 265 nm to 290 nm ( 75, 189). The emission of tryptophan is equally broad, exte nding from 330 nm to 350 nm ( 75, 189). Since it is a naturally occurring amino acid, which absorbs low wavelengt h radiation, it is ofte n used as a FRET donor. 1,5-IAEDANS is an organic fluorophor e with an absorbance at 336nm ( 189). The emission wavelength is found above 500 nm( 189). The free dye is soluble in DMF, however reactions between the iodo group at the tail of the molecule and thio ls will result in attachment of the naphthalene rings to the su lfhydryl (through the IAEDANS scaffo ld) in a cysteine (Figure 5-

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70 7). This reaction is covered in more detail in chapter 5. The absorption wavelength of 1,5IAEDANS falls within the emission band of tryptophan ( 189 ). This overlap makes 1,5IAEDANS useful as a FRET acceptor for a tryptophan donor. The tryptophan (donor) / 1,5-IAEDANS (acceptor) Frster pair has a F rster radius of 1821 ( 189-191). The placement of the donor and acceptor within the IA3 molecule was chosen through an analysis of the 1DP5 crystal structure of the IA3YPrA complex (Figure 3-2). In this complex the N-terminus of a mutant IA3 is found as a helix bound to the protease ( 10 ). The helix formed by this mutant IA3 is nearly identical to the helix formed by WT IA3 in the 1DPJ crystal structure, with the main difference (aside from the Lys to Met mutations at positions 31 and 32) being that the asparagine at residue 2 is visible in th e 1DP5 structure (10). Choice of Fluorescent Dye Position Distance measurements between Asn2 and Lys16 of the IA3 crystal structure yield distances of 20.3 and 19.4 for the and carbon separations respectively. The distance between these residues in the folded structure of IA3 is essentially equal to the Frster radius for the tryptophan donor and IAEDANS acceptor. FRET la bels at these residues could therefore be used as a probe of the unfolding of free IA3. The protease, YPrA, contains 4 tryptophan residues. If a dansyl labeled K16C IA3 is bound to the protease, thre e of the native Trp residues of YPrA are within the Frs ter radius for tryptophan and 1,5-IAEDANS: Trp39, Trp190, and Trp241. The fourth tryptophan residue at positio n 181 is over 28 away, which would lead to an efficiency of transfer of ~15%. Table 3-1 lists the tryptophan residu es in YPrA and their distances from Lys16 in IA3. In the N2W-K16Cdansyl labeled IA3 double mutant, distances between the tryptophan residue at position 2 in IA3 and an IAEDANS labeled cystei ne residue at position 16 of IA3, and the distances from the IA3 WT position 16 to the tryptophan in YPrA could be different from the

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71 C distances due to the length of th e side chains of these molecules. While this concern is worth noting here, the errors are relativ ely small and unlikely to affect the conclusions of this study. Inhibition Constant ( Ki) Determination The attachment of the dansyl adds a large hydrophobic moiety to the inhibitor. It is possible that such an addition c ould alter the binding of the inhib itor to the proteas e. Binding of IA3 to YPrA can be assayed by competitive inhibiti on assay. Samples of YPrA and IA3 in 0.1M NaAc at pH 4.5 were prepared as indicated in chapter 5. Inhib ition constants were measured for all the IA3 mutants as described pr eviously (Table 3-2) ( 192, 193 ). Florescence Resonance Energy Transfer Determination We verified intramolecular FRET in labeled IA3 by comparison of the equilibrium fluorescence spectrum of the N2W(his) single mutant and K16Cdansyl(his) labeled single mutant with the N2W-K16Cdansyl(his) labeled double mutant. Fluorescence spectra of each mutant at temperatures from 5 to 85C in 5C steps were taken. Samples in 17% TFE were excited with 266 nm wavelength radiation which would cause a broad but strong emission peak for tryptophan at 330-340 nm, and a weak emi ssion peak for dansyl at 520 nm. If FRET occurred then the tryptophan peak would increase in intensity while the dansyl peak would be diminished in intensity du ring the unfolding process. The integrated fluorescence intensity of each peak was taken by integrating the fluorescence spectra from 310 nm to 424 nm for tryptophan and from 425 nm to 550 nm for dansyl. These values were then plotted as a function of temperature (Figure 3-3). Circular Dichroism Data Analysis Far-UV CD spectra were collected for each IA3 mutant in the presence of various concentrations of TFE, and at different temperat ures as described in chapter 5. Assuming a twostate model of folding for IA3 in TFE, we can say that the CD signal at each wavelength,

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72 temperature, and TFE concentration is due to the fractions of molecules that are folded ( ff) or unfolded ( fu) ( 8) as in Equation 3-2. u f TFETfcfba CD ,, (3-2) In a two-state model, all the molecules are either in the folded or unfolded states. 1 ufff (3-3) This allows Equation 3-2 to be rewri tten as an equation of one variable: u TFETfcc CD 21 ,, (3-4) for some constants c1 and c2. The fraction of molecules that can be found in the unfolded state has a dependence on change in free energy of unfolding. 1)1( RT G uef (3-5) Since the concentration of TFE and temperature bot h affect the folded state of the protein, a simple model for G can be written ( 8) as in Equation 3-6. GTFE G0 m [ TFE ] (3-6) That is, the free energy of unfoldi ng varies linearly in TFE concen tration and contains terms for the free energy in water ( G0) and the helix stabilizi ng effect of TFE (the m -value). Recognizing that Equation 3-6 includes both a change in enthalpy and a change in entropy (by the Gibbs free energy equation), we can write the m -value as a combination of c ontributions from the enthalpy and entropy. S HmTmm (3-7) Using Equation 3-7, Equation 3-6 can be rewritten as seen in Equation 3-8. TFEmSTTFEmHTFETGS H 0 0][ (3-8)

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73 This suggests that we can fit the spectra deri ved from the large set of CD experiments at various temperature and TFE with only four parameters: H0, S0 mH, and mS. Uncertainties in these values can be estimated by bootstrapping the datasets. Temperature Jump Spectroscopy Kinetic data of the folding of the IA3 protein were collected using laser induced temperature jump spectroscopy. In this experiment an IR laser is used to rapidly heat a sample, raising the temperature in 20-30 ns ( 194). The fluorescence of the sample is then probed by a UV laser as the protein relaxes to a new equilibrium at the raised temperature ( 194, 195). Our experimental setup allows us to capture not only the temporal re laxation of the system, but also the spectral distribution of the fluorescent emission over a 300 nm to 725 nm range. Spectra are collected over a ti me range covering the ns to s region, with ns resolution. The set of time resolved spectra are decompos ed using singular value decomposition (SVD) to extract the dominant features from the data ( 8). A short description of SVD can be found in chapter 2. SVD of the dataset results in a set of spectral com ponents, a set of time resolution components and a set of singular values which are used to weight the spectral and time resolution components. Assuming that IA3 is a two-state folding protein, th e time resolution components from the SVD analysis can be fit to a single expone ntial function depending on a single parameter tebay (3-9) is characteristic of the relaxation of the syst em and is called the observed relaxation time. The inverse of this time constant is the observed rate kobs which is the sum of the folding and unfolding rates. 1 kobs kf ku (3-10)

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74 The ratio of the folding rate to the unfolding rate can be related to the change in unfolding free energy of the system by Equation 3-11. RTG u fe k k (3-11) where G is the change in free energy for a certain temperature and TFE concentration as deduced from equilibrium CD measurements. Combining Equations 3-10 and 3-11, we can arrive at a relationship between the folding rate the observed relaxation ra te and the change in free energy. RTG obs RTG fe ke k 1 (3-12) Results Inhibition of the IA3 Mutants We added a fluorescent IAEDANS acceptor on residue 16 and a tryptophan mutation at residue 2 of IA3 in order to create a FRET probe of its folding/unfolding. This was implemented as an N2W and K16C double mutant with a dansyl label attached to the cysteine. In the crystal structure of the IA3YPrA complex, the asparagine of IA3 at position 2 fits into a pocket along the upper fl ap of YPrA (Figure 3-2) ( 10, 11 ). Mutating this residue to a larger tryptophan residue could alter the binding constant since the larger residue may not be accommodated as well into the pocket in the protease. The lysine of IA3 at position 16 presents another concern since it is directly opposite the ca talytic aspartates within the protease. It is possible that a lysine to cysteine mutation at this position could affect the inhibition constant by changing the positioning of the inhibitor directly above the active site of the protease. WT IA3 exhibits a sub-nanomolar binding affinity towards YPrA. Ki measurements of the N2W(his) IA3

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75 single mutant were still within the nanomolar ra nge (Table 3-2). Dansyl labeled K16C(his) IA3 also had a subnanomolar Ki towards YPrA. Circular Dichroism Analysis To assess temperature dependent changes (unfolding) in IA3, we collected CD spectra of each of the IA3 mutants as a function of concentrati on of TFE and temperature in phosphate buffer. The CD spectra at 0% TFE for all muta nts appeared unstructured at all temperatures (Figure 3-4). As the concentration of TFE increased, however, a second spectrum appeared which displayed an -helical character. The structure formed in TFE could be melted with increasing temperature resulting in a spectrum that resembled the spectra at 0% TFE (Figure 34). Globally fitting the dataset for each mutant (as in Figure 3-5) yields the representative folded and unfolded spectra. These spectra ha ve characteristic lineshapes which resemble unfolded structures for the unfolded spectrum and -helical structures for the folded spectrum (Figure 3-6). The transition in TFE and temperature can also be mapped out from this global fit of the CD data (Figure 3-7). The transition midpoint is defined as the point at which G = 0 kJ/mol, where 50% of the molecules are unfolded. Plo tting the change in free energy as function of temperature and TFE concentrati on allows a view of how the G is changing with each variable. In all cases the change in free energy has a very broad transition in both TFE concentration and temperature. The TFE midpoints of the transition at 25C are lis ted in Table 3-3. These TFE midpoints and transitions are similar, and correspond to similar unfolding G values. Assessing Temperature Dependent Changes Since we wanted to thermally perturb folded IA3 and investigate th e kinetics of the fluorescence relaxation, it was imperative to verify that there was an observable FRET

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76 fluorescence change upon thermal unfolding. Measurements of IA3 mutants containing either the tryptophan acceptor or IAEDANS donor fluorophor e were compared to the double mutant containing both fluorophores in 17 % TFE (Figure 3-3). This TFE con centration is at or near the midpoint of the folding tran sition at 25C for all the IA3 mutants. Tryptophan exhibits an intrinsic decrease in fluorescence as the temperature increases ( 194). This effect is quantifiable and amounts to a five fold decrease over the temp erature range of the study. However, in the presence of the dansyl acceptor, as in the N2W-K16Cdansyl(his) mutant, the fluorescence of tryptophan decreases only two-fold (Figure 3-3). This diminish ment in tryptophan intensity in the presence of the dansyl fluorophore suggests that there is an energy transfer between the donor and acceptor fluorophores. The reciprocal experiment comparing dans yl fluorescence emission as a function of temperature indicates that fluorescence decreas es as the temperature is increased. Direct investigation of the fluorescence of dansyl is confounded due to several factors, however this does suggest that there is a fl uorescence intensity change in th e tryptophan and dansyl emission spectra as a function of temperature. Folding Rates We calculated the folding rates of IA3 at several TFE concentrations. The observed folding and unfolding rates were calculate d from the observed relaxation rate and G at a certain temperature using Equation 3-12. The folding an d unfolding rates at 0% TFE were extrapolated from fitting the folding and unfolding rates at th e various TFE concentrations (seen for 25C in Figure 3-8) and are lis ted in Table 3-4. Discussion Intrinsically disordered proteins are found in a variety of biological contexts including signaling and transcriptional regulation ( 7, 110). These proteins can be found bound to their

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77 target proteins with certain s econdary structures, however when free in solution they do not appear to have any discer nable secondary structure ( 7, 40, 174 ). Why would a protein, which can be found structured in a complex, exist with lit tle structure by itself in solution? Is there a discernable advantage to the unstructured nature of an intrinsically unstructured protein? Studies of the intrinsically unstructured IA3 were used to shed li ght on these questions. The N-terminus of IA3 can be induced to fold into a heli cal structure in th e presence of the alcohol cosolvent TFE, or in the presence of its target aspartic protease, YPrA. If folding and binding of the inhibitor to the prot ease are coupled processes, then the unstructured nature of the inhibitor may assist in raising the rate at wh ich folding occurs as compared to independent folding and binding processes. Investigation of the Equilibrium Structure CD studies of WT(his), N2 W(his), K16Cdansyl(his), a nd N2W-K16Cdansyl(his) IA3 indicate that these IA3 mutants largely display an uns tructured character at low TFE concentrations (Figure 3-4). CD spectra of al l the mutants transition towards an alpha-helical spectrum with the addition of TFE. In all mutants with TFE-indu ced structure, the spectrum at higher temperatures resembles the unstructured spect ra collected at 0% TFE. These data can be fit to a global two-state model over all temperat ure and TFE concentrations, strongly indicating that to a good degree the folding and unfolding of IA3 by TFE and temperature can be described as a two-state transition: an unfolded state converting to a folded state with strong -helical character (Figures 3-5 and 3-6). Equilibrium CD of the IA3 mutants at increasing temperatures and TFE concentrations allowed us to construct a linear model for the unfolding free energy, G as a function of these two variables. The results of this model indicat e that the folding/unfoldi ng transition midpoint in

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78 both TFE and temperature show some variation among the IA3 mutants (Figure 3-7). While this transition is similar in all of the mutants, the midpoint is slightly shif ted towards higher TFE concentrations for the N2W(his) mutant, and even more so for the N2W-K16Cdansyl(his) mutant. Since this midpoint shif t did not occur in the K16Cdansyl( his), it would appear that the N2W mutation subtly affects the TFE dependence of the transition. We note that the crystal structures containing the IA3 helix start at residue 2 and do not contain th e initial methionine; it is possible that helix initiation ma y begin at residue 2. If this is so, the tryptophan mutation may slightly supress helix initiation leading to a larger concentration of TFE be ing needed to initiate helix formation. Observing Fluorescence It is not possible to undert ake kinetic experiments on IA3 folding without a probe that is sensitive to changes in peptide secondary st ructure. Molecules displaying fluorescence and FRET can be used as probes since the fluores cence emission provides a readily identifiable signal, and reliance on distance can be exploited by observing changes in the intensity as a function of time needed for a protein to relax toward an equilibrium ( 188, 189). Observations of the N2W-K16Cdansyl(his) IA3 mutant indicate that at low temperatures the dansyl fluorescence is high (Figure 3-3). As the temperature is in creased, the dansyl fluorescence emission declines. The tryptophan fluorescence for the labeled double mutant displays a decrease w ith rising temperature. If fluorescence tryptophan emission decreases with rising temperature, how do we know if there is a FRET signal? A solution of free tryptophan has a characterist ic decrease of 2% per degree Celsius in fluorescence emission as the temperature is increased (194). This decrease in emission would amount to a four fold decrease over the 5C-85 C range. At low temperatures the tryptophan fluorescence of the labeled double mutant decreases by half as much as expected for an indole on

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79 a polypeptide. This increase could be indicative of FRET. At higher temperatures this effect decreases, which is consistent with thermal denaturation of the peptide. By CD we have seen that at lower temperatures IA3 appears to have a helical structure, and could be found as a folded molecule ( 8, 9). At these lower temperat ures the dansyl fluorophore displays a very intense fluorescence spectrum. At higher temperatures this spectrum is vastly diminished. This represents a quantifiable change in the fluorescence of the N2WK16Cdansyl(his) IA3 mutant, and is consistent with res onance energy transfer occurring within the folded inhibitor. Folding Kinetics of IA3 Using temperature jump spectroscopy, we meas ured the observed folding relaxation of the N-terminus of IA3. By combining this observed foldi ng rate with the unfolding free energy deduced from the CD study, we can calculate the unfolding rate constants (or folding rate constants) as a function of TFE at each temperature studied. By extrapolating these values to 0% TFE, we can obtain the folding rate in water for the inhibitor at a particular temperature (Figure 3-8). The extrapolated folding and unfolding rates in water are listed in Table 3-4. Examination of these rates shows that as the temperature of the system is increased, the unfolding rate dramatically increases. The unfolding rate s increase over two orders of magnitude, from 0.41.71 106/s at 5C to 25 106/s at 50C. This is well in line with our unde rstanding of the system being folded at low temperatures and unfolding at higher temperatures. Comparisons of the unfolding/folding rates in the presence of YPrA are needed to constrain the folding/binding model of the inhibito r to the protease. No change in the folding rate of IA3 in the presence of the YPrA would indicate that the formation of a helix is not enhanced by binding, and suggest that helix form ation occurs before binding. An increase or

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80 decrease in the folding rate would suggest an enhancement in the presence of the protease, indicating that folding and binding are coupled processes. Early results of IA3 in the presence of YPrA show a folding rate of ~107/sec (or ~100ns) indicating that IA3 does interact with YPrA, and this interaction leads to a distinct folding rate enhancement. Conclusions The folding rate of IA3 is found to be more sensitive to temperature than to TFE concentration. The TFE dependence in the obs ervation relaxation rates is exceptionally weak, thus we can deduce with confidence the folding and unfolding rates of free IA3 in water at various temperatures. These data are valuable as a step towards a comparison of folding rates in the presence of the protease since they allow us to interpret the folding rate of IA3 in the presence of YPrA. Comparisons of the folding rates of IA3 in water and with the protease will allow us to constrain the folding and binding interaction models of IA3. Investigations of the kinetics of IA3 folding in the presence of the pr otease are currently under way.

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81 Table 3-1: Distances fro m tryptophan residues in YPrA to Lys16 in IA3. These distances were found using the 1DPJ crystal structure. YPrA residue Distance to K16 in IA3 () Trp39 19.0 Trp181 28.2 Trp190 18.4 Trp241 17.9

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82 Table 3-2: Binding co nstant values of IA3 to YPrA, for the mutants used in this study. Inhibition values for all the mutants were at or beyond the limits of instrument detection at 25C, pH 4.5, indicating that they all lie in the subnanomolar range. IA3 mutant Inhibition constant (nM) WT(his) <0.1 N2W(his) <0.1 K16Cdansyl(his) <0.1

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83 Table 3-3: The TFE folding mi dpoint values at 25C for the IA3 mutants in this study. These values are extracted from globally fitting the CD data for each mutant and extracting the TFE value corresponding to G = 0. IA3 mutant TFE Folding Midpoint ( G = 0) at 25C WT(his) 17.8.3% N2W(his) 21.4.2% K16Cdansyl(his) 17.0.3% N2W-K16Cdansyl(his) 23.8.3%

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84 Table 3-4: Folding and unfolding rates for the IA3 helix, as measured in TFE and extrapolated to water (0% TFE). As the temperature incr eases there is a large increase in the unfolding rate of the protein. T (C) Kf (106/s) Ku (106/s) 5 0.02.10 0.41.71 10 0.04.09 0.68.59 15 0.07.10 1.14.71 20 0.11.07 1.86.15 25 0.18.04 2.99.64 30 0.27.03 4.56.51 35 0.41.09 6.71.45 40 0.63.21 10.33.49 45 0.99.40 16.22.58 50 1.55.89 25.40.57

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85 Figure 3-1: Simple folding and binding models for the interaction of IA3 (in blue) with YPrA (in orange). In model A folding takes place after binding. In model B IA3 folds into a helix, then this preformed helix binds to the protease. Figure reproduced with permission from Ranjani Narayanan. M M o o d d e e l l A A YPrA YPrA YPrA BINDING FOLDING M M o o d d e e l l B B YPrA YPrA YPrA BINDING FOLDING

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86 Figure 3-2: Several views of the IA3YPrA complex illustrating the placement of IA3 residues within 'pockets' in YPrA. YPrA in blue is surrounding the helical N-terminus of IA3 in grey. The catalytic aspart ates of YPrA are shown in gr een. Lys16 in IA3 is shown in orange space filling form. Residues Asn2 and Phe30 in the IA3 helix are listed. These views of IA3 and YPrA were generated using PyMOL.

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87 Figure 3-3: Fluorescence in the presence or absence of the donor or acceptor excited at 266 nm. A) Tryptophan donor fluorescence in the pres ence or absence of dansyl in N2WK16Cdansyl(his). At lower temperatures the tryptophan fluorescence is higher alone than in the presence of dansyl. B) Dansyl acceptor fluorescence. In the presence of tryptophan the dansyl fluorescence is increased at lower temperatures.

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88 Figure 3-4: CD spectra gathered as a function of TFE and temper ature. Shown here are the CD spectra for WT(his) IA3, which is typical of the data gathered for all the mutants in this study. Note that at 0% TFE the line shapes of CD sp ectra at all temperatures are similar. As TFE is added the CD spectra change indicating -helical structure. At low temperatures this -helical structure is visible in the CD spectra. With increased temperatures these structures melt, re sulting in an unfolded CD spectrum.

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89 Figure 3-5: Typical fit to the far-UV CD spectra using the global two-state fit. A) The fit (in red) performed simultaneously on all CD spectra (in black ) at all temperatures and TFE concentrations for K16Cdansyl(his) IA3. B) Enlargement of the CD spectra and fit at 25% TFE.

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90 Figure 3-6: Folded and unfolded CD spectra of IA3 as obtained from a globa l two-state fit of the spectra in Figure 3-4. The unfolded and fold ed spectra are charac teristic of unfolded and -helical spectra, respectively.

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91 Figure 3-7: Contour plots of th e free energy of unfolding (left co lumn) and fraction of molecules that are unfolded (right column) as a func tion of temperature and percentage of TFE for all IA3 proteins in this study, as obtained from the two-state global fit for each protein. All the contour plots display th e same behavior: a broad transition in temperature and TFE concentration.

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92 Figure 3-8: Plot of the observed relaxation rate, folding rate a nd unfolding rate as a function of TFE for N2W-K16Cdansyl(his) IA3 at 25C. Circles are data points, lines are fits to the data points allowing extrapolation to 0% TFE. This yields the relaxation rate, folding rate and unfolding of the mutant in water at 25C.

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93 Figure 3-9: Plot of the rela xation rates at various TFE con centrations as a function of temperature. The folding rate of IA3 appears to be more sensitive to temperature than to changes in TFE concentration.

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94 CHAPTER 4 ISOTHERMAL TITRATION CALORIMETRY OF IA3 PROTEINS IN THE PRESENCE OF YPRA Isothermal Titration Calo rimetry of the YPrAIA3 System Introduction All biological processe s at the chemical level (and ind eed chemical reactions generally) involve molecular recognition between the elemen ts involved. Within living systems these elements can be very diverse, ranging from protei ns to DNA to other small and large molecules. Additionally, proteins can be in different conformational states, which could allow for differences in molecular recognition. To understa nd any chemical or biological process, it is important to understand the nature of the recognition and interac tions between elements at the molecular level, and elucidate the for ces that stabilize the interactions ( 196-199). The intrinsically unstructured protein, IA3, can inhibit yeast proteinase A, YPrA. To inhibit YPrA, IA3 undergoes an unstructured to structured transition; however, this transition does not manifest throughout the whole protein, but rather crys tal structures of the YPrAIA3 complex show that the N-terminus of the protein adopts a helical structure ( 10, 11). The IA3 structure found bound in the crystal structure of the i nhibited YPrA IA3 complex are the Nterminal residues 2-32 found in a helix. These re sidues are less than half of the full length protein. The x-ray structure im plies that the C-terminus of IA3 is unstructured, and hence has no known function. Given that half of the IA3 protein is not needed for inhibition, one wonders what benefit the C-terminal end may offer the whol e protein. A possible e ffect of the C-terminus may be to increase the concentration of IA3 near the active site of YPrA through weak interactions with the surface of YPrA. This explanation is similar to the study of IA3 using an Nterminal his tag by Winterburn et al. ( 135). Here I present evidence for the interaction of the Cterminus with YPrA, and a possible explanation for this interaction.

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95 Choice of IA3 Proteins To investigate the C-terminal interaction of IA3 towards YPrA it was necessary to obtain three IA3 variants: Wild type IA3 WT Wild type with a his6 tag addition WT(his) The N-terminal portion of IA3, which is found as a helix in the crystal structure of the YPrAIA3 complex (PDB accession number 1DPJ) Nterm These variations were chosen based on several attributes (Figure 41 and Table 4-1). The crystal structure of IA3 shows that residues 2-31 interact w ith the upper flaps of YPrA by binding to several residues as an -helical structure ( 11). Although the full length inhibitor is 68 amino acids long, the C-terminal residue s are not seen in the crystal st ructure which could indicate that this region is very mobile ( 10, 11, 108). Circular dichroism and NMR studies of the full length protein indicate that it is unstr uctured in the absence of YPrA ( 9). Several authors have shown that only the N-terminal half of the protein is needed for inhibition, and has potency similar to that of the wild type inhibitor ( 10, 135). Since the C-terminal portion of the protein is not seen in the crystal structure, it is possible that it is not necessary for inhibition of the protease. In 2000, Shoemaker et al. proposed the fly-casting mech anism which postulated that natively unfolded proteins may have an enhanced ability to find a binding pa rtner as compared to structured proteins ( 108). The authors hypothesized that this is due chiefly to two main effects: A well structured protein is re stricted conformationally, a nd cannot explore the immediate solution environment for a binding partner as quickly as an unst ructured protein. An unstructured protein may not be able to inte ract as strongly with its binding partner as a structured protein, however through weak nons pecific interactions the two proteins can draw together enhancing existing interactio ns which may prompt th e unstructured protein to fold.

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96 This suggests that the C-terminus of IA3 may serve to enhance the ability of the protein to find, bind, and fold onto YPrA. I used isothe rmal titration calorimetry to investigate the interactions of YPrA w ith wild type protein and the truncated N-terminal IA3. The additional WT IA3 with C-terminal his6 tag addition variant (WT(his))was tested as well. The WT(his) variant was made to ease in purification of IA3 by making use of the affinity of the his6 tag for nickel ( 10). However, it also served to check the relationship between charge and IA3 binding to YPrA. Within the cell YPrA is located in an acidic vacuole ( 132 ). In such an environment, YPrA has a negative surface charge (calculated to be 7.6 at pH 4.5) (Figure 4-2) ( 132, 200, 201). Wild type and the truncated N-terminal IA3 have surface charges of +5.4 and +2.3, respectively at pH 4.5. The addition of a his6 tag to the wild type IA3 significantly enhances the positive char ge through the titratable histidine groups, with a resulting charge of +10.6 at pH 4.5. This his6 tag addition to the wild type IA3 then serves to verify if charge is a driving force in the C-terminus interaction of IA3 with YPrA. Methods Protein Preparation YPrA and IA3 were prepared as described in chapter 6. Concentrations of stock solutions were determined using amino acid analysis at the University of Florida Interdisciplinary Center for Biotechnology Research Protein Chemistry Core. Isothermal Titration Calorimetry Experiments ITC experiments were performed on a VP-ITC titration microcalorimeter from Microcal, LLC. (Northhampton, MA). A typical expe riment consisted of injections of IA3 into YPrA. Samples of YPrA in 0.1M sodium acetate, pH 4.5, at 1 M were put in the sample cell. An IA3 sample (in 0.1M sodium acetate, pH 4.5) was load ed into the titration syri nge at a concentration of 13 M.

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97 The experiment consisted of 60 injections of IA3. Each 10 L injection lasted 20 seconds followed by an equilibration time of 240 seconds be fore the next injection. The equilibration time was sufficient for the system to return to baseline before the next titration. An initial injection of 1 L, was deleted from the analys is as previously described ( 9 ). The sample cell was stirred at 460 rpm to mix the solutions during the experiment. Control experiments, to determine the heat of dilution, were carri ed out by titrating IA3 into the sample cell containing on ly buffer and by averaging the he at obtained per injection after saturation of YPrA by IA3. The heats of dilution for contro l injections were averaged and subtracted from each injection of the binding ex periment. Further explanation of the ITC experiment and methods can be found in chapter 5. Isothermal Titration Calorimetry Analysis The data were analyzed using Origin (O riginLab, Northampton, MA). The binding isotherm was deconvoluted by well-established methods ( 72, 73, 202 ), and a LevenbergMarquardt nonlinear le ast-squares fitting method was then used to generate a best fit of the data and determine H (enthalpy change of the binding reaction), Ka (association or binding constant), and n (stoichiometric ratio of inhibitor to protease bi nding sites). The binding affinity of IA3 for YPrA is very strong, which causes the titration curve to be very steep and similar to a step function. Ka values determined from these ITC data are not accurate and were not used in our analysis ( 9, 203). A more detailed explanation of IT C analysis can be found in chapter 5. Heat Capacity ( Cp) Determination The relationship between the cha nge in heat capacity upon binding ( Cp) (at constant pressure) and the change in binding enthal py can be written as in Equation 4-1 ( 73). dT Hd Cp)( (4-1)

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98 Plotting the change in heat capacity of binding ve rsus the temperature can provide insight into the equilibrium binding of the YPrA IA3 complex. The change in heat capacity was determined by fitting the H values at each temperature to Equation 4-1. The fit was optimized using the error in H provided by the ITC analysis. The optimi zation consisted of using the error in a particular H value as a weighting of that value. Smaller errors in H resulted in a higher weighting for that H value. Circular Dichroism Experiments CD experiments of the WT, WT(his) and N-terminal IA3 proteins were done as described in chapters 3 and 5. Results Optimizing the ITC Curves The titration binding curv e of WT and WT(his) IA3 binding to YPrA appears sharp, and very similar to a step function; additionally the temperature difference as seen by the microcalorimeter after each injection of titrant into titrand is not large (Figures 4-3 and 4-4). Raising the IA3 concentration would increase the temp erature difference in each injection; however, doing so would sharpen the binding curve cl oser to that of a st ep function. Lowering the IA3 concentration would broaden out the curv e making fitting of the data more robust, however this increases the error in each data po int since the signal to noi se ratio would be much lower. Finding optimal concentrations for the experime nts was crucial to the success of the ITC measurements. Through several titr ations at differing YPrA and IA3 concentrations it was determined that a YPrA concentration of 1 M and an IA3 concentration of 13 M would be sufficient to determine the values of H and Cp.

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99 Figure 4-3 shows the raw data fr om the interactions of WT IA3 and YPrA, measured by the ITC microcalorimeter. The binding reactions seen in these data are exothermic ( 72, 73). Integration of the heats of reaction of the experiment results in a binding curve (Figure 4-3). The distance between the beginning baseline of the curve and ending saturati on is the enthalpy of binding, while the slope of the curve is propor tional to stoichiometry multiplied by the binding constant. The titration data are fit using a least-squares fittin g method to obtain the stoichiometry of binding for the ligand to the pr otease, change in enthalpy, and equilibrium binding constant. A similar analysis was done for WT(his) (Figure 4-4) and N-terminal (Figure 4-5) proteins at temperatures from 15C to 45C in 5C increments. A few of the titrations had a very low si gnal to noise ratio even at the optimal concentrations of the IA3 and YPrA. The higher temperatures in all datasets appeared to be noisier. This was attributed to air bubbles fo rming in the sample cell during the experiment ( 73) despite degassing all samples before each experiment. Additionally, titrations of the N-terminal protein appeared to yield a lower signal than the other proteins. Investigating the Heat Capacity The H values derived from fitting the ITC data we re fit using Equation 4-1, as previously described, and are plotted in Fi gure 4-6. Linear fits of the H values provided Cp values which are listed in Table 4-2. All IA3 proteins had differing Cp values, however the plots of H versus T for WT and WT(his) data seemed similar to each other when compared to the Nterminal data. The Cp value for the N-terminal protein was found to be 387.3 cal/molK (1.6.13 kJ/molK), while the WT and WT(his) values were 697.4 ( 2.9.16 kJ/molK) and 1090.3 cal/molK ( 4.5..34 kJ/molK) respectively.

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100 Examining the Stoichiometry A plot of the fractional stoichiometry of the inte ractions of the three proteins can be seen in Figure 4-7. The WT and WT(his) proteins had n -values which were close to each other up to 35C. This value had a mean of ~.43 After this, both the WT and WT(his) n-value started to decrease. The n-value for the N-terminal protein had a mean of 1.3.3. Discussion IA3 Structure Before Binding to the Protease Several studies have proven that the IA3 has little intrinsic struct ure in the absence of YPrA ( 8-11). Circular dichroism measurements of WT, WT(his) and N-terminal IA3 proteins indicate that without YPrA, these mutants have little second ary structure (Figure 4-8 for WT(his)). It is possible that these mutants are temp erature sensitive, and that sec ondary structures could form at low temperatures. To test this, CD studies of the mutants at various te mperatures were done. Examination of the CD spectra shows that there is very little variation in secondary structural content as temperatures are lowered. IA3 Structure After Binding to YPrA Crystal structures of IA3 (and IA3 mutants) bound into YPrA have been made and studied by several groups (9, 11 ). All of these crystal structures show IA3 as an amphipathic helix bound into the inner side of the bindi ng cleft of YPrA (Figures 1-5 and 3-2). The hydrophobic side of the helix is surrounded by YPrA, while the hydrophili c side is mainly oriented out of the binding cleft towards the solvent ( 10). CD spectra of IA3 in the presence of YPrA do show a mix of secondary structure ( 9 ). Comparison of the CD spectra of YPrA alone with spectra of YPrA and IA3 also show a slightly higher helical content, wh ich could suggest that IA3 is folded in the presence of YPrA ( 9).

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101 Green et al. showed NMR studies of the YPrAIA3 complex ( 9). In these spectra it is evident that several of the resonances asso ciated with the N-terminal residues of IA3 shift or disappear upon binding to YPrA. These changes were interpreted as the N-terminus of IA3 transitioning through a conformationa l change to a structure simila r to the crystal structure. Additionally, it was noted that residue p eaks identified in the C-terminus of IA3 significantly broaden in the presence of YP rA, indicating intermediate ex change and suggesting that Cterminus is interacting with YPrA (9). Potency of WT, Nterm, WT(his) The interaction of IA3 and YPrA is extremely strong. Ki values for the mutants in this study are all in the subnanomolar range ( 9, 133, 135). Heat Capacity ( Cp) Value Comparison The heat capacity of a protein can change upon binding ( 204-206). Binding can be seen as leading to a more condensed polypeptide environment within a sa mple. The physical effects of heat capacity are many and may include effects on solvent exposure, el ectrostatics, hydrogen bonding, among others ( 207, 208). Considering solvent exposure, heat capacity changes mirror these condensing interactions, by exhibiting a lo wered heat capacity as the hydrophobic residues are buried and surfaces of both prot eins are less solvent exposed (204, 209, 210). This has been extended to protein folding as well, as a lowere d heat capacity can be used to qualitatively describe a protein which undergoes a transition from open, solv ent exposed coil to condensed secondary structure ( 211-213). The IA3YPrA system involves folding of the IA3 molecule and binding of IA3 to YPrA; this complicates analysis of the enthalpy value si nce it involves terms from both folding and binding. Empirical evidence states that changes in polar and non-polar surf ace area are correlated with changes in heat capacity (204, 207). However, several author s have suggested that heat

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102 capacity changes may not simply reflect surfac e area changes and hydroph obic interactions, but are also influenced by electrostatic interactions, hydrogen bonding, and conformational entropy ( 204, 214). Of these effects, only solvent exposure of polar and apolar re sidues and their effect on heat capacity is understood ( 203, 215-218 ), leaving understanding of the other contributions uncertain. The heat capacity values for the IA3 proteins in this study appear to agree with these ideas as well. The Cp values for the three proteins interac ting with YPrA (all in the same solution environments) are markedly different (Table 4-2). The 68 amino acid WT IA3 has a temperature dependent Cp = 697.4 cal/molK ( 2.9.16 kJ/molK), while the 76 amino acid WT(his) IA3 has a Cp of 1090.3 cal/molK ( 4.5.34 kJ/molK). The N-terminal mutant IA3 is only 31 amino acids, and has a Cp of 387.3 cal/molK (1.6.13 kJ/molK), which is more than half that of the WT IA3. The Cp values of all three proteins are negative indicating that the interactions between the IA3 proteins and YPrA lead to less solvent exposed and more condensed systems. This is consistent with the idea that when IA3 binds to YPrA, it buries hydrophobic residues into the proteinases, as well as covers over hydrophobic re sidues in the proteinases. In the process, YPrA and the unstructure d IA3 form a more condensed system. The Cp values of the WT and WT(his) proteins are much more negative than the Nterminal protein. This could be indicative of ot her interactions of the C-terminus of the longer proteins with YPrA. At the pH of the experiments, the his6 tag on the WT(his) IA3 leads to a much more positive molecule. This positive tip at the C-terminal end of the WT(his) protein could interact with the negativel y charged surface of YPrA more readily than that of the WT protein (Figure 4-9). While the C-terminal end of WT IA3 could potentially act as a tether

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103 between the N-terminus and YPrA, the C-terminus of WT(his) would act as 'stickier' tether between YPrA and the inhibi ting N-terminal portion of IA3. The N-terminal protein, however, does not have the benefit of this tether. While only the N-terminus is necessary to inhibit YPrA, the smaller Cp value seems to suggest that the nature of the interactions between the N-terminal protei n and YPrA are not as strong as compared to the interactions between YPrA and the WT or WT(his) proteins. N -value Comparisons The WT IA3 consists of 68 amino acids, of which onl y half are necessary to inhibit YPrA. If the N-terminus of one IA3 molecule interacted with one YPrA then the ligand to protein ratio, the n-value, should most certainly be one. This is not completely the case, however, since the average n -value for the N-terminal prot ein is 1.3.3. Noninteger n -values have been seen in studies of other protein systems, and have been attributed to additional binding sites with low cooperativity, additional folding events or misfolding ( 219), aggregation ( 220), or additional protonation states ( 221). The numerical value itself is confounding, however it may indicate that more than one N-terminal protein may be found interacting with each YPrA molecule. Inhibition of YPrA takes place when one IA3 molecule binds into the flaps of the active site cleft. The ITC binding data could suggest that more than one N-terminal protein binds to YPrA, which further suggests that these proteins may be in teracting somewhere other than the YPrA active site. Rather, once YPrA is inhibited by one N-terminal IA3 molecule, other positively charged N-terminal molecules may be simply interacting nonspecifically with the negatively charged surface of YPrA (Figure 4-9). The n-values of the WT and WT(his ) proteins also hold an inte resting story. The addition of the C-terminal residues to the N-terminal IA3 causes a dramatic shift in n-value. The n-value of WT IA3 averages to 0.41.09, while the average va lue for WT(his) is 0.43.05. The values,

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104 however seem to be similar up to 35C, after which they both become less positive. The average n-value from 10C to 35C, for both WT and WT(his) is ~0.42. This value indicates that each molecule is binding more than one YPrA. At temperatures higher than 35C the n-values of both WT and WT(his) proteins decrease, which could be indicativ e of more solvent exposed binding regions due to a loosening of the YPrAIA3 complex, or an increase in the accessible surface area of more mobile regions of the complex. Aggreg ation of the samples at these temperatures was not seen. N-terminus Versus C-terminus Why would a cell spend resource s and energy to create a longer protein when a smaller version would do the job sufficiently? From the above description and analysis it would seem that the N-terminal end of IA3 is all that is necessary to inhi bit YPrA. The C-terminus of the protein appears to play some role in the intera ction of YPrA with the protein. The longer IA3 proteins have more negative Cp values indicating that they ar e more involved in producing a less solvent exposed or more condensed syst em, namely the interaction between the IA3 protein and YPrA. This suggests that the C-terminus of the protein acts to bring YPrA and IA3 together, while the action of the N-terminus of the protein is to inhibit YPrA (Figure 4-9). The N-terminal IA3 is unstructured in the absence of YPrA so it doe s not serve as a strong test of the fly-casting model; however the additi on of the unstructured C-terminus doe s change the heat capacity of the interactions between YPrA and IA3. The C-terminus is not seen in the crystal structure which may point to not only the unstructured nature of th e C-terminus, but that the interactions between the C-terminus and YPrA are weak and fleeting. In investigations of the interactions between IA3 and YPrA using NMR, Green et al noted that the C-terminal re sidues of IA3 appeared to be interacting with YPrA. These w eak interactions do not appear to enhance the inhibitory binding

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105 of the N-terminus, but could instead promote the meeting of the N-terminus with binding partners in the active site of YPrA (Figure 4-9). The WT(his) protein serves as an interest ing variant of this theory. WT(his) IA3 has a positive charge at pH 4.5 which is nearly double that of the WT protein. The Cp values of the two proteins are different, however the H values of both are reasona bly close to each other at all temperatures as compared to the N-terminal protein. The source of this difference in Cp values is unknown, but could perh aps be explained by the weak ch arge interactions between the surface of YPrA and WT IA3 being fewer/less than the charge interactions between YPrA and WT(his). The WT(his) explanation above is similar to that of Winterburn et al. ( 135 ). In Winterburn's work the authors modified the 68 amino acid WT IA3 with an N-terminal his7 tag. This addition did not aff ect the affinity of IA3 for YPrA, however it broadened the specificity of the molecule to allow IA3 to act as an inhibitor for a re lated protease, vacuolar aspartic proteinase from Pichia pastoris. Winterburn's explanation of th is broadening of the selectivity was based on increasing the local concentration of IA3 around the active site by taking advantage of the charge interaction of the his7 tag and the surface of YPrA ( 135). In a similar fashion the charged his6 tag on the C-terminus of the WT(his) IA3 and the C-terminus of WT IA3 generally may be responsible for raising the local concentr ation of N-terminus around the active site cleft in YPrA (Figure 4-9). Conclusion IA3 is a unique protein molecule, both in seconda ry structure and the se lective, potent, and distinctive way that it inhibits yeast proteinase A. The N-termi nus of the inhib itor tightly binds YPrA, however the C-terminus has been mysterious and largely ignored due to its unstructured nature. Here we present evidence that the C-te rminus may serve to enhance the meeting of the

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106 N-terminus of IA3 with the active site of YPrA. The Cterminus may do this by exploiting weak charge interactions between its positive end and the negative surface of YPrA, thus raising the local concentration of th e inhibiting portion of IA3 for the protease.

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107 Table 4-1: Properties of the proteins in this study. The mole cular weights, pI, and charge at pH 4.5 were calculated using EMBO tools. Th e molecular weights were verified using MALDI mass spectrometry. Protein Number of amino acids Molecular weight Calculated pI Calculated charge at ph 4.5 WT 68 7707.4 7.63 +5.4 WT(his) 76 8772.5 6.82 +10.6 N-term 31 3483.9 9.20 +2.3 YPrA 329 35749.2 4.16 7.6

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108 Table 4-2: Heat capacity of binding ( Cp) and n -values for WT, WT(his), and N-term IA3 derived from ITC data. Protein Cp (cal/molK) Cp (kJ/molK) n-value WT 697.4 2.9.16 0.41.9 WT(his) 1090.3 4.5.34 0.43.05 N-term 387.3 1.6.13 1.3.3

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109 Figure 4-1: Schematic representations of the IA3 proteins. The sequence used is listed at the top. In red are representations of the three prot eins, the 68 amino acid wild type (WT), the 76 amino acid wild type protein with a his6 tag addition (WT(his)), and the Nterminal IA3 protein (N-term). The region which is found in a helix in the IA3YPrA crystal structure is in dicated by blue H's.

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110 Figure 4-2: Several views of YPrA illustrating the surface charge on the protease. Negatively charged surface is in red, while positive elements are in blue.

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111 Figure 4-3: Representa tive ITC result for 13 M WT IA3 titration into 1 M YPrA at pH 4.5. Panel A shows the raw power data from the microcalorimeter as a function of time. Panel B shows the integration of the baseline subtracted data at each injection as circles. The red curve is a fit to the data as explained in chapter 5. 0.00.51.01.52.02.53.03.5 -20 -15 -10 -5 0 -0.10 -0.08 -0.06 -0.04 -0.02 0.00 0306090120150 Time (min)cal/secMolar Ratiokcal/mole of injectant

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112 Figure 4-4: Typical ITC result of 13 M WT(his) IA3 titration into 1 M YPrA at pH 4.5. Panel A shows the raw power data from the micro calorimeter as a function of time. Panel B shows the integration of the baseline subtra cted data at each injection as circles. The red curve is a fit to the da ta as explained in chapter 5. 0.00.51.01.52.02.5 -40 -30 -20 -10 0 10 -0.10 -0.08 -0.06 -0.04 -0.02 0.00 0.02 -300306090120150180210240270 Time (min)cal/secMolar Ratiokcal/mole of injectant

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113 Figure 4-5: Typical ITC result for the 13 M N-terminal IA3 titration into 1 M YPrA at pH 4.5. Panel A shows the raw power data from the microcalorimeter as a function of time. Panel B shows the integration of the baseline subtracted data at each injection as circles. The red curve is a fit to the data as explained in chapter 5. 0.00.51.01.52.02.53.03.5 -8 -6 -4 -2 0 2 -0.04 -0.03 -0.02 -0.01 0.00 0.01 0306090120150 Time (min)cal/secMolar Ratiokcal/mole of injectant

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114 Figure 4-6: Change in enthalpy of binding ( H ) values for the three proteins. The H values for WT, WT(his) and Nterm are represented here in green, blue and black respectively. Fits of these values as a function of temp erature according to Equation 4-1 are plotted as red lines, with the Cp value listed accordingly.

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115 Figure 4-7: Plots of th e stoichiometry value (n -value) for IA3 protein to YPrA. The n-values for WT, WT(his) and Nterm are represented here in blue, green and black respectively.

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116 Figure 4-8: Circular dichroism spectra for WT(his) IA3. WT(his) at 100uM is plotted for several temperatures ranging from 5C to 85C. All of these spectra have been overlaid to illustrate the lack of intrinsic structure in the IA3 protein at al l temperatures.

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117 Figure 4-9: Possible models for IA3 binding. IA3 is in orange, and is represented as a squiggle when unfolded. Helical, bound IA3 N-terminus is represented as an orange box, bound into YPrA in blue. Panel A s hows binding of the N-terminal IA3; Panel B shows possible interactions of the whole IA3 protein with YPrA with the N and C termini of IA3 labeled as indicated.

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118 CHAPTER 5 METHODS AND PROTOCOLS Due to the m any different biophysical experiments that were done on IA3 and YPrA, there are many methods associated with them. Many of these protocols deal with purification of proteins, particularly several different mutants of IA3, WT IA3, and YPrA. Additionally, some time will be spent illustrating some features of thes e protocols such as considerations to be noted concerning IA3 cysteine mutants and the temperature stab ility of YPrA. A labeling protocol for attaching dansyl to cysteine mutants of IA3 is also given below. These biochemistry protocols are followed by general methods used in several experiments. IA3 Purification Purifying IA3 Histidine Tagged Proteins Working with IA3 When working with IA3, a main concern is contaminating proteases ( 11, 130, 222, 223 ), since unstructured proteins ar e particularly susceptible to proteolytic degradation ( 224 ). Within the cell proteolytic degradation is considered to be a method of quality control, attempting to ensure that misfolded or nonfolded proteins are removed (224, 225). The specificity of IA3 towards YPrA and its inability to inhibit proteas es which are even similarly to YPrA (both in sequence and structure) make it a candi date substrate for other proteases ( 11, 130, 222, 223). An additional concern with IA3 is the possibility of interac tion with other molecules. A proposed advantage of a protein being intrinsically disordered or having a disordered region is so that it may interact with many partner molecules ( 226). Examples exist of unstructured proteins, or proteins with unstructured regions, interacting with multiple partners ( 226, 227). As an intrinsically disordered protein, IA3 has the potential to interact with other molecules in a way

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119 that could alter its interaction with the prot ease, or yield results which would confuse our understanding of the interaction of IA3 to YPrA. These issues are reasons why IA3 must be separated from other contaminating molecules. Removal of other proteins allows us to investigate and characterize IA3 and its interaction with YPrA without dealing with other effects su ch as degradation or other interactions. Purifying IA3 Histidine tagged IA3 has several properties that are unique in a solution of E. coli cell lysates. Foremost is the his6 tag which was added to the protein to aid in purification. This tag has a micromolar affinity towards nickel (and cobalt) ( 228), and is used simply by flowing the cell lysate over immobilized ni ckel and 'washing' away other molecules with various buffer solutions ( 228, 229 ). An additional exploitable property of IA3 is the unstructured nature of the protein. Boiling or freezing a structured protein will often disrupt secondary and tertiary interactions present within the protein ( 230). Often these disrupted stru ctures lead to aggregation ( 230, 231). Unstructured proteins have higher tolerances for boiling and freezing as compared to most well structured proteins ( 6, 7, 174 ). The disordered nature of uns tructured proteins can allow these proteins to evade this aggregation. Purification protocol The purification scheme that is laid out here is similar to schemes previously followed ( 9, 11). However, some significant differences allow th is scheme to be more efficient and robust in producing IA3. Additionally, this purification scheme serves as a template to compare differences with other purification schemes in this chapter. Preparation prepare the following buffers as indicated: o pH 8 buffer: 50 mM phosphate bu ffer + 300 mM NaCl at pH 8

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120 o pH 6.3 buffer: 50 mM phosphate buffe r + 300 mM NaCl + 10% glycerol at pH 6.3 o pH 4 buffer: 50 mM phosphate bu ffer + 300 mM NaCl at pH 4 Protocol o Step 1: Boil cell lysates for at least 15 minutes, then rapi dly cool to room temperature. o Step 2: Centrifuge the solution at 27,000 g for 15 minutes. o Step 3: Filter the solution through a .2 micron nylon filter to remove aggregates. o Step 4: Flow the filtered solution over a nickel column pre-equilibrated in pH 8 buffer o Step 5: Wash the column with 2 column volumes of pH 8 buffer, followed by 2 column volumes of pH 6.3 buffer o Step 6: Separate the bound histidine tagged IA3 from the nickel column by washing the column with 2 column volumes of pH 4 buffer. Further purification or characteri zation of the hi stidine tagged IA3 can be done using size exclusion chromatography. The presence of DN A was tested for and was not found in purified proteins following this purifica tion scheme. The protein can then be dialyzed to a new buffer or to water and lyophilized. All histidine tagged IA3 proteins have molecular weight s around 8 to 9 kDa based on the amino acid sequences. SDS-PAGE gels of IA3 show that these proteins appear at their expected molecular weights (Figure 5-1). All IA3 variants and mutants run as expected under reducing and denaturing conditions, and display a band at the expected molecular weight. Purifying WT IA3 WT histidine tag free IA3 Purification of the WT IA3 presents a different challenge as compared to the other IA3 variants. The eight amino acids pr esent in the histidine tagged IA3 mutants provides an affinity for nickel, which aids in purification. The lack of this tag in the WT IA3 essentially eliminates

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121 this technique as a method for purificati on. Different properties of the WT IA3 need to be exploited, since the histidine tagged IA3 variants rely on nickel affinity columns for purification. The unstructured nature of IA3 can still be used to separate it from a solution simply by boiling or freezing. Another important property is the mobility of the WT protein through a size affinity column, as the molecule is relatively small at 68 residues (average protein length is between 300 and 400 residues) ( 232-234). For an appropriate size gel filtration column WT IA3 may move slower than other components in a solution. However, size exclusion chromatography relies on globular proteins to make its size estimates. Because IA3 is unstructured, its mobility in a size exclusion column is different than a globular protein of the same molecular weight. An additional property is the charge of the WT molecule at a certain pH. Separation based on charge can be achieved using an anion exchange column (Figure 5-2). The protocol Preparation prepare the following buffers as indicated: o Anion Exchange buffer: 50mM phosphate buffer, at pH 8 Protocol o Step 1: Boil the sample as indicated in the histidine tagged IA3 protein protocol o Step 2: After boiling, rapidly fr eeze the samples on liquid nitrogen (or in a -80C fridge) o Step 3: Thaw the samples and centrifuge at 48,000 g for 20 minutes to pellet aggregates o Step 4: Filter the samples through a .2 micron filter o Step 5: load the sample onto a Superd ex 75 (16/60) column which has been equilibrated with anion exchange buffer o Step 6: Run the protein solution through the column and collect the peak of interest o Step 7: Run the collected fractions through a HiTrap QFF column which has been equilibrated with anion exchange buffer and collect the flow through.

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122 The protein can then be dialyzed to a new buffer, or to water and lyophilized. Cysteine mutants and dimer formation The cysteine mutants of IA3 pose a unique concern as compared to the other IA3 variants. The addition of a sulfhydryl gr oup in these mutants adds a very reactive and strongly reducing residue to the protein. Cy steine residues in two IA3 molecules can easily be oxidized, forming a disulfide bridge. Dimer formation c ould result in a radi cally different IA3 molecule. A dimer would affect resu lts which characterize IA3. The most obvious differences would be in molecular weight and the size of the protei n. Dimerization would result in a protein which is (roughly) double in molecular weight, while th e apparent 'size' of the molecule could potentially double as well. The formation of di sulfide bonds affects higher order structures in proteins by linking protei n molecules together. Purification of all IA3 mutants can be followed by SDS-PAGE gel analysis, and shows IA3 as a band around its expected weight (Figure 5-1). All IA3 mutants display similar characteristics in gel filtration with the exception of mutants wh ich involve a cysteine mutation (Figures 5-3 and 5-4). These mutants displa y anomalous mobility during gel filtration chromatography as compared with WT IA3, WT(his) IA3, or other IA3 mutations (Figure 5-3). Denaturing and reducing conditions during gel analysis indicate th at cysteine mutants elute in later fractions from gel filtration chromatography (Figure 5-4). It is possible that dimer formation between two IA3 molecules through disulfide bonding of the free cysteine residues is the cause of retarded mobility within the gel filtration column. Native gels were used to evaluate possible dimer formation of the cysteine mutants of IA3 (Figure 5-5). Native gels are similar to SDS-P AGE gels, however they di ffer in the removal of SDS and BME, and the samples are not boiled ( 235, 236). Without the denaturing effects of BME and SDS, a protein may still retain some three-dimensional structure, as opposed to

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123 adopting a more open structure in the presence of BME and SDS ( 235, 236). With SDS removed, the charge of the protein can influe nce its mobility with a gel, since the negative charges of SDS no longer coat the protein ( 235, 236). Essentially the native charge of the protein is no longer masked out by a ne t negative charge due to SDS binding. Due to these effects, mobility of a protein wi thin a native gel is significantly different as compared to an SDS-PAGE gel ( 236). Proteins migrate through the gel according to their electrophoretic mobility; however, the sieving propert ies of the gel resist their movement within the gel ( 237-242). Lower molecular weight proteins are generally smaller and encounter less of this retarding force; consequently they migrate mo re quickly through a gel th an larger proteins of higher molecular weight ( 237-242). Native gels of the IA3 cysteine mutants could indicate if dimer formation is present. Samples of IA3 cysteine mutants were run on native gels and SDS-PAGE gels of similar acrylamide composition (Figures 56). In Figure 5-6 the K24C IA3 single mutant appears similar to WT IA3, suggesting that this mutant has not dimerized. The other cystine mutations, however, exhibit very different responses in the native gel. All of the cystine double mutants, and the K16C single mutant show significant smearing (Figures 5-5 and 5-6) The N2W-K16C IA3 double mutant displays a band which is in line with WT IA3, however it displays prominent bands above and below as well. All cystine mu tants which contain the K16C mutation show a band with increased mobility below WT IA3, however the large smear below this band is confusing. The SDS-PAGE gel of the same sa mples shows all the samp les in line with WT IA3. The results of this analysis indicate that IA3 mutants containing the K16C mutation are forming structures which are not the same as WT IA3.

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124 Dansyl Labeling IA3 Specifics of the Reaction The thiol reactive probe 1,5-IAEDANS is used to add a dansyl moiety to IA3 cysteine mutants. The reaction mechanism is nucleophilic bimolecular substitution ( 243) (Figure 5-7). In this mechanism cysteine acts as a nucleophile towards the IAEDANS molecule (243 ). The iodo group in the IAEDANS molecule draws electron s towards itself, causing a partial positive character on the adjacent carbon. The negative charge on the reactive sulfhydryl group is attracted to this carbon immediatel y attached to the iodo group ( 243). This not only enhances the attraction of the sulfhydryl group towards the carbon, but makes the iodo group an excellent candidate as a leaving group in the reaction ( 243). The sulfhydryl a ttacks the carbon as a backside attack, essentia lly popping off the iodo group ( 243). The Protocol The protocol provided by Mo lecular Probes (now owned by Invitrogen) has changed several times over the last 6 years. Over this ti me there has been varying degrees of success with these supplied protocols. Understanding the reaction allowed creation of a simple protocol which works well at room temperature. The reacti on is sufficiently efficien t using this protocol to achieve 50-80% labeling. Preparation prepare the following buffer solutions as indicated: o Labeling buffer: 50 mM phosphate buffer at pH 7.3 o Dye solution: prepare the dye at a 20x mo le concentration of dye to protein. Dissolve the IAEDANS dye in DMF using as little DMF as possible. A preferred dye concentration is 10 mM. Wrap foil around the dye solution to protect the solution from light. Protocol o Step 1: prepare the protein at a concentration of 10-100 M in labeling buffer o Step 2: add TCEP to a final concentra tion of 10 times the protein concentration

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125 o Step 3: allow the solution to stir for 10 minutes at room temperature o Step 4: cover the flask with foil o Step 5: add the dye solution dropwise to the reaction flask o Step 6: blow nitrogen over the reaction then tightly cap o Step 7: allow the reaction to stir at room temperature for 2 hours Purification of the labeled IA3 from the reaction mixture can be done using a Vydac C18 reverse phase column and watching for a peak at both 278 nm (for tryptop han) and 337 nm (and dansyl) (Figure 5-8). Unreacted dansyl, salts and DMF leave the column early, followed by the labeled IA3, then unlabeled material. The labeled fr actions can also be seen by shining a UV light on the collected fractions. Working With YPrA Purifying YPrA Why is purification necessary? YPrA that is purchased from Sigma may not have a purity that is satisfactory for our purposes. Past experience has shown that different lots of YPrA could contain a contaminating protease (possibly YPrB) ( private communication, T. Green ). This poses two problems. IA3 is a specific inhibitor towards YPrA, and may not in hibit the other protease. Additionally, the contaminating protease may proteolyze both YPrA and IA3. For these reasons further purification of the YPrA is necessary before use. Purification concerns Purification of YPrA is also problematic since the protease has been known to degrade itself. Two methods are used to combat the possi bility of self degradation. The protease is active at acidic pH 4.5. All of the separation is done at pH of 6.5 in a buffer in which the

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126 protease is stable but has a minimal activity. Ad ditionally, the whole purification is done at 4C to lower the amount of thermal energy present for proteolysis to occur. The protocol Preparation prepare the following buffers as indicated: o Buffer A: 20 mM phosphate buffer with 50 mM NaCl, at pH 6.5 o Buffer B: 0.1 M NaAc, pH 4.5 o PMSF solution: 100 mM phenylmethylsulphony l fluoride (PMSF) in isopropanol o Equilibrate a Sephacryl (S-100) High Resolution column with Buffer A, place at 4C Protocol Step 1: Dissolve YPrA into 500 uL of Buffe r A, and add to it 10 uL of PMSF solution Step 2: Load the sample onto the S-100 column Step 3: Run the column at 4C, collecting 2 mL fractions, this will take approximately 3 hours. Collect the second peak. Step 4: Concentrate the pooled fractions us ing an Amicon concentration cell with a 3,000 NWML membrane. Do this at 4C. Step 5: Switch the buffers from Buffer A to Buffer B using the Amicon cell. YPrA will be active in this buffer. Aliquot the sample into 1 mL vials, and store at -80 until needed. The Stability of YPrA Stability of YPrA is crucial for several of the experiments in this disse rtation. The role of YPrA is to proteolytically degrade other protein substrates found within the same vesicle as itself ( 223). This action has a preference for other subs trates, and disfavors de gradation of itself and its inhibitor IA3 ( 222). Nevertheless, at high concentra tions or given enough time, YPrA could degrade itself or its inhibitor. Temperature stability of YPrA In an SDS-PAGE gel, mature YPrA appears as a band at ~42 kDa ( 132). Stability studies of YPrA under these conditions can be enlightening. As a protease, YPrA can cleave other

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127 proteins including itself. A concern during any e xperiment is whether YPrA will cleave itself as data are being gathered. Twenty micromolar YPrA was allowed to equilibrate in 0.1M NaAc at pH 4.5. SDS-PAGE gels of a time course of YPrA allowed to equilibrate at various temperatures would show if YPrA is cleaving itself (Figure 59). YPrA which is not degraded would show up as a clear band in the gel, while the band for de graded YPrA would have a lower intensity, and possibly appear 'smeared' depending on the size a nd concentration of the degradation products. While there may be slight degr adation at the highest temperature and longest time period, it appears that 20 uM YPrA in 0. 1M NaAc, pH 4.5 is stable up to 3 hours at 55C (Figures 5-9 and 5-10). Stability of the YPrA and IA3 complex Once it has been realized that YPrA appears st able over the course of a 3 hour experiment, the next concern is for the YPrAIA3 complex. It was important for us to verify that IA3 would not be degraded by YPrA over the course of an experiment. One to one concentrations of YPrA and IA3 were allowed to equilibrate in 0.1M NaAc at pH 4.5 for various amounts of time at 25C (Figure 5-11). These results indi cate that YPrA does not degrade IA3 at 25C for up to 3 hours. Circular Dichroism Experiments What Is CD Used For? The differential absorption of circularly pol arized light can be used to identify and characterize proteins and secondary structures ( 244 ). The measurement of the absorption of right and left circularly polarized light from a sa mple is known as circular dichroism spectroscopy ( 244). Secondary structures presen t in proteins cause distinct bands of differential absorption which can be viewed using CD (Figure 5-12). While CD can illustrate the overall secondary structural ch aracter of a protein, it is probably best used to monitor secondary structur al changes as a function of temperature or an

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128 additional chemical. Wavelength scans can be used to assess secondary structural changes as a denaturant or renaturant is titr ated into the sample (Figure 5-13). Temperature scans involve monitoring a certain wavelength wh ile changing the temperature of a sample (Figure 5-14). These two modes are not independent of one a nother, and can be combined to assess how chemical addition and temperature simultaneously affect a sample. Experimental Setup CD wavelength scan experiments were typical ly done in the far-UV, from 190 nm to 260 nm. At these wavelengths differential absorptio n by secondary structures in proteins can be observed. Scans were taken with a 1 nm step si ze with a settling time of 3 seconds and a data collection and averag ing time of 1-5 seconds. CD temperature scan experiments could typically be taken from 5C to 85C at 2-10C increments The temperature equilibration time depended on the particular experiment, however usual equilibration times were 1-5 minutes. Data were collected for 1-5 seconds at each temperature. The instruments used for these experiments were all Aviv CD spectrometers, models 202, 215 and 400 using CD software from Aviv Biomedical, Inc. (Lakewood, NJ). ITC Experiments What Is ITC? Molecular level thermodynamics can point towards molecular interactions between molecules, and the energetics a ssociated with their binding ( 211 ). Association events can be thermodynamically characterized by several parameters ( 201): Association constant ( Ka) defined as the ratio of complex to reactants, Stoichiometric ratio of interacting molecules ( n ), Enthalpy of the binding interaction ( H ), Entropy of the binding interaction ( S), Free energy of the binding interaction ( G ), Change in heat capacity associated with binding ( Cp).

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129 Many methods exist to characterize molecular in teractions (such as equilibrium dialysis and radio ligand binding assays), however most of them are indirect methods that rely on an additional phenomenon or molecule to report on th e recognition events, but are external to these events ( 245, 246 ). One method which can be used to investigate binding events directly is isothermal titration calorimetry (ITC) ( 73, 247). All binding events either release energy (exothermic) or absorb energy (endothermic) ( 72, 73, 197, 202 ). This evolved heat can be directly measured by ITC, and can be used to determine the thermodynamic parameters mentioned above ( 248, 249). The ITC Instrument An ITC instrument can be generally characterized as a sensitive calorimeter. The instrument is conceptually simple, consisting of two identical cells made of a highly conductive material and surrounded by an adiabatic jacket ( 73 ) (Figure 5-15). Ther mocouples detect the difference in temperature between the cells and the jacket and between the cells themselves ( 72, 73). Heaters attached to the jacket and cells ma intain these elements at identical temperatures ( 72, 73). An ITC Experiment To understand an ITC experiment, consider the simple reaction of two molecules, A and B, binding together to form the complex AB ( 250, 251). (5-1) The reaction is simple, and can exemplify many different biological pr ocesses such as RNAprotein interactions (252 ), DNA-protein ( 253), lipid-peptide ( 254), antibody-antigen ( 203, 255), hormone-receptor ( 72 ), enzyme-substrate (245, 256), or various other prot ein-protein interactions

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130 ( 196-199, 257-259 ). For simplicity we will consider B to be the substrate or ligand to A, the macromolecule. In an ITC experiment, a solution of macromolecule A is placed the sample cell. The reference cell is filled with a solution w ith known thermal characteristics (usually water) ( 260). A solution of ligand B is placed in the inj ection syringe. In this configuration A is the titrand and B is the titrant. During each ITC experiment a specific volume of th e titrant is injected into the sample cell while the contents are rapidly stirred. At each injection of titran t into the cell, heat is absorbed or released depending on the nature of the molecular interactions taking place ( 72, 73, 261). An active feedback circuit maintains both the samp le cell and the reference cell at the same temperature during each experiment ( 72). The observable quantity measured during the ex periment is the time dependent change in electrical power necessary to maintain equivalent temperatures in both the sample and reference cell ( 72, 73, 261 ). If the molecular interactions are exothermic, then the temperature in the sample cell will be higher than the reference cell and power to the heaters is decreased to allow the temperature between the cells to be maintained ( 73). If the interactions are endothermic, the temperature in the sample cell is lower than the reference cell and the power to the heater is increased. The time between each injection can be varied; however sufficient time should be taken between each titration to allow for the temper ature in the sample cells to equilibrate with respect to the reference cell ( 72). During each titration a certain amount of heat is absorbed or released ( 262 ). This heat evolution is proportional to the number and type of molecular interact ions which take place between the molecules within the sample cell, or the number of molecules of ligand which are bound by the macromolecule ( 202, 261 ). During initial injections, all of the ligand injected will

PAGE 131

131 be bound up by the macromolecule. This will be obser ved as a large change in heat evolved and consequently a large signal recorded as the active feedback circuit compensates for the heat difference by changing the power to the sample heater ( 73). As the ligand concentration increases during subsequent injections, the individual molecules of the macromolecule A become saturated, leaving fewer binding ev ents to occur, and consequently less heat evolution to take place. Eventually all binding sites are saturated, with further injections registering small heat changes resulting from dilution of the titrant into the sample cell volume ( 72). Analyzing ITC Data ITC data are collected as the power required to maintain equivalent temperatures in both the sample and reference cell ( 73). This value can be correlated to the amount of titrant present in the cell, plotted against time, and integrated to yield a titration curve. There are many models which could be used to fit titration data ( 72, 73, 201 ). The model used to fit a titration of IA3 into YPrA is outlined below. Using Equation 5-1, we could write the association or binding constant, K as: fBf f K 1 (5-2) where f is the fraction of sites on the enzyme that are occupied by the ligand B, and Bf is the concentration of free ligand. This equation a ssumes that all binding sites on the enzyme are equivalent. We can also write down the total concentration of the ligand BT as: T fTAfnBB (5-3) for n, the number of binding sites or binding stoi chiometry, and the total concentration of the macromolecule, AT. Combining these equations we can solve for f 0 1 12 T T T TnA K nKAnA B ff (5-4)

PAGE 132

132 Additionally, we can write an equation for th e total heat released into the solution ( Q ) in the sample cell volume V0 in terms of the change in enthalpy due to binding ( H ). HVAfnQT 0 (5-5) Solving the quadratic in Equation 5-4 for f and substituting this into Equation 5-5 results in Equation 5-6. T T T T T T T T oTnA B nKAnA B nKAnA B HVnAQ 4 1 1 1 1 2 12 (5-6) Using Equation 5-6 and given a binding stoichiometry ( n), an association constant ( K ) and a change in enthalpy of binding ( H ), we can calculate a heat content of the solution. A nonlinear least squares fit can produce a fit of Equation 5-6 to the data. Uncertainties in these values can be found using the bootstrapping method.

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133 Figure 5-1: Representative 10% SDS-PAGE protein gel for seve ral of the histidine tagged IA3 proteins indicating similar molecular weights.

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134 Figure 5-2: Anion exchange chromatography of WT IA3 (without histidine tag). A) Results of purification. WT IA3 is not trapped by the anion exchange column and can be found in fraction A5. B) Gel of fractions from the several purifications, with results similar to panel A. The protein standard is marked as S in the gel.

PAGE 135

135 Figure 5-3: Purified IA3 WT(his) protein elution profile from an S75 colu mn elutes in fraction B12. A) Elution profile; this result is typical for any noncysteine containing IA3. B) The peak is confirmed by SDS-PAGE gel analysis. The protein standard is marked as S which matches with fraction B12 as the WT(his) IA3.

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136 Figure 5-4: Elution profile of a cysteine mutant of IA3. A) Cysteine mutants display anomalous retention times in an S75 column. Inst ead of eluting around fraction B12, these mutants elute around fraction C10. B) This is confirmed by SDS-PAGE gel analysis. The protein standard is marked as S.

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137 Figure 5-5: A 7% native gel of several IA3 proteins illustrating the differences between cysteine mutants and WT(his) IA3.

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138 Figure 5-6: SDS-PA GE and native gel results for several IA3 mutants illustrating odd mobility that can be seen in the native gel, whil e the SDS-PAGE gel shows similarity among the proteins.

PAGE 139

139 Figure 5-7: Nucleophilic substi tution reaction which labels cy steine with a dansyl moiety.

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140 Figure 5-8: Purifying labeled IA3 using HPLC over a C18 reverse phase column. The blue line is absorbance at 280 nm, the red line is abso rbance at 337 nm for dansyl. The labeled protein elutes at 18 minutes, followed by the unlabeled protein. Elution peaks at 5 minutes are the unreacted components in solution or salts.

PAGE 141

141 Figure 5-9: Gel analysis of YP rA temperature stability. YPrA was allowed to equilibrate for different amounts of time at different temp eratures. The black ramp above each gel indicates the increase in temp erature. A) A marker is in lanes 1 and 10, initial YPrA (before temperature equilibration) is in lane s 2, 3 and 9, lanes 4 through 8 have been allowed to equilibrate for 10 minutes at 4C, 15C, 25C, 35C, and 55C respectively. B) A marker is in lanes 1 and 10, initial YPrA (before temperature equilibration) is in lanes 2 and 9, lane 3 is empty, lanes 4 through 8 have been allowed to equilibrate for 45 minutes at 4C, 15C, 25C, 35C, and 55C respectively. C) A marker is in lanes 1 and 10, initial YPrA (before temperature equilibration) is in lanes 2 and 9, lane 3 is empty, lanes 4 through 8 have been allowed to equilibrate for 3 hours at 4C, 15C, 25C, 35C, and 55C respectively.

PAGE 142

142 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0102030405060 temperature (C)band density 10 minutes 45 minutes 3 hours Figure 5-10: Densitometry results of the gels in Figure 5-9.

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143 Figure 5-11: YPrAIA3 complex stability. Complexes of YPrA and IA3 were allowed to equilibrate at two different temperatures for increasing amounts of time. Both panels show gels of the YPrAIA3 complex, with panel A at 4C, while panel B was at 25C. The complexes were allowed to equilibrate for 0, 30 minutes, 1 hour, 2 hours and 3 hours as denoted by the black wedge above the appropriate la nes of each gel.

PAGE 144

144 Figure 5-12: Typical CD spectra for various secondary structures.

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145 Figure 5-13: CD wavelengt h spectra of WT(his) IA3 in 15% TFE at temperatures ranging from 5C to 85C.

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146 Figure 5-14: CD temperature scan at a wavelength of 222 nm of WT(his) IA3 in 15% TFE indicating the change in CD signa l as the temperature increases.

PAGE 147

147 Figure 5-15: An ITC instrument.

PAGE 148

148 CHAPTER 6 FUTURE DIRECTIONS AND CONCLUSIONS Future Directions The investigation of the foldi ng and binding interactions of IA3 with YPrA is not complete. This is mainly due to several interesting questi ons and observations that were raised during the course of my study which point towa rd other experiments. Time is always a factor in research. Satisfactorily resolving the experiments on hand always take precedence over pursuing new experimental studies. This does not stifle research, but rather makes an interested scientist yearn for more hours in a day, and more days in a week This section on Future Directions is a short listing of several experiments which would sh ed more light on the interaction of IA3 and YPrA, pushing our understanding of intrinsically disordered proteins forward. N-terminal IA3 Kinetics In chapter 3 laser induced temperature jump spectroscopy highlighted a rate enhancement in the folding of IA3 in the presence of YPrA. These stud ies logically lead to an interesting question. Does the C-terminus of the inhibitor cause this rate enhancem ent? Construction of a truncation mutant containing only the N-terminus and the N2W and dansylated K16C mutation would be necessary to investigat e this question. A lack of rate enhancement from this Nterminal construct would provide direct evidence of the interaction of the C-terminus with YPrA, and an explanation of its function. Calorimetry Repetition of ITC experiments is needed to ve rify the initial ITC results in chapter 4. However these results indicated that the C-termi nus of the inhibitor is interacting with the protease. Good measurements of the asso ciation constant were impossible since IA3 has a high affinity for YPrA with a subnanom olar inhibition constant. Severa l authors have now shown that

PAGE 149

149 association constants for tight bi nding inhibitors can be measured A possible experiment would consist of the strong binding inhibitor IA3 being titrated into YPrA which is saturated with a weak binding inhibitor. Weak binding inhibito rs could be one of se veral weak binding IA3 mutants or a small molecule inhibitor. DSC experiments could provide more informa tion about the energetic s as well. Melting point temperatures of IA3 in the presence of TFE would be useful in further verifying TFEmidpoint values found in chapters 2 and 3. Additio nally, melting curves of YPrA and various IA3 mutants may provide clues as to the Cterminal interactions of IA3 to YPrA. Inactive YPrA In our hands, we have never been successfu lly able to produce active, mature YPrA. While being able to make YPrA would be a boon both in terms of YPrA supply and cost, another interesting advantage would be in making catalyti cally inactive YPrA. Mutation of the catalytic aspartates (asp residues 32 and 215) within the active site of the protease would allow for surface interaction studies of peptides/p roteins with YPrA without the f ear of proteolytic degradation by YPrA. Examination of a truncation mutant containing only the C-terminus of IA3 and its interaction with YPrA could provi de complementary evidence to the studies undert aken in this dissertation. Additionally, othe r experimental studies of the in teraction between catalytically inactive YPrA and IA3 may provide more insight into the binding mechanism of the inhibitor. C-terminal EPR Spectroscopy Regions with low electron density in the crystal structure of the YPrAIA3 complex can be interpreted as dynamic regions or mob ile regions. The C-terminus of IA3 is not seen in the crystal structure, and additionally is not necessary for inhibition of the protease. However, we are proposing that the C-terminus interacts via no n-specific charge interac tions, weakly with the surface of YPrA. EPR experiments of the WT IA3 with YPrA could provide more evidence of

PAGE 150

150 the interaction of the C-terminus of the inhibito r with the protease. EPR labels systematically placed in each residue in the C-terminus of IA3 may highlight which residues are nonspecifically interacting with YPrA. Mutati ng these residues may lead to a means of modulating the folding rate enhancement seen in chapter 3. Conclusions In this dissertation I have detailed severa l experiments to understand the folding and binding processes involved in IA3 and its interaction with YPrA. Using IA3 as a model system, I have investigated the benefits of intrinsic disorder. The work in this dissertation has focused on intrinsic disorder and the IA3YPrA system, and is summarized in this section. NMR was used in chapter 2 to understand the residue level movements of the amino acids in IA3 and their contribution to a folded IA3 structure as a function of TFE concentration. This chapter highlighted that while a structure derived from NMR da ta would be useful, it is not needed to assess the residue movements of the pr otein in TFE. This was advantageous since solving a structure from NMR data at each TFE concentration would be time consuming. Instead relatively quick HSQC experiments were combined with a few 3D NMR experiments at interspersed TFE concentrations to derive a picture of the relativ e movements of the residues as a function of TFE. Additionally, SVD was used in chapter 2 to emphasize data arising from the residue movements of the inhibitor above the noise in the data, and investigate how the folding transition varied with TFE and in which residues was this transition most prominent. Datasets were decomposed using SVD into two datasets, one which contained the relative residue movements and one which contained the TFE dependence. Analysis of the residue movements indicated that while residues throughout the sequence of IA3 showed an increased helical propensity wi th the addition of TFE, residues in the N-

PAGE 151

151 terminus of the protein exhibited a greater tran sition towards an alpha-helical structure than residues in the C-terminus of the protein. The residues which demonstrated the greatest transition to an alpha-helical structur e were also the same residues of IA3 found in the crystal structure of the YPrAIA3 complex. This suggests that while the whole protein may be affected by the unstructured to structured transition, residue s in the inhibitory N-te rminus of the protein show more of this transition as a function of TFE concentration. SVD analysis of the data was surprising in that three components of TFE dependence were identified in our data. Two SVD components were well fit by a two-st ate model of folding, which could be represented as an unfolded popul ation of molecules tran sitioning to a folded population of molecules as the TFE concentration is raised. However, a weakly populated third component was present in the data as well, sugges ting that a two-state desc ription of the folding of IA3 may not be entirely complete. Taken together these data suggest that while all the residues in the protein are undergoing largely two-state transition, residues in the N-term inus of the protein exhi bit this transition to a larger degree. Considering that the C-terminus of the protein is not ne eded for inhibition of YPrA, and that it is not found in the crystal structure of the IA3YPrA complex, it is possible that the third component may be relate d to the C-terminus of the inhi bitor and its inte ractions with YPrA. In chapter 3, the kine tics of folding of IA3 was examined in TFE or with YPrA using FRET and laser induced temperature jump spectro scopy. The addition of fluorescence to the inhibitor, both by mutation and fluorescence la beling, provided a convenient way to assess folding, by taking advantage of the distance ba sed resonance energy transfer using FRET.

PAGE 152

152 Temperature jump spectroscopy was used to investigate the kinetics of the folding/unfolding transition. We have so far assessed the folding rate constants of IA3 in water for various temperatures. This is an important step in eluc idating the folding/binding process IA3 goes through in the presence of YPrA. Preliminary results indicate th at the folding of the Nterminus of the protein is enhanced by the presence of YPrA. This suggests that the interaction of IA3 with YPrA affects folding of the portion of IA3 which is necessary for inhibition of the protease. This may be an indicator that the unstructured C-terminus of th e protein is interacting with YPrA to promote folding of the N-terminus of IA3. Examination to see if this effect is present in a truncated IA3 containing only the N-terminus of the protein is necessary, however be yond the scope of this dissertation chapter. This was el aborated upon in the Future Direct ions section of this chapter. Energetics of the interaction of interaction of IA3 with YPrA was investigated in chapter 4 using ITC. ITC experiments allowed us to inve stigate the interaction of YPrA with several constructs of IA3: an N-terminus only construct, WT IA3, and WT IA3 containing a C-terminal his6 addition. These IA3 proteins differ only in their C-termin i which display different charges at the experimental pH of 4.5. Non-specific intera ctions between the C-termini of these molecules and YPrA were deduced using ITC. These results indicate that the C-terminus of the IA3 molecule is indeed interacting with the surface of YPrA. The action of the unstructured C-terminus of IA3 may be to enhance the meeting of the inhibitor N-terminus of IA3 with partner residues in the flaps of YPrA. Weak, non-specific charge interactions between the C-terminus of IA3 may not be lasting enough to be seen in the crystal structure of the complex, but are effectively raising the local concentration of

PAGE 153

153 the N-terminus around the active site of YPrA. This work also provides evidence of the flycasting effect due to the uns tructured C-terminus of IA3. This dissertation has looked at the folding and binding of IA3. IA3 provides a unique model to investigate and understand the benefits of intrinsic disord er within a protein. Several biophysical techniques and analysis were used to assess the folding of IA3 in TFE, or the coupled folding and binding of IA3 in the presence of YPrA. The results of this dissertation indicate that this small 68 amino acid protein displays a ri ch and complex set of responses. The understood interaction between IA3 and YPrA is inhibition due to bindin g of the N-terminus into YPrA, and intrinsic disorder in the inhibito r appears to enhance this inhibitory action. Understanding of the benefits of intrinsic disorder in IA3 could lead to not only a gene ral understanding of intrinsically disordered proteins, but the deve lopment of novel inhibitory proteins/peptides which are able to exploit disordered regions to enhance their inhibition.

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154 APPENDIX PROGRAMMING CODE Why Programming? My research has consisted of a fair amount of programming in a variety of scripts and languages. This is useful since it provides a way to more fully understand the mechanics of data analysis. Additionally, it leads away from a dependence on supplied programs which inevitably can act like black boxes for research (where data is stuffed into one end of the black box and numbers sprout out the other leading to a low understanding of how the analysis was done). It can be argued that this is akin to reinve nting the wheel for some analyses. While this may be a fact, it is probably tr ue that researchers on the cuttin g edge of science should have a strong grasp of any analysis of their data. Errors or inconsistencies may be missed or misinterpreted if there is a dependence on analysis software that is incorrectly programmed. One would hope that a software package to analyze data would always work correctly. This, sadly, is not a rule and can be seen in the various iterations a piece of software goes through over time. Two main advantages of programming to a rese archer are the ability to do complicated analyses, and to aid in re petitive computer tasks. Much data analysis can be done (and is often done) using Excel. While this provides a conveni ent way to quickly anal yze data, it is also confining in the types of analysis that can be done. Complicated fitting, analysis, or advanced mathematical procedures are beyond its capabilities. While it is an excell ent tool for the little that it does, reliance on Excel (or Excel like spread sheet programs) certainly limits data analysis. The other advantage, programming to lower repetit ive actions, is strictly a time saver. Spending an hour to code together a program to end a repetit ive set of actions is time well spent. This is especially true in a field of study where time sp ent on repetitive actions means time away from other productive tasks such as data gathering or analysis.

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155 One Program to Rule Them All? A common problem when deciding to start programming is which language/script should be used? The answer to this question unfortunately varies depending on the situation and the problem needing to be answered. More often than not, however, there are multiple choices available in terms of programming languages/scri pts to solve a particular problem. This does mean that a researcher needs to be flexible enough to learn and work with many different languages. There is a benefit in that there is a similarity of logic be tween languages, and with each new language/script that is learned, it becomes easier to learn the next one. The scripts I have listed in this appendix are written in a variety of programs. Each one is particular to its situation. While there are other, and arguably better, ways (use of different languages, better coding style, c oding construction) to achieve the same results, these programs served me well when I needed them. I have done my best to comment the code appropriately and write code which could be followed easily, however there are some bits which were added ad hoc. For unintelligible code I apologize. Here I have included several scripts/programs which I found very useful. The Whole Shebang bash Scripting NMR indirect referencing This bash script was used to indirectly re ference the NMR values used in chapter 2. #!/bin/bash # # indirect referencing # a shell script (portable to any platform with standard bash and bc) # okg 8/23/05 # ### USER CONFIGURABLE OPTIONS # # How much precision (digits) do you want

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156 scale=12 # # Chemical Shift Ratios # current ratios can be found at: # http://beta.bmrb.wisc.edu/ref_info/cshift.html X1H=1.00 # 1H (DSS) X15N=0.101329118 # 15N-1H (DSS) X13C=0.251449530 # 13C-1H (DSS) # ###### NO MORE CHANGES BELOW THIS LINE ### FUNCTIONS # calc_freq() calculates the frequency based on # Chemical Shift Ratio # 1H DSS signal (in MHz) # Nucleus signal (in MHz) calc_freq() { XCSR=$1 # chemical shift ratio of interest DSS=$2 # MHz value of DSS Nuc=$3 # MHz value of Nucleus of interest zeroNuc=`echo "${DSS}*${XCSR}" | bc` #echo "scale=${scale} ; ((${Nuc}-${zeroNuc})*1000000)/${Nuc}" NucPPM=`echo "scale=${scale} ; ((${Nuc}-${zeroNuc})*1000000)/${Nuc}" | bc` echo $NucPPM } # exit_check() checks to see if input is null and exits if it is exit_check() { if [ -z $1 ] then exit 0 fi } ########### ### CASE STATEMENTS base=${0##*/} # get the prgram basename # case $1 in help) # need help? echo Returns frequencies in ppm from: 1H frequency at DSS in MHz 1H carrier frequency in MHz (sfo1) 15N and 13C carrier frequencies (sfo2/3) usage: $base [option] options: with no option present, $base will function normally equations equations for indirect referencing license display the license and quit help show this help then quit exit 0;; # exit cleanly license | lic ) # license information echo

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157 This work was based on \"reference.exe\" a FORTRAN program by Dr. A. S. Edison. \"${base}\" is licensed by O. Ganesh under the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License http://creativecommons.org/licenses/by-nc-sa/2.5/legalcode any derivative works based on this work shall reference the author exit 0;; # exits cleanly equation | equations | eq | eqs) echo Indirect Chemical Shift Referencing DSS = 1H DSS frequency in MHz Nuc = nucleus frequency in MHz Xcsr = Chemical Shift Ratio for nucleus of interest calculation of 0ppm freq: 0ppm = DSS Xcsr indirect referenced frequency (ppm): ind_ref = ((Nuc 0ppm) 1e6) / Nuc exit 0;; *) ;; # catch all for anything else (currently not used) esac ########### ### THE PROGRAM (finally!) echo use '$base help' for help Returns frequencies (ppm) from: 1H frequency at DSS in MHz 1H carrier frequency in MHz (sfo1) 15N and 13C carrier frequencies (sfo2/3) Enter 1H DSS frequency in MHz: read DSS ; exit_check $DSS # gets 1H-DSS freq and checks it echo "Enter 1H carrier frequency in MHz: read H ; exit_check $H # gets 1H freq and checks it HPPM=`calc_freq $X1H $DSS $H` # calculates the 1H referenced frequency echo "DSS: $DSS 1H: $H indirect H1(ppm) = $HPPM echo "Enter 15N frequency in MHz: read N ; exit_check $N # gets 15N freq and checks it NPPM=`calc_freq $X15N $DSS $N` # calculates the 15N referenced frequency echo "DSS: $DSS 15N: $N indirect N15(ppm) = $NPPM echo "Enter 13C frequency in MHz: read C ; exit_check $C # gets 13C freq and checks it CPPM=`calc_freq $X13C $DSS $C` # calculates the 13C referenced frequency echo "DSS: $DSS 13C: $C indirect C13(ppm) = $CPPM exit 0 # exit cleanly

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158 Web Scraping Using Ruby (via bash) Web scraping is the name given to any form of extracting data from a webpage/website over the internet. This is importa nt and useful as scientific info rmation/data is increasingly being moved online (examples of this are PubMed, the Protein Data Bank, or WormBase). Unfortunately, there is no common 'information format' which is readily accessible. Each database may have its own described format which it may or may not adhere to. Because of this, collecting, collating, and correlating large amounts of information for local analysis can become a tedious task. There are many ways to scrape information from a website, however my tools of choice have become scRUBYt! and Hapricot. Hpricot is excellent at working through structured data such as HTML or XML, while scRUBYt! handles forms on websites well. Ruby is an evolving language and has odd characteristics which have not been ironed out In this incomplete code snippet I am calling ruby via bash to scrape data from aprs.he.fi. The data are then parsed and put into an array for later work. #!/bin/bash # output for the user echo "Gathering the data from aprs.he.fi" # build ruby code ruby_call=" require 'rubygems' require 'scrubyt' aprs_data = Scrubyt::Extractor.define do fetch 'http://aprs.he.fi' fill_textfield 'nick', 'me' submit fetch 'http://aprs.he.fi/?c=raw&call=N6WPV-3&limit=1000' tracking_info \"/html/body/span[@class='raw_line']\" end aprs_data.to_xml.write(\$stdout, 1)" # call it out to ruby # this is not clean because scRUBYt! is still beta

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159 # hpricot would be better, but can't handle forms like scRUBYt! raw_data=`echo "$ruby_call" | ruby` # output for the user tput cuu1; echo "Parsing the data and building arrays" # parse out the dates dates=`echo "$raw_data" | grep "tracking_info" | grep "A=0" | \ sed "s/ GMT:/>/" | sed "s/A=/>/" | cut -d">" -f2` # parse out the altitude alt=`echo "$raw_data" | grep "tracking_info" | grep "A=0" | \ sed "s/ GMT:/>/" | sed "s/A=/>/" | cut -d">" -f4 | \ cut -d" -f1` # make an altitude array for the output altitude=( $(echo "$alt") ) Perl Programming Sequence dependent chemical shifts The chemical shift of a residue can be influenced by its nearest residue neighbors, up to two neighbors on either side. Calculating the correction factor s can be done by hand; however, this is tedious and can be error-prone. This perl script automates the task, by reading in an amino acid sequence and generating the random coil shift values as well as the sequence dependent corrections. #!/usr/bin/perl -w # # correction.pl takes amino acid sequence and # generates the random coil shift values along # with sequence dependent corrections. # okg 8/28/05 # to run use 'correction.pl help' in UNIX/Mac/Linux # or use 'perl correction.pl help' in Windows # use strict; use warnings; use complete_db; # load the database module # Initialize variables my $primary_seq = '; my (@primary_seqa, $aa, $aaHN, $aaHA, $aaNH, $aaCA, $aaCO, $previi, $previiHN, $previiHA, $previiNH, $previiCA, $previiCO, $previ, $previHN, $previHA, $previNH, $previCA, $previCO, $nexti, $nextiHN, $nextiHA, $nextiNH, $nextiCA, $nextiCO,

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160 $nextii, $nextiiHN, $nextiiHA, $nextiiNH, $nextiiCA, $nextiiCO, $sumHN, $sumHA, $sumNH, $sumCA, $sumCO); my $proteinfilename=$ARGV[0]; my $outputfile=$ARGV[1]; # help sub show_help { print STDERR <$outputfile") ) { print "Cannot open file \"$outputfile\" to write to.\n";

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161 print "(maybe it's write-protected?)\n"; exit; } # initial titles for the output file print OUTFILE "AA Atom RC_val seq_dep_i-2 seq_dep_i-1 "; print OUTFILE "seq_dep_i+1 seq_dep_i+2 sum_of_all_corrections\n"; # read in the sequence from the protein file open(PROTEINFILE, $proteinfilename); @primary_seqa = ; $primary_seq = join( '', @primary_seqa); # Remove whitespace, clean up the input sequence, upper-case it $primary_seq =~ s/\s//g; chomp $primary_seq; $primary_seq = uc $primary_seq; # get the sequence length my $seq_length= length($primary_seq); # send the sequence to the screen for the user print "$primary_seq\nsequence length is $seq_length residues\n"; for(my $i=0; $i < $seq_length ; $i += 1) { # take care of the random coil values $aa = substr($primary_seq,$i,1); $aaHN = randcoilvals($aa 'HN'); $aaHA = randcoilvals($aa 'HA'); $aaNH = randcoilvals($aa 'NH'); $aaCA = randcoilvals($aa 'CA'); $aaCO = randcoilvals($aa 'CO'); # take care of the i-2 sequence dependence if ( $i > 1 ) { $previi = substr($primary_seq,$i-2,1); } else { $previi = '-'; } $previiHN =seq_dep_previi($previi 'HN'); $previiHA =seq_dep_previi($previi 'HA'); $previiNH =seq_dep_previi($previi 'NH'); $previiCA =seq_dep_previi($previi 'CA'); $previiCO =seq_dep_previi($previi 'CO'); # take care of the i-1 sequence dependence if ( $i > 0 ) { $previ = substr($primary_seq,$i-1,1); } else { $previ = '-'; } $previHN =seq_dep_previ($previ 'HN'); $previHA =seq_dep_previ($previ 'HA'); $previNH =seq_dep_previ($previ 'NH'); $previCA =seq_dep_previ($previ 'CA'); $previCO =seq_dep_previ($previ 'CO');

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162 # take care of the i+1 sequence dependence if ( $i + 2 > $seq_length ) { $nexti = '-'; } else { $nexti = substr($primary_seq,$i+1,1); } $nextiHN =seq_dep_nexti($nexti 'HN'); $nextiHA =seq_dep_nexti($nexti 'HA'); $nextiNH =seq_dep_nexti($nexti 'NH'); $nextiCA =seq_dep_nexti($nexti 'CA'); $nextiCO =seq_dep_nexti($nexti 'CO'); # take care of the i+2 sequence dependence if ( $i + 3 > $seq_length ) { $nextii = '-'; } else { $nextii = substr($primary_seq,$i+2,1); } $nextiiHN =seq_dep_nextii($nextii 'HN'); $nextiiHA =seq_dep_nextii($nextii 'HA'); $nextiiNH =seq_dep_nextii($nextii 'NH'); $nextiiCA =seq_dep_nextii($nextii 'CA'); $nextiiCO =seq_dep_nextii($nextii 'CO'); # sum up all the values # (RC + sd_i-2 + sd_i-1 + sd_i+1 + sd_i+2) $sumHN = $aaHN + $previiHN + $previHN + $nextiHN + $nextiiHN ; $sumHA = $aaHA + $previiHA + $previHA + $nextiHA + $nextiiHA ; $sumNH = $aaNH + $previiNH + $previNH + $nextiNH + $nextiiNH ; $sumCA = $aaCA + $previiCA + $previCA + $nextiCA + $nextiiCA ; $sumCO = $aaCO + $previiCO + $previCO + $nextiCO + $nextiiCO ; # and the nice output written to the output file print OUTFILE "$aa HN $aaHN $previiHN $previHN $nextiHN $nextiiHN $sumHN\n"; print OUTFILE CO $aaCO $previiCO $previCO $nextiCO $nextiiCO $sumCO\n"; print OUTFILE NH $aaNH $previiNH $previNH $nextiNH $nextiiNH $sumNH\n"; print OUTFILE Ca $aaCA $previiCA $previCA $nextiCA $nextiiCA $sumCA\n"; print OUTFILE Ha $aaHA $previiHA $previHA $nextiHA $nextiiHA $sumHA\n"; # updating output for the user print "."; } # close the output file close (OUTFILE); # final instructions for the user print "\ncomplete.\nOpen \"$outputfile\" in Gnumeric/OO/Excel (space delimited) for results\n"; exit;

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163 Database of random coil values The random coil values needed fo r the previous script are stor ed here. I decided to keep this database separate from the main program since it would ease in updating these values if they changed. # This is the complete database of random coil values # and sequence dependent effects on chemical shift values # # it is a module for easy updating # (so no one has to mess with the main program) # # To use this module, add the following line (without the comment) # to your code, making sure the module is in the same directory as # the program you are using it from (or another place where Perl can find # it). # # use complete_db.pl; # random coil values # # Schwarzinger S, Kroon GJ, Foss TR, Wright PE, Dyson HJ. # J Biomol NMR. 2000 Sep;18(1):43-8. # sub randcoilvals { my($aa) = @_; my(%aa_val) = ( "ACA" => "52.8", "ACO" => "178.5", "AHA" => "4.35", "AHN" => "8.35", "ANH" => "125", "CCA" => "55.6", "CCO" => "175.5", "CHA" => "4.76", "CHN" => "8.54", "CNH" => "118.7", "DCA" => "53", "DCO" => "175.9", "DHA" => "4.82", "DHN" => "8.56", "DNH" => "119.1", "ECA" => "56.1", "ECO" => "176.8", "EHA" => "4.42", "EHN" => "8.4", "ENH" => "120.2", "FCA" => "58.1", "FCO" => "176.6", "FHA" => "4.65", "FHN" => "8.31", "FNH" => "120.7", "GCA" => "45.4",

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164 "GCO" => "174.9", "GHA" => "4.02", "GHN" => "8.41", "GNH" => "107.5", "HCA" => "55.4", "HCO" => "175.1", "HHA" => "4.79", "HHN" => "8.56", "HNH" => "118.1", "ICA" => "61.6", "ICO" => "177.1", "IHA" => "4.21", "IHN" => "8.17", "INH" => "120.4", "KCA" => "56.7", "KCO" => "177.4", "KHA" => "4.36", "KHN" => "8.36", "KNH" => "121.6", "LCA" => "55.5", "LCO" => "178.2", "LHA" => "4.38", "LHN" => "8.28", "LNH" => "122.4", "MCA" => "55.8", "MCO" => "177.1", "MHA" => "4.52", "MHN" => "8.42", "MNH" => "120.3", "NCA" => "53.3", "NCO" => "176.1", "NHA" => "4.79", "NHN" => "8.51", "NNH" => "119", "PCA" => "63.7", "PCO" => "177.8", "PHA" => "4.45", "PHN" => "0", "PNH" => "0", "QCA" => "56.2", "QCO" => "176.8", "QHA" => "4.38", "QHN" => "8.44", "QNH" => "120.5", "RCA" => "56.5", "RCO" => "177.1", "RHA" => "4.38", "RHN" => "8.39", "RNH" => "121.2", "SCA" => "58.7", "SCO" => "175.4", "SHA" => "4.51", "SHN" => "8.43", "SNH" => "115.5", "TCA" => "62", "TCO" => "175.6", "THA" => "4.43",

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165 "THN" => "8.25", "TNH" => "112", "VCA" => "62.6", "VCO" => "177", "VHA" => "4.16", "VHN" => "8.16", "VNH" => "119.3", "WCA" => "57.6", "WCO" => "177.1", "WHA" => "4.7", "WHN" => "8.22", "WNH" => "122.1", "YCA" => "58.3", "YCO" => "176.7", "YHA" => "4.58", "YHN" => "8.26", "YNH" => "120.9", ); if(exists $aa_val{$aa}) { return $aa_val{$aa}; # looks like everything went well, continue on }else{ # something's not right! # instead of causing more problems, it's probably better to stop print STDERR "unexpected value: \"$aa\"!!\n"; exit; } } # sequence dependent corrections (i-2 amino acid) # # Schwarzinger S, Kroon GJ, Foss TR, Chung J, Wright PE, Dyson HJ. # J Am Chem Soc. 2001 Apr 4;123(13):2970-8. # sub seq_dep_previi { my($aa) = @_; my(%aa_val) = ( "ACA" => "-0.02", "ACO" => "-0.11", "AHA" => "-0.02", "AHN" => "-0.01", "ANH" => "-0.12", "CCA" => "-0.03", "CCO" => "-0.08", "CHA" => "-0.01", "CHN" => "0", "CNH" => "-0.06", "DCA" => "-0.03", "DCO" => "-0.08", "DHA" => "-0.02", "DHN" => "-0.02", "DNH" => "-0.12", "ECA" => "-0.01", "ECO" => "-0.09", "EHA" => "-0.02", "EHN" => "-0.01", "ENH" => "-0.06", "FCA" => "-0.07",

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166 "FCO" => "-0.27", "FHA" => "-0.06", "FHN" => "-0.03", "FNH" => "-0.18", "GCA" => "0", "GCO" => "0", "GHA" => "0", "GHN" => "0", "GNH" => "0", "HCA" => "-0.05", "HCO" => "-0.1", "HHA" => "-0.03", "HHN" => "-0.01", "HNH" => "-0.12", "ICA" => "-0.07", "ICO" => "-0.2", "IHA" => "-0.03", "IHN" => "-0.01", "INH" => "-0.18", "KCA" => "-0.01", "KCO" => "-0.08", "KHA" => "-0.02", "KHN" => "0", "KNH" => "-0.06", "LCA" => "-0.01", "LCO" => "-0.13", "LHA" => "-0.04", "LHN" => "0", "LNH" => "-0.06", "MCA" => "0", "MCO" => "-0.08", "MHA" => "-0.02", "MHN" => "0", "MNH" => "-0.06", "NCA" => "-0.06", "NCO" => "-0.09", "NHA" => "-0.01", "NHN" => "-0.01", "NNH" => "-0.18", "PCA" => "-0.22", "PCO" => "-0.47", "PHA" => "-0.01", "PHN" => "-0.04", "PNH" => "-0.18", "QCA" => "-0.02", "QCO" => "-0.05", "QHA" => "-0.01", "QHN" => "-0.01", "QNH" => "-0.06", "RCA" => "0", "RCO" => "-0.06", "RHA" => "-0.02", "RHN" => "0", "RNH" => "-0.06", "SCA" => "0", "SCO" => "-0.08", "SHA" => "-0.01",

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167 "SHN" => "0", "SNH" => "-0.06", "TCA" => "-0.01", "TCO" => "-0.08", "THA" => "-0.01", "THN" => "0.01", "TNH" => "-0.06", "VCA" => "-0.07", "VCO" => "-0.2", "VHA" => "-0.02", "VHN" => "-0.01", "VNH" => "-0.24", "WCA" => "-0.02", "WCO" => "-0.26", "WHA" => "-0.08", "WHN" => "-0.08", "WNH" => "0", "YCA" => "-0.07", "YCO" => "-0.28", "YHA" => "-0.05", "YHN" => "-0.04", "YNH" => "-0.24", "-CA" => "0", "-CO" => "0", "-HA" => "0", "-HN" => "0", "-NH" => "0", ); if(exists $aa_val{$aa}) { return $aa_val{$aa}; }else{ # something's not right! # instead of causing more problems, it's probably better to stop print STDERR "unexpected value: \"$aa\"!!\n"; exit; } } # sequence dependent corrections (i-1 amino acid) # # Schwarzinger S, Kroon GJ, Foss TR, Chung J, Wright PE, Dyson HJ. # J Am Chem Soc. 2001 Apr 4;123(13):2970-8. # sub seq_dep_previ { my($aa) = @_; my(%aa_val) = ( "ACA" => "-0.17", "ACO" => "-0.77", "AHA" => "-0.03", "AHN" => "-0.05", "ANH" => "-0.33", "CCA" => "-0.07", "CCO" => "-0.51", "CHA" => "0.02", "CHN" => "-0.02", "CNH" => "-0.26", "DCA" => "0",

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168 "DCO" => "-0.58", "DHA" => "-0.01", "DHN" => "-0.03", "DNH" => "-0.2", "ECA" => "-0.08", "ECO" => "-0.48", "EHA" => "-0.02", "EHN" => "-0.03", "ENH" => "-0.2", "FCA" => "-0.23", "FCO" => "-0.83", "FHA" => "-0.09", "FHN" => "-0.12", "FNH" => "-0.49", "GCA" => "0", "GCO" => "0", "GHA" => "0", "GHN" => "0", "GNH" => "0", "HCA" => "-0.09", "HCO" => "-0.65", "HHA" => "-0.06", "HHN" => "-0.04", "HNH" => "-0.55", "ICA" => "-0.2", "ICO" => "-0.58", "IHA" => "-0.02", "IHN" => "-0.06", "INH" => "-0.14", "KCA" => "-0.11", "KCO" => "-0.5", "KHA" => "-0.02", "KHN" => "-0.03", "KNH" => "-0.2", "LCA" => "-0.1", "LCO" => "-0.5", "LHA" => "-0.03", "LHN" => "-0.03", "LNH" => "-0.14", "MCA" => "0.1", "MCO" => "-0.41", "MHA" => "-0.01", "MHN" => "-0.02", "MNH" => "-0.2", "NCA" => "-0.03", "NCO" => "-0.66", "NHA" => "-0.01", "NHN" => "-0.03", "NNH" => "-0.26", "PCA" => "-2", "PCO" => "-2.84", "PHA" => "0.11", "PHN" => "-0.18", "PNH" => "-0.32", "QCA" => "-0.06", "QCO" => "-0.48", "QHA" => "-0.02",

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169 "QHN" => "-0.02", "QNH" => "-0.14", "RCA" => "-0.07", "RCO" => "-0.49", "RHA" => "-0.02", "RHN" => "-0.02", "RNH" => "-0.14", "SCA" => "-0.08", "SCO" => "-0.4", "SHA" => "0.02", "SHN" => "-0.03", "SNH" => "-0.03", "TCA" => "-0.04", "TCO" => "-0.19", "THA" => "0.05", "THN" => "0", "TNH" => "-0.03", "VCA" => "-0.21", "VCO" => "-0.57", "VHA" => "-0.01", "VHN" => "-0.05", "VNH" => "-0.14", "WCA" => "-0.17", "WCO" => "-0.85", "WHA" => "-0.1", "WHN" => "-0.13", "WNH" => "-0.26", "YCA" => "-0.22", "YCO" => "-0.85", "YHA" => "-0.1", "YHN" => "-0.11", "YNH" => "-0.43", "-CA" => "0", "-CO" => "0", "-HA" => "0", "-HN" => "0", "-NH" => "0", ); if(exists $aa_val{$aa}) { return $aa_val{$aa}; }else{ # something's not right! # instead of causing more problems, it's probably better to stop print STDERR "unexpected value: \"$aa\"!!\n"; exit; } } # sequence dependent corrections (i+1 amino acid) # # Schwarzinger S, Kroon GJ, Foss TR, Chung J, Wright PE, Dyson HJ. # J Am Chem Soc. 2001 Apr 4;123(13):2970-8. # sub seq_dep_nexti { my($aa) = @_; my(%aa_val) = ( "ACA" => "0.06",

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170 "ACO" => "-0.07", "AHA" => "-0.03", "AHN" => "0.07", "ANH" => "-0.57", "CCA" => "0.1", "CCO" => "-0.28", "CHA" => "0", "CHN" => "0.2", "CNH" => "3.07", "DCA" => "0.25", "DCO" => "-0.13", "DHA" => "-0.02", "DHN" => "0.14", "DNH" => "0.86", "ECA" => "0.05", "ECO" => "-0.2", "EHA" => "-0.02", "EHN" => "0.15", "ENH" => "1.51", "FCA" => "0.06", "FCO" => "-0.25", "FHA" => "-0.08", "FHN" => "0.1", "FNH" => "2.78", "GCA" => "0", "GCO" => "0", "GHA" => "0", "GHN" => "0", "GNH" => "0", "HCA" => "0.02", "HCO" => "-0.22", "HHA" => "0.01", "HHN" => "0.2", "HNH" => "1.68", "ICA" => "-0.01", "ICO" => "-0.18", "IHA" => "-0.02", "IHN" => "0.17", "INH" => "4.87", "KCA" => "-0.02", "KCO" => "-0.18", "KHA" => "-0.01", "KHN" => "0.14", "KNH" => "1.57", "LCA" => "0.03", "LCO" => "-0.13", "LHA" => "-0.05", "LHN" => "0.14", "LNH" => "1.05", "MCA" => "-0.06", "MCO" => "-0.18", "MHA" => "-0.01", "MHN" => "0.15", "MNH" => "1.57", "NCA" => "0.23", "NCO" => "-0.1", "NHA" => "-0.02",

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171 "NHN" => "0.13", "NNH" => "0.87", "PCA" => "0.02", "PCO" => "-0.09", "PHA" => "-0.03", "PHN" => "0.19", "PNH" => "0.87", "QCA" => "0.04", "QCO" => "-0.18", "QHA" => "-0.01", "QHN" => "0.15", "QNH" => "1.62", "RCA" => "-0.01", "RCO" => "-0.19", "RHA" => "-0.02", "RHN" => "0.15", "RNH" => "1.62", "SCA" => "0.13", "SCO" => "-0.15", "SHA" => "0", "SHN" => "0.16", "SNH" => "2.55", "TCA" => "0.12", "TCO" => "-0.13", "THA" => "0", "THN" => "0.14", "TNH" => "2.78", "VCA" => "-0.02", "VCO" => "-0.18", "VHA" => "-0.02", "VHN" => "0.17", "VNH" => "4.34", "WCA" => "0.03", "WCO" => "-0.3", "WHA" => "-0.15", "WHN" => "0.04", "WNH" => "3.19", "YCA" => "0.06", "YCO" => "-0.24", "YHA" => "-0.08", "YHN" => "0.09", "YNH" => "3.01", "-CA" => "0", "-CO" => "0", "-HA" => "0", "-HN" => "0", "-NH" => "0", ); if(exists $aa_val{$aa}) { return $aa_val{$aa}; }else{ # something's not right! # instead of causing more problems, it's probably better to stop print STDERR "unexpected value: \"$aa\"!!\n"; exit; } }

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172 # sequence dependent corrections (i+2 amino acid) # # Schwarzinger S, Kroon GJ, Foss TR, Chung J, Wright PE, Dyson HJ. # J Am Chem Soc. 2001 Apr 4;123(13):2970-8. # sub seq_dep_nextii { my($aa) = @_; my(%aa_val) = ( "ACA" => "0.01", "ACO" => "-0.02", "AHA" => "0", "AHN" => "-0.1", "ANH" => "-0.15", "CCA" => "-0.01", "CCO" => "-0.07", "CHA" => "0", "CHN" => "-0.07", "CNH" => "0", "DCA" => "-0.01", "DCO" => "-0.04", "DHA" => "-0.01", "DHN" => "-0.11", "DNH" => "-0.29", "ECA" => "0.01", "ECO" => "-0.03", "EHA" => "0", "EHN" => "-0.07", "ENH" => "-0.12", "FCA" => "0.01", "FCO" => "-0.1", "FHA" => "-0.04", "FHN" => "-0.37", "FNH" => "-0.46", "GCA" => "0", "GCO" => "0", "GHA" => "0", "GHN" => "0", "GNH" => "0", "HCA" => "0.01", "HCO" => "-0.07", "HHA" => "0.01", "HHN" => "0", "HNH" => "0.17", "ICA" => "0.02", "ICO" => "-0.02", "IHA" => "-0.01", "IHN" => "-0.09", "INH" => "0", "KCA" => "0.02", "KCO" => "-0.03", "KHA" => "0", "KHN" => "-0.06", "KNH" => "-0.06", "LCA" => "0.02", "LCO" => "-0.01", "LHA" => "-0.01",

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173 "LHN" => "-0.08", "LNH" => "-0.06", "MCA" => "0.01", "MCO" => "-0.02", "MHA" => "0", "MHN" => "-0.06", "MNH" => "-0.06", "NCA" => "0.01", "NCO" => "-0.03", "NHA" => "-0.01", "NHN" => "-0.07", "NNH" => "-0.17", "PCA" => "0.04", "PCO" => "-0.02", "PHA" => "-0.01", "PHN" => "-0.12", "PNH" => "-0.17", "QCA" => "0.01", "QCO" => "-0.03", "QHA" => "0", "QHN" => "-0.06", "QNH" => "-0.06", "RCA" => "0.02", "RCO" => "-0.03", "RHA" => "0", "RHN" => "-0.06", "RNH" => "-0.06", "SCA" => "0", "SCO" => "-0.06", "SHA" => "-0.01", "SHN" => "-0.08", "SNH" => "-0.17", "TCA" => "0", "TCO" => "-0.05", "THA" => "-0.01", "THN" => "-0.06", "TNH" => "-0.12", "VCA" => "0.01", "VCO" => "-0.03", "VHA" => "-0.01", "VHN" => "-0.08", "VNH" => "-0.06", "WCA" => "-0.08", "WCO" => "-0.17", "WHA" => "-0.16", "WHN" => "-0.62", "WNH" => "-0.64", "YCA" => "-0.01", "YCO" => "-0.13", "YHA" => "-0.04", "YHN" => "-0.42", "YNH" => "-0.52", "-CA" => "0", "-CO" => "0", "-HA" => "0", "-HN" => "0", "-NH" => "0",

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174 ); if(exists $aa_val{$aa}) { return $aa_val{$aa}; }else{ # something's not right! # instead of causing more problems, it's probably better to stop print STDERR "unexpected value: \"$aa\"!!\n"; exit; } } 1; Tcl/Tk NMRView panel placement script This panel placement script is an amazing time saver. Its purpose is to rearrange the windows of NMRView to accommodate my workflow. This may not seem like much. However, after moving panels around to a desire d configuration several times, the benefits of this script become immediately obvious. Tk commands can be run directly from NMRView through the Tk console. # # panel placement # # okg 6/14/05 # because I got sick and tired of moving them every time # # you can call winfo from inside nmrview's TkConsole # to get the geometry settings. However, winfo will # only give you the current settings, and will not let # you reset the window. Instead you have to call to # the windowmanager, wm. It's actually easier to handle # everything using wm, since it almost uses the same # calls that winfo does. To get the window positions # use: # wm geometry .widgetName # To figure out which widget you want, list em: # winfo children # When you are done, set them up with: # wm geometry .widgetName heightxwidth+xposition+yposition # # This Tcl script will load up the settings that I like # to use it, pop open the panels, then load up the script # from the TkConsole. Here's the settings that I like: # Assign > Peak panel wm geometry .peakAnal 468x180+1123+30 # Peak Attributes panel

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175 wm geometry .peakAttr 206x225+1384+241 # View and Cursor menu # (be careful of tearoff order) wm geometry .tearoff1 87x172+1187+241 wm geometry .tearoff2 80x110+1289+241 # Attributes panel wm geometry .specAttr 408x233+1180+497 # Plot panel wm geometry .plot 451x188+1138+761 # TkCon console wm geometry .tkcon 80x10+4+994 # tell the user it's done echo "Panels moved! Have a nice day! -okg" NMRView assignment helper script This script is another NMRView time save r. When making residue assignments of crosspeaks, several spectra need to be consulted simultaneously in a linked way. This script will try to match that peak in other spectra, incl uding spectra which are al ong different dimensions. #! /usr/bin/tclsh # # auto peak stuff # helps unify views of CbCa(CO)NH and HNCaCb with HNCO # # okg 11/22/05 # # NMRView TCL Commands can be found at: # http://www.hhmi.umbc.edu/toolkit/analysis/nmrview3/html/nv_win.html#nv_win # # to run this use: # source assignment_helper_0tfe.tcl # #This work ("assignment_helper") is licensed by O. Ganesh under the #Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License #http://creativecommons.org/licenses/by-nc-sa/2.5/legalcode #any derivative works based on this work shall reference the author # # make sure the hnco window is active nv_win act .ia3_notfe_hnco.0 # get the Proton value set Hline [cross1x] # get the Nitrogen value set Nline [cross1y] # set the HNCaCb as the active window nv_win act .ia3_notfe_cbcanh.0 # reset the spectra (if needed) nv_win full # move the black cursor to top leftmost edge

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176 # make absolutely sure that the axes for the HNCaCb are: # x:HN y:CACB z:15N cross1 [expr $Hline + .13] 17.22 # move the red cursor to bottom rightmost edge # make absolutely sure that the axes for the CbCa(CO)NH are: # x:HN y:CACB z:15N cross2 [expr $Hline .13] 67.9 # expand the region nv_win expand # set the right z plane z $Nline # set the CbCaCONH as the active window nv_win act .ia3_notfe_cbcaconh.0 # reset the spectra (if needed) nv_win full # move the black cursor to top leftmost edge # make absolutely sure that the axes for the HNCaCb are: # x:HN y:CACB z:15N cross1 [expr $Hline + .13] 17.22 # move the red cursor to bottom rightmost edge # make absolutely sure that the axes for the CbCa(CO)NH are: # x:HN y:CACB z:15N cross2 [expr $Hline .13] 67.9 # expand the region nv_win expand # set the right z plane z $Nline # now recenter all Proton lines # set the CbCaCONH as the active window nv_win act .ia3_notfe_cbcaconh.0 # set Proton axis cross1 $Hline 28.0 # set the HNCaCb as the active window nv_win act .ia3_notfe_cbcanh.0 # set Proton axis cross1 $Hline 28.0 # set the HNCO as the active window nv_win act .ia3_notfe_hnco.0 # set Proton and Nitrogen axis cross1 $Hline $Nline # finally, just set the CbCaCONH as the active window nv_win act .ia3_notfe_cbcaconh.0

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177 MATLAB Scripts Curve fitting Much of the analysis that I did was usi ng MATLAB. Fitting functions to data is a relatively straight-forward task, however learning how to do this well is slightly mysterious. Here I have listed a small script which details a working exponential fitt er. I owe a debt of thanks to Dr. Steve Hagen for sharing his weal th of MATLAB knowledge with me (especially with respect to data fitting). function [ Err ] = linearfit( mb_start, xdata, ydata, drawplot) %Linear fit to data %should be used as: %[m,b]=fminsearch('linear',[1,1],[],time_OD,RT_od) %good thing to remember, the variable after the function name is ALWAYS the one that's optimized %(in this case it's [1,1]=mb_start), if you want to pass other info along, then you have to use %[] followed by the data list. % To use this function as a plotter, use: %linear(mb_fit, xdata, ydata,1) m=mb_start(1); b=mb_start(2); if (exist('drawplot') ==1 & exist('mb_start') == 1) figure; yplot=mb_start(1)*xdata+mb_start(2); plot(xdata, ydata, 'o', xdata, yplot, '-'); title(sprintf('Exp fit Example: y_f_i_t=%.3f*x+%.3f', ... m,b)); xlabel('x') ylabel('y') else y_fit=m*xdata+b; Err=norm(ydata-y_fit); End Bootstrapping code Bootstrapping can be use to estimate errors in fitting. Data can be bootstrapped in MATLAB directly by calling the bootstrap command. This small example script will take a dataset and resample it, then submit the resampled dataset to a fitter. function [ answers ] = EF_Tu_bootstrapper( input1, input2, input3 )

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178 % Generalized bootstrapping function % % remember, bootstrap works by resampling the data set(s) % what this will do is minimize the function based on the % new resampled dataset. % % So, in reality what this is doing is finding the best values for dH and % dS based on a resampled dataset, and using the best values on the full % set as a starting point. So, what you get out is an optimized answer % over a resampled dataset. This may be a better answer then the original. % usage: % rhos1000=bootstrp(1000,'EF_Tu_bootstrapper', temp_s50_, cd_fixed_s50_, err_t_s50_); % remember, THIS function works by resampling temp_s50_, cd_fixed_s50_,err_t_s50_ % and submitting those resampled datasets to be refit using the constants % as starting points. % % If you called the bootstrp function directly on the original function % then what you are really doing is testing how robust or fragile a certain % answer is. % EXAMPLE (not this function): % rhos100=bootstrp(100,@(SV1,SV2) CD_T([-110 -.1],temps,SV1,SV2),SV1,SV2); % % to view your results, use % hist(rhos1000(:,1),60); % to see the best values for the first consts=[-89.42 75.32 70.97 2]; temp_s=input1; cd_fixed_s=input2; err_t_s=input3; [consts]=fminsearch('eftu_sig_fit_weighted',consts,[],temp_s,cd_fixed_s,err_t _s); %output answers=[consts(3)]; % the results will be in [midpoint_val]

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179 LIST OF REFERENCES 1. Anfinsen, C. (1993) Christian B. Anfinsen, Nobel Lecture, December 11, 1972, in Nobel Lectures, Chemistry 1971-1980 (Frngsmyr, T., and Forsn, S., Eds.), World Scientific Publishing Co., Singapore. 2. Anfinsen, C. B. (1972) The formation and stabilization of protein structure, The Biochemical journal 128 737-749. 3. Anfinsen, C. B. (1973) Principles th at govern the folding of protein chains, Science (New York, N.Y 181, 223-230. 4. Alberts, B. (2002) Molecular Biology of the Cell 4 ed., Garland Science, New York. 5. Dyson, H. J., and Wright, P. E. (2006) According to current te xtbooks, a well-defined three-dimensional structure is a prerequisite fo r the function of a protei n. Is this correct?, IUBMB life 58, 107-109. 6. Wright, P. E., and Dyson, H. J. (1999) Intr insically unstructured proteins: re-assessing the protein structure-function paradigm, Journal of molecular biology 293, 321-331. 7. Dyson, H. J., and Wright, P. E. (2005) Intrinsically unstructured proteins and their functions, Nature reviews 6, 197-208. 8. Ganesh, O. K., Green, T. B., Edison, A. S ., and Hagen, S. J. (2006) Characterizing the residue level folding of the in trinsically unstructured IA3, Biochemistry 45 13585-13596. 9. Green, T. B., Ganesh, O., Perry, K., Smith, L., Phylip, L. H., Logan, T. M., Hagen, S. J., Dunn, B. M., and Edison, A. S. (2004) IA3, an aspartic proteinase inhibitor from Saccharomyces cerevisiae, is intrin sically unstructured in solution, Biochemistry 43, 4071-4081. 10. Li, M., Phylip, L. H., Lees, W. E., Winther, J. R., Dunn, B. M., Wlodawer, A., Kay, J., and Gustchina, A. (2000) The aspartic prot einase from Saccharomyces cerevisiae folds its own inhibitor into a helix, Nature structural biology 7, 113-117. 11. Phylip, L. H., Lees, W. E., Brownsey, B. G., Bur, D., Dunn, B. M., Winther, J. R., Gustchina, A., Li, M., Copeland, T., Wlodawe r, A., and Kay, J. (2001) The potency and specificity of the interaction between the IA3 inhibitor and its target aspartic proteinase from Saccharomyces cerevisiae, The Journal of biological chemistry 276, 2023-2030. 12. Ramachandran, G. N., Ramakrishnan, C., and Sasisekharan, V. (1963) Stereochemistry of polypeptide chain configurations, Journal of molecular biology 7, 95-99. 13. Ramachandran, G. N., Venkatachalam, C. M., and Krimm, S. (1966) Stereochemical criteria for polypeptide and protein chain conformations. 3. Helical and hydrogen-bonded polypeptide chains, Biophysical journal 6, 849-872.

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200 BIOGRAPHICAL SKETCH Om joy Kumar Ganesh was born in 1976 in A nn Arbor, Michigan to T.S. and Lalitha Ganesh. He was raised in the cereal city of th e U.S., Battle Creek, Michigan until the age of 11. In 1987, his family moved to Gaines ville, Florida to escap e the cold of the north. In Gainesville, he attended Howard W. Bishop Middle School, followed by F.W. Buchholz High School. It was in high school that he realized his interest in science and math, sometimes annoying teachers with "how" and "why" questions. In the summer of 1993 Omjoy (who now went by the nickname OJ) was accepted into the Student Science Training Program, a residential research program at the University of Florida (UF) run by the Center for Precoll egiate Education and Training. During this time he worked with Dr. William G. Vernetson at the UF Training Reactor on a project using nuclear activation analysis to investigate aluminum levels and their relationship with Alzheimer's disease. After graduating from Buchholz High School in 1994, OJ was accepted to and attended UF. The institution was perfect for him, as th e university was large enough to afford him the opportunity to experience many different subjects, and meet a wide variety of people to enrich (and sometimes hurt) his life in many ways. After a year focused on math and English, OJ decided to major in chemistry. A year after that decision, he felt that this did not suit him, and he moved to psychology, which after one semester su ited him even less. A chance decision to take a physics 3 course (which was not part of the general education requirements) proved pivotal. The course, taught by Dr. Gary Ihas, introduced OJ to 'strange' concepts such as subatomic particles, relativity, and quantum physics. This sparked OJ's interest and led to his earning a bachelor's degree in physics. During that time he had the privilege to be instructed by Dr. David Tanner, Dr. Yasumasa Takano, and Dr. Dimitri Maslov. Ad ditionally, he worked on two projects, briefly in computational astrophysics wi th Dr. Robert Bucheler, and more extensively

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201 on atomic force microscopy using carbon nanotubes with Dr. Arthur He bard and Dr. Andrew Rinzler. Graduating from UF in 1999, OJ went to teach mathematics and physics at Santa Fe Community College. But after 2 years he decided to continue his education, by entering graduate school, through the Inte rdisciplinary Program in Biomedi cal Sciences at UF. Rotating through the labs of Dr. Benner, Dr. Purich, and Dr. Walter, he eventually se ttled into the labs of Dr. Hagen and Dr. Edison to work on th e intrinsically unstructured protein IA3. This arrangement worked well, since it provided him with the stability of a physics culture while allowing him to explore new territory in biochemistry. During his graduate work at UF, OJ has co authored three papers, two in the journal Biochemistry ( 8, 9) and one in Nature ( 263). In 2007, he was a coreci pient of the Boyce award. In that same year he also chaired a lecture panel on the uses of NMR and MRI at UF. More importantly, during his years in graduate school OJ married his lovely wife Katia, made many long lasting friendships, and grew academically into an excellen t scientist. While very few things in this day and age have long lasting e ffects, the knowledge that OJ gained from this experience will last him a lifetime, and will be passed on to others.