Group Title: Annals of General Psychiatry 2006, 5 (Suppl 1): S25)
Title: Psychoses and their treatments: a dual threat for the patients' total health?
CITATION PDF VIEWER THUMBNAILS PAGE IMAGE ZOOMABLE
Full Citation
STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00100261/00001
 Material Information
Title: Psychoses and their treatments: a dual threat for the patients' total health?
Series Title: Annals of General Psychiatry 2006, 5 (Suppl 1): S25)
Physical Description: Archival
Creator: Tandon R
Publication Date: 2/28/2006
 Record Information
Bibliographic ID: UF00100261
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: Open Access: http://www.biomedcentral.com/info/about/openaccess/

Downloads

This item has the following downloads:

01471-2105-7-89 ( PDF )


Full Text



BMC Bioinformatics Bie Central


Research article


Integrating protein structures and precomputed genealogies in the
Magnum database: Examples with cellular retinoid binding proteins
Michael E Bradley*1,3 and Steven A Benner2


Address: 'Department of Chemistry, University of Florida, P.O. Box 117200, Gainesville, FL, 32611, USA, 2Foundation for Applied Molecular
Evolution, 1115 NW 14th Avenue, Gainesville, FL, 32601, USA and 3Division of Biological Sciences, Department of Ecology and Evolution,
University of Chicago, 1101 East 57th Street, Chicago, IL, 60615, USA
Email: Michael E Bradley* mbradley@ffame.org; Steven A Benner sbenner@ffame.org
* Corresponding author



Published: 23 February 2006 Received: 19 September 2005
BMC Bioinformatics 2006, 7:89 doi:10.1 186/1471-2105-7-89 Accepted: 23 February 2006
This article is available from: http://www.biomedcentral.com/1471-2105/7/89
2006 Bradley and Benner; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.



Abstract
Background: When accurate models for the divergent evolution of protein sequences are
integrated with complementary biological information, such as folded protein structures, analyses
of the combined data often lead to new hypotheses about molecular physiology. This represents
an excellent example of how bioinformatics can be used to guide experimental research. However,
progress in this direction has been slowed by the lack of a publicly available resource suitable for
general use.
Results: The precomputed Magnum database offers a solution to this problem for ca. 1,800 full-
length protein families with at least one crystal structure. The Magnum deliverables include I)
multiple sequence alignments, 2) mapping of alignment sites to crystal structure sites, 3)
phylogenetic trees, 4) inferred ancestral sequences at internal tree nodes, and 5) amino acid
replacements along tree branches. Comprehensive evaluations revealed that the automated
procedures used to construct Magnum produced accurate models of how proteins divergently
evolve, or genealogies, and correctly integrated these with the structural data. To demonstrate
Magnum's capabilities, we asked for amino acid replacements requiring three nucleotide
substitutions, located at internal protein structure sites, and occurring on short phylogenetic tree
branches. In the cellular retinoid binding protein family a site that potentially modulates ligand
binding affinity was discovered. Recruitment of cellular retinol binding protein to function as a lens
crystallin in the diurnal gecko afforded another opportunity to showcase the predictive value of a
browsable database containing branch replacement patterns integrated with protein structures.
Conclusion: We integrated two areas of protein science, evolution and structure, on a large scale
and created a precomputed database, known as Magnum, which is the first freely available resource
of its kind. Magnum provides evolutionary and structural bioinformatics resources that are useful
for identifying experimentally testable hypotheses about the molecular basis of protein behaviors
and functions, as illustrated with the examples from the cellular retinoid binding proteins.







Page 1 of 18
(page number not for citation purposes)


70p7en7A7c







http://www.biomedcentral.com/1471-2105/7/89


Background
The amino acid sequences from a set of homologous pro-
teins contain an imperfect record of the history of
sequence divergence within that protein family. Much of
this history can be modeled by a process that formally
reconstructs the sequences of ancestral proteins through-
out an evolutionary tree, given a multiple sequence align-
ment relating individual sites in the descendent proteins
[1]. These reconstructions are generally presented in prob-
abilistic form, where the likelihood that each of the stand-
ard amino acids occupied a particular site at a point in the
tree is represented by a vector whose coefficients sum to
unity, and where each coefficient represents the probabil-
ity, conditional on the reconstruction model, that each of
the standard amino acids occupied that site at that point.

Reconstructed ancestral sequences can be viewed as an
expedient representation of extant sequence data, because
they include all of the sequence information in a way that
represents a best guess model for the historical reality. It is
not surprising, therefore, that explicitly considering recon-
structed ancestral sequences is a powerful tool for inter-
preting sequence data. For example, explicit consideration
of ancestral states provided an understanding of the gen-
eral features of correlated changes in proteins [2], an
understanding that eluded a perplexed literature that
attempted to analyze correlated change by leaf-leaf com-
parisons [3-6]. Likewise, many compelling tools for pre-
dicting the folded structure of proteins are based on
analyses that consider reconstructed ancestral sequences
[7,8]. Ancestral sequence reconstructions have provided
insight as well into the relation between protein sequence,
behavior, and adaptive function in proteins as diverse as
ribonucleotide reductase [9], leptin [10], aromatase [11],
sulfotransferase [12], steroid receptor [13], alcohol dehy-
drogenase [14], elongation factor [15], ribonuclease and
itshomologs [16,17], rhodopsin [18], lysozyme [19], and
biofluorescent proteins [20]. In the last seven cases, ances-
tral proteins from extinct organisms were actually resur-
rected in the laboratory (reviewed in [21]).

As valuable as such studies are, however, only a very few
scientists pursue them. Two reasons explain, at least in
part, why studies involving reconstructed ancestral pro-
teins are challenging. First, evolutionary analyses are
highly dependent on the availability of a large amount of
data collected by others. An evolutionary analysis of a pro-
tein family can generate interesting biological interpreta-
tions only if it contains a sizeable number of members,
and only if those sequences are contributed from interest-
ing organisms. Further, the analysis depends on the extent
to which the sequences have diverged, and how the tree
interconnecting the sequences is articulated.


Non-sequence information to support an evolutionary
analysis of sequence data is also usually available only
opportunistically. Thus, the availability of a crystal struc-
ture for a member of a protein family is generally not
determined by a desire to support an evolutionary analy-
sis. Likewise, there is no guarantee that a fossil record will
exist for organisms holding ancestral proteins of interest,
or kinetic data will be collected on the interesting proteins
as biomolecules, or even that an interesting physiological
recruitment has been studied by cell or organismic biolo-
gists.

This contingency means that one rarely knows in advance
when one sets out to perform an evolutionary analysis
what will be discovered. Generally the most interesting
insights are true discoveries, coming opportunistically by
those who can resourcefully take advantage of all infor-
mation, especially that from researchers who delivered it
to the literature for reasons quite different from the desire
to support an evolutionary analysis.

This places a high value on database architectures that
support browsing. The most useful database for an evolu-
tionary bioinformaticist must allow the user to ask "What
if? and "Why not? questions about the evolutionary his-
tories of multiple families, where the answers can be
extracted from the database in minutes or hours, not days
or weeks.

This generates the second reason why detailed evolution-
ary bioinformatic analyses are rarely done. The public
databases are inadequate for those who wish to browse,
especially if they hope to collect information about ances-
tral protein sequences. The decision to do an evolutionary
analysis with the NCBI web page is a decision to spend
weeks doing expert interpretation of BLAST outputs, mul-
tiple sequence alignments, and tree constructs. This
means that evolutionary analyses are today done one fam-
ily at a time, by experts in evolutionary analysis, and are
not done by bench experimental scientists who have the
most access to the non-sequence information that
enriches a purely bioinformatics study.

A naturally organized commercial database incorporating
information from ancestral sequences, known as the Mas-
terCatalog, was introduced in 2000 to support browsing
[22]. It has been remarkably successful doing so in the
hands of those who have had access to it [10-12,23,24].
The product falls between market niches, however; too
expensive to be available to academic scientists, yet gener-
ating too little revenue to attract major capital support.
Therefore, it has not permitted broad based discovery
using genomic sequence data. Some time ago, we set out
to develop a publicly available database that would sup-
port browsing of evolutionary and structural data jointly.


Page 2 of 18
(page number not for citation purposes)


BMC Bioinformatics 2006, 7:89








http://www.biomedcentral.com/1471-2105/7/89


We have been able here to assemble such a database, Mag-
num, that covers protein families where at least one mem-
ber has a crystal structure.

We report here a characterization of the Magnum data-
base, which covers the multiple sequence alignments,
phylogenetic trees, inferred ancestral sequences, amino
acid residue replacements on branches, and crystal struc-
tures. We demonstrate the utility of Magnum as a research
tool for addressing general trends in protein evolution as
well as more specific questions of relevance to individual
protein families. In this case, we performed queries to
select individual amino acid replacements occurring
along short branches, located at internally folded protein
sites, and requiring a nucleotide change at each of the
three codon positions for interconversion. The results lead
us to, among others, the family of proteins that bind reti-
nol and other hydrophobic ligands, where we discovered
along one branch a replacement that may explain physio-
logical differences in ligand binding specificities, and
along another branch a set of replacements related to the
recruitment of this protein as an eye lens crystallin in diur-
nal geckos.

Results
Description of magnum
In its current form, Magnum comprises 1,820 "Super-
families" [25] from the Protein Information Resource
(PIR) containing from 4 to 191 family members.
Together, these contain a total of 85,386 protein
sequences (Figure 1A). The average family has 43
sequences aligned over 333 sites (Figure 1B). If all of the
alignments were concatenated, the total alignment length
would be 606,638 sites.

Of the 606,638 alignment sites, 68% were completely
gap-free. Where gaps do occur, they comprise less than
15% of all alignments characters for approximately two-
thirds of the families (Figure 1C) and are concentrated
within ten or fewer distinct regions for the typical family
(Figure ID).

The average family in Magnum has a width of 4.2 amino
acid replacements per site (Figure 2A). The average branch
represents a distance of 0.2 replacements per site (Figure
2B). An unrooted phylogeny with n leaf sequences has n-
2 internal nodes and 2n-3 branches. The trees collected in
Magnum contain 81,746 internal nodes and 165,312
branches. Over 80% of internal nodes are less than 0.4
amino acid substitutions per site distant from a leaf
sequence (Figure 2C). A substantial fraction of these con-
nected an internal node to a leaf. The remaining 20% of
the nodes, nearly all not directly connected to a leaf, were
more than 0.4 amino acid substitutions per site distant


700
600
500
400
300-
200-
100
a


581


4-10 11-20 21-50 51-100 101-191
Members


600
500 451
( 400389
I 300 1
E 2
S200136 133
100
0


52


27- 101- 201- 301- 401- 501- 601- 701- 801- 901-
100 200 300 400 500 600 700 800 900 1874
Alignment Sites


500
450
0 391 384 370

300 273


100 74
50 9
0-5 6-10 11-15 16-20 21-25 26-30 31-35 3640 4145
Total Alignment Gap Characters (%)


700-
600
51
500
400
300
200
io1


_24 12 8 1 1
6-10 11-15 16-20 21-25 26-30 31-35 36-40 4145 46-50
Alignment Gap Regions


Figure I
Alignments. Histograms showing the distribution of protein
sequence families (y-axis, number of families) in Magnum ver-
sus (A) Number of homologous family members, (B)
Number of aligned sites in the multiple sequence alignment
of the family, (C) Percentage of characters that are gaps in
the multiple sequence alignment, and (D) Number of regions
containing gaps.


Page 3 of 18
(page number not for citation purposes)


BMC Bioinformatics 2006, 7:89


-








http://www.biomedcentral.com/1471-2105/7/89


45C
I; 40C
*= 35C
S30C
CX 25C
LL 20C
15C
10C
50
SoQ


31 36 20 4121
31 36 20 15 12 7 6 21
.05-1.1- 2.1- 3.1- 4.1- 5.1- 6.1- 7.1- 8.1- 9.1- 10.1-11.1-12.1-13.1-14.1-15.
1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11 12.013.014.015.038.
Maximum Leaf-Leaf Distance


1200

1000
800
,(-
C 600

ID 400

200

0.0000 0.0005 0.0018 0.0060 0.0200 0.0665 0.2219 0.7400 2.5018 8.343;
Branch Length


45000
) 40000 36348

0 30000
Z 25000
CE 20000
E 15000 12267
10000 7549
50006 2o3801520 988 626 1011
0_ L 1-1 --- M_
0- 0.11- 0.21- 0.31- 0.41- 0.51- 0.61- 0.71- 0.81- 0.91- 1.00-
0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 4.99
Minimum Node-Leaf Distance


16000C
:9 14000C
( 12000C
C 10000C
0)
E 8000
C 60000
:= 4000
49 20000


157382


S u.UO- U..I I.U i.l L.UI -
0.50 1.00 1.50 2.00 2.50
Substitution Rate


10688 8414
2.51- 3.00-
3.00 70.8


Figure 2
Phylogenies. Histograms showing the distribution of families,
branches, nodes, and sites as a function of the evolutionary
feature indicated (x-axis). (A) Width of the evolutionary tree
interconnecting the family members, (B) Length of the tree
branches, (C) Shortest distance from internal nodes to a leaf
node, (D) Substitution rate factor. (A-C) Units are replace-
ments per site.


l I


Page 4 of 18
(page number not for citation purposes)


BMC Bioinformatics 2006, 7:89


from a leaf. The distribution of substitution rates at indi-
vidual sites is shown in Figure 2D.

Accuracy and error in reconstructed sequences
To obtain a general measure of the reconstruction accu-
racy at the typical site in the Magnum database, condi-
tional posterior probabilities were obtained from the
marginal reconstruction method, which independently
treats each site at each internal node in order to calculate
the probability of deriving the leaf sequences starting
from each of the 20 amino acids at that position in the
tree (see Methods). These probabilities are conditional on
the parameters of the metatheory used during the recon-
struction. As the accuracy of a reconstruction declines, so
to does the posterior probability value generated by the
scoring tool.

In Magnum, 31,283,104 ancestral sites were recon-
structed. At 6,022,509 sites, the reconstruction suggested
gap characters, which are not assigned a probability value.
Although we do not consider replacements involving gaps
in this study, we chose to model the gapping history
because even a simple representation of the indel process
was preferable to ignoring it. Where the reconstructions
did not contain gaps, the overwhelming majority of con-
ditional posterior probabilities for the primary amino
acids (the preferred amino acid reconstructed at a site in a
nodal sequence) were above 90% (Table 1).

The primary amino acid residues reconstructed using mar-
ginal and joint methods were then compared. Discrepan-
cies between these reconstructions would indicate
disagreement, which would imply uncertainty in recon-
structions arising from an irresolvable concern over which
of the two methods is more likely to capture the historical
reality. Fortunately, the primary residue inferred using the
marginal reconstruction method differed from that
inferred by the joint reconstruction method at only 2.8%
of the ungapped ancestral sites (747,557 sites, see Table
1). These sites predominantly occurred among sites where
the marginal posterior probabilities were low. This indi-
cates that disagreement between the "marginal recon-
struction" and "joint reconstruction" metatheories is
found primarily at sites where the inferences of the ances-
tral amino acid are not confident in any case. At the
majority of these sites, the residue inferred by the joint
reconstruction method was the second or the third most
likely residue inferred by the marginal reconstruction
method.

From these results, we conclude that the majority of
ancestral reconstructions in Magnum are fairly accurate
and robust to the reconstruction philosophy (joint versus
marginal). More precisely, inferences drawn directly from
the Magnum database without further refinement of its








BMC Bioinformatics 2006, 7:89


http://www.biomedcentral.com/1471-2105/7/89


ancestral characters are not likely to be dominated by
database error. Further, when performing detailed analy-
ses with the data, numerous indicators of accuracy are
available from Magnum to the user for deciding where the
reconstructions are likely to be less certain.

Structural characteristics of the families
As a condition for inclusion within Magnum, each PIR
Superfamily had to be associated with at least one protein
chain from a structure in the Protein Data Bank (PDB).
The median Magnum family has members associated with
two PDB chains (Figure 3A). The families with the longest
alignments have the fewest PDB chains and vice versa
(Figure 3B). We explain this by the observation that short
sequences are more prevalent in the PDB.

Multiple sequence alignment was used to relate PDB
chain sites with family member sites. At 80,373 of the
final sites, gaps were placed within the PDB chain
sequences (Figure 3C). At an additional 22,571 sites, resi-
dues in the PDB-listed sequence were aligned, but the
PDB database did not report resolved crystallographic
coordinates for their side chains, and therefore secondary
structural information was unavailable (Figure 3C). After
correcting for gaps and unresolved residues, 503,694 sites
were aligned with at least one resolved residue from the
PDB.

In the current version of Magnum, 1,057 families are asso-
ciated with multiple PDB chains (Figure 3A). For the
majority of these families, a strong correlation exists
between the average length of the PDB chains and the
alignment length after correcting for sites without struc-
tural information (Figure 3D). This demonstrates that in
many families with multiple structures the same align-
ment sites are covered by each PDB chain.

The structural information was then used to assign surface
accessibility and secondary structural characteristics for
the 503,694 sites where it was available (Table 2). If more
than one PDB chain was applicable to a site, the side chain
accessibility values were averaged. At the 426 and 6,416
sites where four and three, respectively, secondary struc-
ture types were assigned we report the most common
type. In the event that none of the secondary structures are
more common than the others, such as in the 56,877 sites
with exactly two assignments, the secondary structure is
reported according to the following hierarchy: turn, helix,
coil, and then strand.

Residues at approximately half of the sites were less than
30% accessible. This is a threshold at which we distin-
guish 'buried' and 'exposed' sites for the studies below,
although the user is able to select alternative cutoffs. The
four secondary structure types were each well represented


A 900
800
700
S 600
500
M 400
300
200
100


388

205
117 107
53m 23 22
2 3 4 5 6 7 8 9 10 >10
PDB Chains


1800-
1600
W 1400-

1000
lO
E 800
600

200
0


glutamate
synthase

or

+ ;


T4 phage
lysozyme c lysozyme


0 50 100 150 200 250
PDB Chains


300 350


Ill. .....
0% 1 3 5 7 9 11 13 15 17 19 21
2% 4% 6% 8% 10% 1% 16 18% 20% 72%
Sites Without Structural Residues


D 1600
& 1400
1200
1000


0 200 400 600 800 1000 1200 1400 1600 1800
Ungapped Alignment Sites


Figure 3
Structures. (A) Histogram showing the distribution of PDB
chains associated with individual families in Magnum. (B) Scat-
ter plot of alignment length versus the number of PDB chains
for each family. Glutamate synthase has the longest alignment
length; lysozyme c and T4 phage lysozyme have the largest
number of non-redundant PDB chains. (C) Frequency histo-
grams for the proportions of alignment sites without struc-
tural information due to indels (gray), and unresolved areas
of the structure (black). (D) Average PDB chain length plot-
ted against the number of sites aligned to at least one PDB
chain residue for families with at least two PDB chains (the
coefficient of determination calculated by linear regression is
also shown).





Page 5 of 18
(page number not for citation purposes)








http://www.biomedcentral.com/1471-2105/7/89


Table I: Marginal versus joint reconstruction


posterior probability


joint = marginal I


1,238
75,322
261,119
462,681
645,994
823,937
759,976
789,136
1,007,528
20,433,891
25,260,822


joint marginal I


marginal 2

181
16,354
66,689
I 16,133
146,765
146,931
57,359
18,078
4,057
431
572,978


marginal 3

122
10,065
31,945
37,273
19,181
7,127
2,576
799
215
24
109,327


marginal 4

112
6,285
13,920
8,923
3,320
1,207
414
124
30
3
34,338


marginal 5 20

401
I 1,801
I 1,556
4,508
1,587
522
158
66
5
3
30,607


All reconstructed sites are cross-tabulated according to the posterior probability of the best marginal reconstruction amino acid and the marginal
reconstruction rank, from highest to lowest posterior probability, of the sole joint reconstruction amino acid.


at all levels of surface accessibility. Residues in strands
were more likely to be buried, while residues in turns were
more likely to be found on the surface [26]. Residues in
helices and coils did not show a preference for buried or
exposed sites.

Amino acid residue replacements along branches
As a further test of the uncertainty in the ancestral
sequence reconstructions, we asked whether patterns in
the replacement of amino acids as represented by replace-
ments inferred from ancestral sequences were similar to/
different from those patterns obtained by many workers
(e.g., Dayhoff [27]) by comparing extant sequences. For
short, medium and long branches, 10,000 leaf-leaf and
node-node pairs were randomly selected. Replacements
were counted within these six collections of aligned
sequences, and Dayhoff matrices were constructed from
the replacement counts [see Additional file 1].

Table 2: Surface accessibility and secondary structure


For the node-node comparisons, replacements were
counted using three tools that differed in the weighting of
replacements. The 'best count' tool assigned equal weight
to all replacements, which were detected by searching for
sites where the most probable parent residue differed
from the most probable residue at the same site in the
child nodal sequence. Using the same strategy to search
for replacements, the 'best fractional' tool weights each
replacement by the product of the marginal posterior
probability values of the parent and child residues. The
'all fractional' tool exhaustively considers, for every
aligned site, all possible combinations of parent and child
residues weighted by their combined probability. In addi-
tion to the 190 pairs of different residues (i.e. replacement
events), each tool also keeps a running total the 20 non-
replacement pairs.


surface accessibility*


secondary structure**


60,833
16,411
13,424
12,014
10,923
10,578
9,856
9,130
8,232
30,045
181,446


strand

48,079
9,849
7,374
6,158
4,992
4,372
3,745
3,141
2,583
6,573
96,866


coil

23,319
1,260
9,147
7,857
7,131
6,484
5,825
5,149
4,250
17,945
98,367


* The average surface accessibility is reported when multiple PDB chains are aligned at a site.
** The most commonly called secondary structure type is reported when multiple PDB chains are aligned at a site.


Page 6 of 18
(page number not for citation purposes)


turn

19,417
10,601
9,695
9,869
9,748
9,575
9,465
8,207
7,470
32,968
127,015


total

151,648
48,121
39,640
35,898
32,794
31,009
28,891
25,627
22,535
87,531
503,694


BMC Bioinformatics 2006, 7:89








http://www.biomedcentral.com/1471-2105/7/89


A clear linear relation was observed between correspond-
ing node-node and leaf-leaf matrix elements at all three
distances regardless of the replacement counting tool (Fig-
ure 4A-C and data not shown). From this, we conclude
that replacement events reconstructed from node-node
comparisons are similar in type to the amino acid replace-
ments based on leaf-leaf comparisons.

Intriguingly, the correlation coefficient was larger in the
long branches examined than in the medium and short
branches. This phenomenon is not understood. Interest-
ingly, the replacement pairs that deviate from the regres-
sion line in Fig. 4 appear most frequently to involve
alanine or valine. Further, alanine and valine appear to be
most conserved, regardless of the distance between the
sequences. This is a discovery worth investigating in the
future.

The branch lengths used in the Magnum trees are
obtained by fitting the matrix of pairwise leaf-leaf dis-
tances (in units of replacements per site) upon the tree
that gives the smallest total distance using the strategy in
the program Weighbor [28]. Once the ancestral sequences
are in hand, an alternative measure of the distance of a
branch connecting two nodes (or a node-leaf branch) was
obtained by examining two (or one) ancestral sequences,
and counting the number of changes, weighted by the
fractional probabilities of those residues where appropri-
ate. These are "branch-lengths-from-reconstruction".

The number of changes on any branch at any individual
site cannot exceed unity, as multiple substitutions at a site
are not captured by simply comparing two sequences.
Thus, the branch-lengths-from-reconstruction, a metric
that averages these over all sites, cannot exceed unity. The
branch-lengths-from-reconstruction with the all frac-
tional tool closely resembled the actual branch lengths,
especially at short distances (Table 3). The other tools
rarely overestimated the actual branch length (Table 3).
Where branch-lengths-from-reconstruction are less than
actual lengths, one explanation is that intermediate
replacements occurred at some sites. By correcting for
multiple substitutions, which occur more frequently on
longer branches and for sites with higher substitution
rates, it would be possible to improve the correspondence
between branch-lengths-from-reconstruction and the
actual tree branch lengths.

A browsing example: F-to-E, F-to-Q, and F-to-K
replacements
We illustrate an example of how the Magnum database
can be used to ask a question about amino acid replace-
ment in general, where possible answers to the question
bear on evolutionary and structural biology.


0


o
0
5.


-
z 0.









B 20-

15-

10-


-10-




c 20-

15-

10-


-10 -5 0 5 10 15 20
Leaf-Leaf




R2=08



0

a






-10 -s 0 10 15 20
Leaf-Leaf




R2=097


ooe









-10 -5 0 10 15 20
Leaf-Leaf


Figure 4
Dayhoff matrix comparisons. Elements of leaf-leaf (x-axis)
and node-node (y-axis) Dayhoff matrices are plotted
together from (A) short, (B) medium and (C) long branches.
The coefficient of determination calculated by linear regres-
sion is shown. Labeled points involve the amino acids alanine
or valine. The all fractional counting method was used in
node-node comparisons shown. Similar results were
obtained with the other counting methods (data not shown).





Page 7 of 18
(page number not for citation purposes)


R2=078

0
0
0


.P 0
01.


BMC Bioinformatics 2006, 7:89









BMC Bioinformatics 2006, 7:89


http://www.biomedcentral.com/1471-2105/7/89


Table 3: Branch-lengths-from-reconstruction versus Tree-branch-length


Branch-lengths-
from-
reconstruction*


Tree-branch-length


0 .0001 -.006 .0061 -.020 .0201 -.040 .0401 -.070 .0701 -.130 .1301 -.220 .2201 -.400


1,292
16,365
1,576
410
274
221
89
28
I

7,791
11,570
883
10
0
0
0
0


17,555
0
2,683
16

0
0
0
0
I


6
2,268
11,110
2,406
1,591
1,555
474
88
2

1,260
4,748
12,873
606
12
0
0
0
I

2,581
0
15,870
988
60
0
0
0
I


4
129
1,829
7,070
2,795
3,717
1,673
237
3

295
1,282
9,119
6,487
269
5
0
0
0

405
0
7,557
8,382
1,074
38
0
0
0


0
29
93
146
3,475
I 1,268
5,753
1,866
48

103
275
2,641
5,554
10,172
3,887
46
0
0

128
0
340
1,731
9,621
10,587
270
0
0


0
I
47
80
125
5,841
10,051
2,848
63

51
90
883
2,046
4,592
10,244
1,142
7
71

71
0
122
277
1,240
11,484
5,813
48
I


0


31
66
305
10,022
8,811
178

23
28
232
779
1,770
8,278
7,954
350
I

30
0
59
77
217
2,207
13,177
3,646
2


.4001 1.00



0
0
2

5
33
583
14,136
2,492

8
15
51
175
559
2,757
8,907
4,737
43

10
0
21
17
46
264
2,598
13,213
1,083


*Replacements per site from all fractional (AF), best fractional (BF), and best count (BC) tools.


We started by noting, trivially, that 190 distinct amino
acid replacement patterns exist given 20 standard amino
acids and ignoring directionality in time (X-to-Y equals Y-
to-X). In 14 of these, three nucleotide substitutions (one
at each codon position) are required to effect the replace-
ment, regardless of the codons used in the parent or child
genes. Three of these pairs interconvert the hydrophobic
amino acid phenylalanine (F) with one of the polar
amino acids, lysine (K), glutamine (Q), or glutamate (E).
For example, the F-to-K replacement requires the conver-
sion of a TTY codon (where Y is a pyrimidine) to an AAR
(where R is a purine), requiring a total of three transver-
sions. The six shortest pathways for the F-to-E, F-to-Q, and
F-to-K replacements are shown in Figure 5.


The F-to-E/Q/K replacements on branches of any length
were found to be relatively infrequent, observed at only
0.026% of all recorded replacements (Table 4). The collec-
tion of F-to-E/Q/K replacements appears to be distributed
with no obvious preference in large and small families,
long or short alignments, or branches connecting sites
with higher or lower posterior probability. The F-to-E/Q/
K replacements are, however, found more frequently on


longer branches and in sites holding side chains on the
protein surface (Table 4). Each of these facts is readily
retrieved given the structure of the Magnum database.


In the hope of identifying F-to-E/Q/K replacements
involved in adaptive change, buried sites on short
branches (less than 0.12 substitutions per site) were
selected. Again, the Magnum database permitted this
information to be extracted with little effort. A total of 291
replacements satisfied these criteria [see Additional file 2].


First, the alignments and phylogenies were inspected to
determine which of these events might reflect database
error; misalignments were of special concern. Out of 41
events we found that 17 of the alignments were most
likely incorrect. This is not surprising due to the unusual
nature of the replacement events under inspection in this
sample. We expect, therefore, that this is a worst case
example.


Cellular retinoid binding proteins
While each of the remaining F-to-E/Q/K replacements
proved to be interesting, we choose to discuss here just



Page 8 of 18
(page number not for citation purposes)


7,196
19,281
14,900
12,700
15,526
27,777
32,214
29,406
6,312

16,806
18,607
31,207
24,650
21,502
25,539
19,094
7,273
634

28,108
0
27,982
19,367
20,575
25,291
21,905
18,033
4,051








http://www.biomedcentral.com/1471-2105/7/89


Lys(K) '


GAY
Asp (D)

GTR
Val (V)

TAR
STOP

CAY
His (H)

CTR
Leu (L)

TAR
STOP

AAY
Asn (N)

ATR
Met (M)
lie (I)
TAR
STOP


x









x












.


GTY
Val (V)

TAY
Tyr(Y)

TTR
Leu (L)

CTY
Leu (L)

TAY
Tyr (Y)

TTR
Leu (L)

ATY
Ile (I)

TAY
Tyr(Y)

TTR


Figure 5
F-to-E/Q/K replacement pathways. Start and end residues
are black with white codons, intermediate residues are gray
with black codons. Codons are written using DNA symbols
(A, adenine, T, thymine, C, cytosine, G, guanine, Y, pyrimi-
dine, R, purine). Red pathways cross two different residues;
blue pathways cross one residue; dashed pathways cross a
stop codon. Note that dashed paths are identical in (A) F-to-
E, (B) F-to-Q, and (C) F-to-K.


one to illustrate how opportunities arise as one exploits a
browsable database. In the example chosen for this illus-
tration, an F-to-Q replacement was found at alignment
site 7 in a family of beta barrel folded proteins that bind
to assorted hydrophobic ligands, including sterols, fatty
acids, and retinoids [see Additional file 3].

This particular F-to-Q replacement was found in a lineage
leading to a subfamily of proteins, known as cellular reti-
nol-binding protein II, after the mammals holding these
proteins diverged from bony fishes (Figure 6; branch 3). A
crystal structure of a member of this subfamily (135
amino acids, PDB: lopbA) was used as a reference struc-
ture; the F-to-Q replacement occurs at position 4 in this
protein.

We considered two hypotheses. The first, actually inti-
mated in the literature [29,30], is that the residue at align-
ment site 7 influences the geometry of the glutamine at
alignment site 122 (PDB:lopbA position 108) via hydro-
gen bonding. The glutamine at site 122 directly contacts
the hydroxyl group of retinol, and the carbonyl group of
retinal [29,30]. According to this model, F in site 7 forms
a "pi anion" interaction with the amide hydrogen of site
122 glutamine, constraining it to offer its amide oxygen to
either retinol or retinal, and therefore causing the protein
to favor the first as a ligand over the second. A Q at site 7
would be able to hydrogen bond with either the amide
oxygen or the amide hydrogen from Gin 122, allowing it
(in turn) to offer either the amide hydrogen or the amide
oxygen to the retinal or retinol (respectively), allowing it
to bind either (but see [31]). The intermediary amino
acids along the shortest pathways between F and Q do not
each possess the chemical properties to perform this
molecular physiological function. Leu cannot form a
hydrogen bond of any kind. His can form hydrogen
bonds that are analogous, although not geometrically


Table 4: F-to-E/Q/K replacements versus all 190 possible replacement patterns


replacements*
average probability**
distinct families
average members/
family
distinct alignment sites
average substitution
rate/site
sites with PDB data
protein surface sites
distinct branches
average branch length


all pairs

5,918,700
0.641
1,820
46.9

491,794
1.12

442,102
54%
148,522
0.207


F-to-E/Q/K

15,454
0.567
1,457
54.1

13,169
1.48

12,101
72%
11,800
0.697


F-to-E

5,922
0.563
1,142
60

5,347
1.44

4,932
73%
5,235
0.807


F-to-K

5,815
0.564
1,160
60.8

5,181
1.49

4,734
74%
5,089
0.782


F-to-Q

3,717
0.577
1,068
62.5

3,424
1.52

3,172
65%
3,456
0.761


*Detected as sites where the most probable parent and child residues were different.
*The average product of probabilities for the parent and child residues.


Page 9 of 18
(page number not for citation purposes)


BMC Bioinformatics 2006, 7:89








http://www.biomedcentral.com/1471-2105/7/89


0.1


Figure 6
Phylogeny of retinoid binding proteins. Cellular retinoic acid binding protein (CRABP) subfamilies I and II and cellular retinol
binding protein (CRBP) subfamilies I IV are boxed. Extant and ancestral amino acids corresponding to alignment site 7
(PDB:I opbA position 4) are shown at leaf and internal nodes.


identical, to those that can be formed by Phe or Gin. Tyr,
in contrast, can form hydrogen bonds using the pi anion
mechanism; indeed these are at least as strong as those
formed by Phe.

The second hypothesis is that at least some of the interme-
diates between Phe and Gin are acceptable to the protein.
Under this hypothesis additional sequence data that artic-
ulated the branch, sequences that were not available when
version 2.62 of iProClass was established, might show
that the intermediate steps were found.

The second proved to be the case. Motivated by the pecu-
liarity of the F-to-Q event in this protein family, we
searched for and found additional sequences from Callus
gallus (Genbank: XP422636) and Xenopus tropicalis (Gen-
bank: DN068529). Further, both contained a Tyr at site 7.
Therefore, articulating the tree along branch 3, we recon-
structed a Tyr as an intermediate between the parent Phe
and the derived Gin. This is, perhaps not accidentally,
exactly the residue that would be chosen based on the
chemical logic in the preceding paragraphs.


It is still necessary, of course, to take two steps to get from
Tyr to Gln. Leu would presumably be unacceptable in this
role; His might be tolerated. Although efforts to further
articulate the tree must involve the sequencing of marsu-
pials (these are in progress), we would predict that if the
tree were articulated and an intermediate amino acid were
detected, it would be His, not Leu.

Curiously, although Leu is not known in this lineage, it is
known elsewhere among the cellular retinol binding pro-
tein paralogs. This fact is easily discoverable with Mag-
num, given its support of browsing. It is found in site 7 in
several of the cousin families, including the cellular reti-
nol-binding protein III, known in human and gecko (as a
crystallin [321), and cellular retinol-binding protein IV,
known in mouse and human. Interestingly, the affinity of
these for retinal and retinol was both very low: Kd values
of 60 and 200 nM [33,34], compared with 0.1 nM for the
homologs that retain the ancestral F, and 10 nM for those
with the derived Gln at site 7 [35,36].

The opportunistic nature of database browsing, and the
ability of the Magnum database to support it, was then
illustrated again. The amino acid replacements along the


Page 10 of 18
(page number not for citation purposes)


F= '. ., ,, F..II u ,lI ,4 F
F ..... ,, r, ....i...i i. F C RA B P I
F F1". .... PDB: lcbiA
I .. .' I.F...4' 11 F
| F' , , ,' r L , , r F i ,'- F
Sr. *I. '.I. L IIF '.1 J: F
F


FF !F~ "" '" F PDB:lxcaA

-Ee l, 1 r IF-.. . ............. ....... F


Gallus gallus{chicken} NF00047806 F
Q Musmusculus{housemouse) NF00530124 Q
3 Q Rattus norvegicus{Norway rat}NF00559288 Q
2 F CRRP II
Q lHomo_sapiens{human) NF00132596 Q CRBP II
Sus scrofa{pig}NF00150317 Q PDB: lopbA
Daniorerio{zebra daniol NF00826980 F
L Mus_musculus{house_mouse} NF00510017 L CRBP IV
Homo_sapiens{human} NF00137740 L PDB: 1 IpjA
SRatusnorvegicus{Norwayrat}NF00567514 F
F F Mus musculus{housemouse} NF00517150 F
4 F 6 F Bostaurus{bovine)NF00160809 F CRBP I
5 Homosapiens{human} NF00078388 F PDB: 1crbO
LygodactyluspicturatusNF00043544- CRBP III
Homosapiens{human) NF00113102 L PDB: 1gglA


BMC Bioinformatics 2006, 7:89







http://www.biomedcentral.com/1471-2105/7/89


branch leading to the gecko crystallin (Figure 4; branch 7)
were delivered from the precomputed Magnum database
[see Additional file 4], as was their distribution in the crys-
tal structure of the nearest homolog (PDB:lgglA).
Remarkably, the sites undergoing replacement along this
branch were not randomly distributed. Rather, many of
them were concentrated in strands I and J, and helices 1
and 2, on the amino acids presented to the surface, creat-
ing a highly modified surface (Figure 7). We expect that
these are contact sites required for the formation of lens
crystals. A planetary biology explanation as to why this
protein, which binds the light absorber 3,4-didehydroreti-

A


nol, evolved as geckos moved from a nocturnal lifestyle to
a diurnal lifestyle is found in the literature [37].

Discussion
The Magnum database is presented here as a tool to per-
mit the rapid exploration of hypotheses relating to protein
structure and function, using evolutionary analyses, and
to support the pursuit of leads obtained by browsing. As
required to obtain the full power of a combined evolu-
tionary and structural analysis, Magnum integrates crys-
tallographic protein data with detailed evolutionary
features, such as amino acid replacements inferred along


C D



















Figure 7
Gecko crystallin contact site prediction. 15 of the 25 sites experiencing amino acid replacement along the branch leading to the
gecko crystallin protein (Figure 6; branch 7) are highlighted in red. The reference structure is PDB:IgglA. (A-D) Successive
quarter turns of the molecule about its y-axis.



Page 11 of 18
(page number not for citation purposes)


BMC Bioinformatics 2006, 7:89







http://www.biomedcentral.com/1471-2105/7/89


branches within the evolutionary tree. The precomputed
nature of the Magnum resource is critical for meeting its
intended uses but also imposes certain constraints.

Application to example questions
The present study interrogated a subset of replacements
involving three nucleotide substitutions, a buried site in
the protein structure, and a short branch in the phyloge-
netic tree. It is a rare and interesting event when a site
experiences multiple amino acid replacements on a short
branch. We hypothesized that these replacements on rela-
tively short branches may indicate a functionally impor-
tant site. The rationale behind this hypothesis is that if
none of the intermediates are observed, a "non-Marko-
vian" process might drive the introduction of the second
and third transversion after the first occurs, because the
amino acids that are intermediates in the F-to-E, F-to-Q, or
F-to-K replacements create selective disadvantage on the
host organisms. As a counterhypothesis, some of these
events correspond to sites that are unimportant for func-
tional reasons that would constrain their evolution, where
the intermediates are not seen in the reconstructed history
simply because the branch is not articulated (noting the
contingencies discussed in the introduction).

The F-to-Q replacement at alignment site 7 along the
branch leading to the mammalian retinol binding pro-
teins was a case of poor articulation. Orthologs from the
recently completed chicken and frog genomes possessed
Tyr, an intermediate amino acid in the F-to-Q pathway.
Nevertheless, the structural position of this site suggested
that the amino acid residue it holds can modulate ligand
binding affinity by influencing the orientation of the dual
hydrogen bond donor/acceptor residue at a second site,
which is in direct contact with the polar end of the bound
ligand. These hypotheses are specific enough to be exper-
imentally testable.

In the same protein family, we noticed an unusual protein
sequence from the gecko (Lygodactylus picturalis). Litera-
ture searches revealed that it had been found in the eye
lens and prompted us to ask if the sites replaced along this
branch might explain how this protein had been recruited
to function as a crystallin. Many of the sites were clustered
in a region of the protein with their side chains protruding
away from the surface a pattern consistent with the
emergence of a new binding site.

These three examples illustrate how the naturally organ-
ized Magnum database supports querying across many
protein families, as well as opportunistically following
leads obtained by browsing, to quickly explore and gener-
ate hypotheses related to protein structure and function.


Integrating protein evolution and structures
Existing protein sequence and structure data sources are
Magnum's foundation, yet Magnum is much more than
the sum of its source data. The specific sources of raw data
chosen for inclusion in Magnum, and three major steps
employed to generate the final product, distinguish Mag-
num from other databases such as PFAM [38], Pandit
[39], and PALI [40]. The Magnum source data is first
organized under a common schema designed to allow
storage, integration, and retrieval of the source data and
its derived information. Importantly, this step establishes
the orthologous relationships between sequence and
structure protein sites. Genealogies are then built for each
protein family and the derived molecular evolutionary
information stored in the database. This process is not a
trivial one. It requires extensive planning, computational
processing, and integrating of results from different stages
of the evolutionary analysis. Finally, interactive browsing
and analysis tools are deployed that make use of a custom
application programming interface to the Magnum data-
base.

The chemical behaviors of polypeptides determine, to a
large extent, biological phenotypes. Investigating the
molecular basis of phenotypes would be simple if the
behavior of a protein and the consequence of replacing
residues at particular protein sites were predictable from
the linear sequence of amino acids. In the absence of these
predictive powers, combining phylogenetic sequence
analyses with additional sources of complementary data
represents a powerful approach to the study of protein
molecular function [41,42]. While many would agree that
integrative biology is a worthwhile goal, the slow pace of
progress in this direction indicates significant challenges
exist. Here we succeeded at developing an integration
scheme for just two fields of protein study, evolution and
structure, which obviously have a synergistic relationship.
We applied the method in a high-throughput manner to
full-length protein families where at lea one crystal struc-
ture could be identified.

We attribute the successful synthesis, at least in part, to the
particularly detailed approach employed for reconstruct-
ing protein evolution. While many recognize that homol-
ogous sequences are more valuable when aligned and
organized phylogenetically, few have considered the ben-
efits of capturing branch-specific replacement patterns
through the use of ancestral sequences. Reconstructing
amino acid replacements along phylogenetic lineages is a
realistic and efficient means of modeling the actual events
that have occurred during the divergent evolution of the
homologous protein sequences. The alternative, when
ancestral sequences are unavailable, is to make 'leaf-leaf
comparisons. In leaf-leaf comparisons lineages are visited
multiple times as the paths for different leaf-leaf pairs


Page 12 of 18
(page number not for citation purposes)


BMC Bioinformatics 2006, 7:89







http://www.biomedcentral.com/1471-2105/7/89


cross many of the same branches [43]. Leaf-leaf compari-
sons introduce bias in the counting of replacements, thus
making the interpretations more difficult. In addition,
leaf-leaf pairs are always separated by at least two
branches and the number of replacements separating
them will always be larger (in many cases much larger)
than the number of replacements along individual
branches. When searching for a few replacements of func-
tional importance amidst a background of functionally
neutral replacements the smaller groups of mutations
organized by node-node comparisons are expected to be
helpful. In addition, node-node comparisons should cap-
ture multiple substitutions at a site better than leaf-leaf
treatments.

Homologous proteins with detectable sequence identity
possess nearly identical backbone structures [44]. There-
fore, a single structure reasonably depicts the fold of all
members of a family of homologs. Here the definition of
what constitutes a protein family becomes important.
Homeomorphic protein families, such as those from the
PIR Superfamily resource, contain similar domain con-
tents and arrangements, and the aligned sites share com-
mon structural environments. From a folded protein
model, sites with buried residues and sites with residues
exposed to the solvent are easily identified. Buried resi-
dues commonly interact to stabilize the protein fold,
whereas exposed residues can bind crucial ligands, cata-
lyze reactions, and mediate the formation of inter-molec-
ular complexes. Three-dimensional structures also divulge
groups of sites that might be distant in a linear sequence
but proximal in the folded structure, and regions of the
molecule that tend to remain flexible. When different pro-
teins are crystallized as a complex, a detailed map of
where and how different proteins interact is also captured.
All of these structural insights can be investigated from a
fresh perspective when the different levels of protein struc-
ture (primary, secondary, etc.) are connected with a
detailed account of protein evolution.

Strengths and weaknesses of the approach
As demonstrated here with examples from the cellular
retinoid binding proteins, Magnum allows any inquisitive
user to ask questions about individual protein families
and get quick answers, which may beg additional ques-
tions that are either answerable through Magnum or by
consulting the scientific literature. There is no need to be
an expert or to spend days or weeks preparing the data
before the question can be asked. This is the most obvious
advantage of a precomputed resource such as Magnum.

Another distinguishing feature of Magnum is its inclusion
of detailed molecular evolutionary and structural infor-
mation for a large number of diverse protein families. This
permits the user to browse or query the data across a range


of scales, such as specific replacement patterns (e.g. F-to-
Q), groups of coincident replacements along a branch,
replacements in similar structural elements, or all of these
in combination. For those who are willing to follow leads
opportunistically, Magnum offers a unique resource that
enables discovery through browsing. We have found that
this can be an effective method for research project selec-
tion.

Ideally, Magnum should consider all families of proteins,
regardless of whether they are represented in the crystallo-
graphic database, and should be rebuilt continuously.
Limited resources required us to consider only those fam-
ilies represented in the PDB by a crystal structure, and to
construct only a static database from a single release of
iProClass (Version 2.62). The first limitation is not exces-
sively severe; as such families are most susceptible to the
combination of structural biology and evolutionary biol-
ogy that has proven to be so valuable in inferring func-
tion. The second is more severe, as is illustrated by our
discussion of the cellular retinol binding proteins.

Use of Magnum to analyze cellular retinoid binding pro-
teins, and proteins involved in inflammation (M.B. and
S.A, in preparation), showed certain deficiencies related to
statically built databases. Most notable of these was the
absence of sequences deposited in the public databases
since the last build of Magnum was completed.

Another disadvantage is that specific metatheories must
be chosen before the database is built. The metatheories
and evolutionary parameters that we chose here generated
outputs that appear, by various metrics, reasonably accu-
rate, meaning that errors in the alignments, trees, and
ancestral sequences will not dominate any conclusions
drawn by automated analysis of the database. Ideally, if
resources were available, the evolutionary models for
individual families would also be continuously upgraded
as these metatheories improve. We can even imagine a
human inspector evaluating each protein family.

Expediency of ancestral sequences
The core of an evolutionary model is a multiple sequence
alignment and an evolutionary tree. The first describes the
historical relation between individual codons in the genes
that encode the derived proteins; aligning two amino
acids in two derived sequences is equivalent to a state-
ment that the codons encoding these two amino acids are
descendents of a single codon in the last common ances-
tral gene for the two.

Few dispute the existence of the ancestral gene/protein,
given the existing derived genes. The likelihood that the
model for any particular ancestral sequence corresponds
to a sequence that actually existed depends on many fac-


Page 13 of 18
(page number not for citation purposes)


BMC Bioinformatics 2006, 7:89







http://www.biomedcentral.com/1471-2105/7/89


tors. Most critical are the number of homologous
sequences that contribute to the reconstruction, the extent
to which the sequences have diverged, and how the tree
connecting them is articulated. Perhaps obviously, more
sequences, less divergence, and greater articulation leads
generally to a higher likelihood of correspondence than
fewer sequences, more divergence, and less articulation.
Also obviously, correspondence requires that the multiple
sequence alignment and the phylogenetic tree represent
the true evolutionary relationships. The treatment of phy-
logenetic correctness has received little attention in this
study for several reasons. The true evolutionary relation-
ships for a protein family, including topology and branch
lengths, are unknowable in many cases due to the large
number of possible trees. Assessing the reliability of an
evolutionary tree through bootstrapping is also not with-
out problems [45]. These issues would be partially sur-
mountable if additional resources were available. For
example, the hierarchal Bayesian framework integrates
over uncertainty in the tree and branch lengths when
inferring ancestral sequences [46].

Given a substantial dataset and satisfactory congruence
between the actual history of a protein family and features
of the model for that history, other factors become impor-
tant. These include the choice of a metatheory describing
the likelihood of specific amino acid replacements and
various tactics for apportioning variation between differ-
ent sites. Although much has been written comparing
these various features of evolutionary metatheories (e.g.
[47]), the selection of a metatheory generally has far less
impact on the outcome than other factors. In particular, it
does not appear to be important to consider higher order
models for protein sequence evolution that include
replacements at adjacent or distant sites when inferring
the amino acids at individual sites in ancestral sequences.
To a good approximation, the amino acids at the derived
sites are sufficient [48-50].

Conclusion
In these demonstrations we have relied upon universal
protein/codon relationships, and additional criteria from
structural and evolutionary biology, to investigate indi-
vidual amino acid replacements requiring three nucle-
otide substitutions. We remind that this is but one of
many analyses that can be performed using the protein
information assembled in the current version of Magnum.
Any analysis that considers how the amino acid replace-
ments recorded at individual sites, or collections thereof,
along a single branch (or even branches in series) have
affected a measurable property of the descendent pro-
tein(s) would be addressable with Magnum. Of course,
analyses that integrate structural information would make
fullest use of the resources delivered.


The integrative framework outlined here can be viewed as
the outset of an ambitious project to model biological sys-
tems from the bottom up. Integrating biological data from
other maturing areas would increase the number and type
of questions that can be addressed. For example, addi-
tional molecular components of biological systems (e.g.
transcription, translation, and protein dynamics), connec-
tions between these components (e.g. biochemical path-
ways, protein interactions, and metabolic networks), and
even a dating framework for inferring correlations
between the molecular and natural history records could
be integrated. When done properly, the integration of dis-
parate, but interrelated, sources of biological information
offers a powerful resource for asking biological questions,
where the answers can be used to formulate new hypoth-
eses and make predictions that warrant experimental test-
ing.

Methods
Protein families and structures
Sequences and annotations for a universal, non-redun-
dant protein set were obtained from the iProClass
resource [51]. Version 2.62 of iProClass was obtained in
March 2005. PIR Superfamily classifications [25] were
also obtained from iProClass. Unlike protein family clas-
sifications that split individual proteins into domains, PIR
Superfamilies include full-length proteins with common
domain architectures and sequence similarities over their
entire lengths.

To manage the redundancy in the Protein Data Bank, a
non-redundant set of protein chains from structures
solved by crystallography was obtained from the Pisces
protein culling server [52]. Each protein chain in the PDB
was then associated with individual iProClass protein
entries. This was accomplished with data from the
Seq2Struct resource [53], which associates proteins in the
PDB to entries in Swiss-Prot and TrEMBL [54].

In this way we obtained a set of PIR Superfamilies con-
taining at least one member associated with a structure in
the PDB. Secondary structure data were obtained using
the dictionary of protein secondary structure (DSSP) [55].
Surface accessibility of individual residues is reported as
the DSSP value divided by the maximum value obtainable
for the residue [56]. When more than one PDB chain is
aligned at a site we report the average surface accessibility.
Atomic coordinates were collected from PDB legacy files.

Multiple sequence alignments
To begin investigating families and structures together,
global multiple sequence alignments of family members
and PDB chains were constructed using ClustalW [57].
Because insertions and deletions occur less frequently in
structured (alpha helix or beta sheet) regions relative to


Page 14 of 18
(page number not for citation purposes)


BMC Bioinformatics 2006, 7:89







http://www.biomedcentral.com/1471-2105/7/89


unstructured regions [58], secondary structure informa-
tion was used to set the scoring penalties for placing gap
characters in the alignment.

For each family, a second alignment was constructed that
corresponds to the region in between the first and last col-
umns having homology to at least one PDB chain residue.
Redundant sequences were removed from these align-
ments. Sequences with fewer characters than 60% of the
alignment length were deemed to be fragmentary and
were also removed from the alignment. These alignments
were used for all further analyses.

Amino acid replacement model
Pairwise distance estimation, phylogenetic tree inference,
and ancestral sequence reconstructions were carried out
with the Ancescon package [50]. Ancescon employs a
probabilistic model of amino acid replacement in which
alignment sites are assumed to evolve independently
according to a homogeneous, stationary and time reversi-
ble Markov process. The assumption of time reversibility
dictates that the matrix of amino acid substitution proba-
bilities (or relative rates), S, be symmetrical. The S matrix
used was collected from more than 3,000 globular
sequences and is one of the best available descriptions of
amino acid replacement for most protein families [59].

For each alignment, an amino acid frequency vector, 7,
and a rate matrix, Q, are determined. Off diagonal ele-
ments Qij are calculated as the off diagonal elements of
the matrix product

Q = S diag(r) (1)

and diagonal elements Qii were fixed so that the row sums
of Q equal zero. In theory, the transition probability
matrix of all 20 amino acids

P(t) = eQt (2)

describes the probability of amino acid i being replaced by
amino acid j over time t as P,(t). In practice, Pi(t) values
are calculated by decomposing the Q matrix into eigensys-
tems.

Substitution rate factors, pairwise distances, and
phylogenies
For each site, a substitution rate factor was calculated
using the alignment based method [50]. Invariant sites
were assigned a rate of zero. At variable sites, substitution
rates were calculated according to the average transition
probability for all non-gapped, non-identical amino acids
pairs and normalized by the length of the alignment. In
this way, the substitution rate across all alignment sites


sums to the length of the alignment, just as if the substitu-
tion rate per site was held at unity.

Evolutionary distances between leaf sequences were esti-
mated by maximum likelihood while incorporating sub-
stitution rate factors for each site. From the matrix of leaf-
leaf distances, phylogenetic trees were inferred with the
weighted neighbor joining algorithm, Weighbor [28].
Weighbor accounts for the larger errors inherent in longer
distance estimates and has been reported to escape the
problems of long branches experienced by parsimony and
neighbor joining approaches. Weighbor produces trees
comparable to those from ML phylogeny reconstruction
in much less time.

Ancestral sequence reconstructions
Probabilistic ancestral sequences for each internal node
were inferred using the Ancescon package [50]. The mod-
els implemented in Ancescon deal with each site in an
alignment independently. The only model variables are
the ancestral amino acids. Other parameters in the model
are defined for each family. These include an amino acid
frequency vector calculated from the alignment, a set of
branch lengths from Weighbor, substitution rate factors
for each site, and, of course, the observed leaf sequences.
Ancestral amino acids at a site are reconstructed by maxi-
mizing the conditional probability of the ancestral charac-
ter given the observed data in the leaf sequences.
Reconstruction using pre-set model parameters is gener-
ally referred to as an empirical Bayesian analysis [46].

Ancescon implements the method of Koshi and Goldstein
[48] for marginal reconstructions. In marginal reconstruc-
tion, each internal tree node site is treated separately. For
each site at an internal node, the probability of ancestral
amino acid X (Ax) conditional on the set of observed leaf
sequences {AL}, the mutation matrix (M), and the phylo-
genetic tree (T) is calculated using Bayes' Theorem:


P(Ax ,M, P({AL} AX,M,T)*P(AX)
S L P({AL} I M,T)


where the P( {AL} Ax, M, T) is the conditional probability
of the leaf sequences for a given ancestral amino acid, and
P(Ax) the probability of the given ancestral amino acid.
Because the amino acids at all other internal nodes are
unknown, P( {AL} Ax, M, T) is obtained by summing the
probabilities of the replacements needed to generate the
leaf sequences through all possible replacement paths
between the node and the leaf sequences. For example, for
a single site at an internal node in an unrooted tree with
four leaf sequences (1-4) and two internal nodes (5, 6),
the P({AL} Ax, M, T) can be expanded as:

P(AIA2,A3,A4IAslT)=- PA&(a, d,&)P, (a dA )P&A,(', d&,A,)P&A,(a, dA,.&A)P&(, dA,) (4)


Page 15 of 18
(page number not for citation purposes)


BMC Bioinformatics 2006, 7:89







http://www.biomedcentral.com/1471-2105/7/89


where the summation is over each of the twenty amino
acids at node A6. For each site at internal node A5, the P(Ax
SAL, M, T) is calculated for each of the twenty amino acids.
Thus, at each internal node site marginal reconstructions
generate a vector composed of the twenty amino acids and
their posterior probabilities. In this context, the best
ancestral amino acid at a site will be that which has the
highest posterior probability.

Ancescon implements the method of Pupko et al. [60] for
joint reconstructions. In a joint reconstruction sites are
treated independently but all internal nodes are treated as
a set. The objective is to identify the most likely set of
amino acids for all internal nodes at a site that yields the
maximum joint likelihood of the tree. As a result, only a
single amino acid is reported for each site and conditional
posterior probabilities at a site are not available. For
details of the joint method the reader is referred to [60].

Gaps were placed in ancestral sequences according to the
default settings of the Ancescon program. The same rule-
based strategy was applied in both marginal and joint
reconstructions. Thus, the gapping history is the same
regardless of the reconstruction method. The placement
of gaps in ancestral sequence proceeds by traversing a tree
from the leaves inward. If an internal node had one or
zero branches leading to the remaining descendents a gap
character was placed at that site at that node.

Reconstructed amino acid replacements
Replacements were tallied by comparing residues at
aligned sites of parent (ancestor) and child (descendent)
sequences that define a branch. Note that the parent-child
relations are created for organizational purposes and do
not imply the use of rooted phylogenies. Three methods
of counting were employed. The simplest method [61]
counts a replacement with weight probability of one if the
best parent and child amino acids are different. This is
referred to as the 'best count' method. The best residue
was judged by the posterior probability values for all
twenty residues. The 'best fractional' method finds
replacements by the same rule as the best count, but
counts are given a weight probability equal to the product
of the parent and child posterior probabilities. The 'all
fractional' method compares each parent with each child
residue and reports the sum of combined probabilities
where the amino acids differ. In the rare event that two
residues had the same posterior probability the residue
suggested by the Ancescon software was treated as the pri-
mary residue. Note that these ties only occur when the
best possible residue has a posterior probability less than
or equal to 0.5.


Count and Dayhoff matrices
Log-odds (Dayhoff) matrices were created with the Crea-
teDayMatrices option in the DARWIN package [62]. As
input, count matrices were constructed using the three
counting methods described above. For the 'best count'
method, identical amino acid pairs (on-diagonal terms)
were scored with a value of 1. A value of 0.5 was added to
both off-diagonal elements for non-identical pairs [61].
For example, upon observing a TS (threonine-serine) pair,
0.5 was added to both TS and ST matrix elements. This
results in a symmetrical matrix to account for the fact that
the direction of change is unspecified along a branch in an
unrooted tree. In the matrices derived with 'best frac-
tional' and 'all fractional' tools, pairs were scored as the
product of the posterior probabilities. As before, non-
identical pair scores were halved and added to both matrix
elements.

Count matrices were constructed from pairs of aligned
sequences representing either two consecutive internal
nodes (node-node), or two leaf sequences (leaf-leaf).
Using branch length values (1), sequence pairs were placed
into one of three bins, short (0.005<1 <0.015), medium
(0.120<1 <0.220), and long (0.520<1 <1.00). In each
range, 10,000 node-node or leaf-leaf sequence pairs were
randomly selected. For node-node pairs the branch
lengths were the values from the Weighbor phylogenies.
For leaf-leaf pairs, branch lengths represent the sum of
individual branch lengths along the shortest path from
one leaf to another. The chosen ranges were a compro-
mise between maintaining a narrow enough range to keep
the members of each bin similar, while maintaining a
wide enough range to obtain enough branches to permit
statistical analyses. Frequency counts and Dayhoff matri-
ces are provided as supplementary material [see Addi-
tional file 1].

Availability
The Magnum home page on the internet http://
www.ffame.org/mbradley/magnum contains a family
search interface, and generates dynamic pages for each
family. This environment is well-suited for interactive
inspection of the protein members, multiple sequence
alignments, phylogenetic trees, crystal structures, all of the
F-to-E/K/Q replacements, as well as other features of the
protein families. The PERL application programming
interface is also obtainable from the Magnum home page.
The MySQL database tables are currently being distributed
on digital video discs upon request from the authors.

Authors' contributions
MB performed the study and drafted the manuscript. SB
participated in designing the study and preparing the
manuscript.



Page 16 of 18
(page number not for citation purposes)


BMC Bioinformatics 2006, 7:89








http://www.biomedcentral.com/1471-2105/7/89


Additional material


Acknowledgements
We thank the Foundation for Applied Molecular Evolution for computa-
tional resources provided. We are grateful to S. Chamberlin, J. Rest, and K.
Bullaughey for constructive comments on this manuscript. MB was sup-
ported by a Ruth L. Kirschstein National Research Service Award from the
National Human Genome Research Institute (I F32 HG003245).

References
I. Benner SA: Interpretive proteomics--finding biological mean-
ing in genome and proteome databases. Adv Enzyme Regul
2003, 43:271-359.
2. Fukami-Kobayashi K, Schreiber DR, Benner SA: Detecting com-
pensatory covariation signals in protein evolution using
reconstructed ancestral sequences. j Mol Biol 2002,
319:729-743.
3. Taylor WR, Hatrick K: Compensating changes in protein mul-
tiple sequence alignments. Protein Eng 1994, 7:341-348.
4. Shindyalov IN, Kolchanov NA, Sander C: Can three-dimensional
contacts in protein structures be predicted by analysis of
correlated mutations? Protein Eng 1994, 7:349-358.
5. Neher E: How frequent are correlated changes in families of
protein sequences? Proc Notl Acad Sci U S A 1994, 91:98-102.
6. Gobel U, Sander C, Schneider R, Valencia A: Correlated muta-
tions and residue contacts in proteins. Proteins 1994,
18:309-317.
7. Benner SA, Cannarozzi G, Gerloff D, Turcotte M, Chelvanayagam G:
Bona Fide Predictions of Protein Secondary Structure Using
Transparent Analyses of Multiple Sequence Alignments.
Chem Rev 1997, 97:2725-2844.


Additional File 1
Frequency counts and I .-ii matrices. Frequency counts of amino acid
pairs from bins of short, medium and long branches, ,,.11 i ,, i ,..; i. .....
matrices derived from these counts.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-7-89-S1.pdf]

Additional File 2
F-to-E/Q/K replacements. List of F-to-E/Q/K replacements on short
branches and at buried sites.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-7-89-S2.pdf]

Additional File 3
Multiple *.. i.... I, ..... ., ..f cellular retinoid bindingproteins. Aligned
sequences ordered according to the tree shown in Figure 6. The sequence
of the reference structure PDB:lopbA has been underlined.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-7-89-S3.pdf]

Additional File 4
Amino acid replacement report. Amino acid replacements for the seven
labeled branches in Figure 6.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-7-89-S4.pdf]


8. Rost B, Sander C, Schneider R: PHD--an automatic mail server
for protein secondary structure prediction. Comput Appl Biosci
1994, 10:53-60.
9. Tauer A, Benner SA: The BI2-dependent ribonucleotide
reductase from the archaebacterium Thermoplasma acido-
phila: an evolutionary solution to the ribonucleotide reduct-
ase conundrum. Proc Not! Acad Sci U S A 1997, 94:53-58.
10. Gaucher EA, Miyamoto MM, Benner SA: Evolutionary, structural
and biochemical evidence for a new interaction site of the
leptin obesity protein. Genetics 2003, 163:1549-1553.
I I. Gaucher EA, Graddy LG, Li T, Simmen RC, Simmen FA, Schreiber DR,
Liberles DA, Janis CM, Benner SA: The planetary biology of cyto-
chrome P450 aromatases. BMC Biol 2004, 2:19.
12. Bradley ME, Benner SA: Phylogenomic approaches to common
problems encountered in the analysis of low copy repeats:
The sulfotransferase IA gene family example. BMC Evol Biol
2005, 5:22.
13. Thornton JW, Need E, Crews D: Resurrecting the ancestral
steroid receptor: ancient origin of estrogen signaling. Science
2003, 301:1714-1717.
14. ThomsonJM, Gaucher EA, Burgan MF, De Kee DW, Li T, ArisJP, Ben-
ner SA: Resurrecting ancestral alcohol dehydrogenases from
yeast. Not Genet 2005, 37:630-635.
15. Gaucher EA, Thomson JM, Burgan MF, Benner SA: Inferring the
palaeoenvironment of ancient bacteria on the basis of resur-
rected proteins. Nature 2003, 425:285-288.
16. Stackhouse J, Presnell SR, McGeehan GM, Nambiar KP, Benner SA:
The ribonuclease from an extinct bovid ruminant. FEBS Lett
1990, 262:104-106.
17. Jermann TM, Opitz JG, Stackhouse J, Benner SA: Reconstructing
the evolutionary history of the artiodactyl ribonuclease
superfamily. Nature 1995, 374:57-59.
18. Chang BS, Jonsson K, Kazmi MA, Donoghue MJ, Sakmar TP: Recre-
ating a functional ancestral archosaur visual pigment. Mol Biol
Evol 2002, 19:1483-1489.
19. Messier W, Stewart CB: Episodic adaptive evolution of primate
lysozymes. Nature 1997, 385:151-154.
20. UgaldeJA, Chang BS, Matz MV: Evolution of coral pigments rec-
reated. Science 2004, 305:1433.
21. Benner SA, Sassi S, Gaucher EA: Experimental paleoscience.
Methods Enzymol 2005, submitted:.
22. Benner SA, Chamberlin SG, Liberles DA, Govindarajan S, Knecht L:
Functional inferences from reconstructed evolutionary biol-
ogy involving rectified databases--an evolutionarily
grounded approach to functional genomics. Res Microbiol 2000,
151:97-106.
23. Benner SA, Caraco MD, Thomson JM, Gaucher EA: Planetary biol-
ogy--paleontological, geological, and molecular histories of
life. Science 2002, 296:864-868.
24. Benner SA: The past as the key to the present: resurrection of
ancient proteins from eosinophils. Proc NotlAcad Sci USA 2002,
99:4760-4761.
25. Wu CH, Nikolskaya A, Huang H, Yeh LS, Natale DA, Vinayaka CR,
Hu ZZ, Mazumder R, Kumar S, Kourtesis P, Ledley RS, Suzek BE,
Arminski L, Chen Y, Zhang J, Cardenas JL, Chung S, Castro-Alvear j,
Dinkov G, Barker WC: PIRSF: family classification system at
the Protein Information Resource. Nucleic Acids Res 2004, 32
Database issue:D 112-4.
26. Cohen FE, Abarbanel RM, Kuntz ID, Fletterick RJ: Turn prediction
in proteins using a pattern-matching approach. Biochemistry
1986, 25:266-275.
27. Dayhoff MO: Atlas of Protein Sequence and Structure. Edited
by: Dayhoff MO. Washington, D.C., National Biomedical Research
Foundation. 5 volumes (3 supplements) (1965-1978)
28. Bruno WJ, Socci ND, Halpern AL: Weighted neighbor joining: a
likelihood-based approach to distance-based phylogeny
reconstruction. Mol Biol Evol 2000, 17:189-197.
29. Cowan SW, Newcomer ME, Jones TA: Crystallographic studies
on a family of cellular lipophilic transport proteins. Refine-
ment of P2 myelin protein and the structure determination
and refinement of cellular retinol-binding protein in complex
with all-trans-retinol. j Mol Biol 1993, 230:1225-1246.
30. Winter NS, BrattJM, Banaszak LJ: Crystal structures of holo and
apo-cellular retinol-binding protein II. j Mol Biol 1993,
230:1247-1259.




Page 17 of 18
(page number not for citation purposes)


BMC Bioinformatics 2006, 7:89








http://www.biomedcentral.com/1471-2105/7/89


31. Jamison RS, Kakkad B, Ebert DH, Newcomer ME, Ong DE: Test of
the contribution of an amino-aromatic hydrogen bond to
protein function. Biochemistry 1995, 34:1 128-1 1132.
32. Werten PJ, Roll B, van Aalten DM, de Jong WW: Gecko iota-crys-
tallin: how cellular retinol-binding protein became an eye
lens ultraviolet filter. Proc Natl Acad Sci U S A 2000, 97:3282-3287.
33. Folli C, Calderone V, Ottonello S, Bolchi A, Zanotti G, Stoppini M,
Berni R: Identification, retinoid binding, and x-ray analysis of
a human retinol-binding protein. Proc Not! Acad Sci US A 2001,
98:3710-3715.
34. Folli C, Calderone V, Ramazzina I, Zanotti G, Berni R: Ligand bind-
ing and structural analysis of a human putative cellular reti-
nol-binding protein. j Biol Chem 2002, 277:41970-41977.
35. Li E, Qian SJ, Yang NC, d'Avignon A, Gordon Jl: 19F nuclear mag-
netic resonance studies of 6-fluorotryptophan-substituted
rat cellular retinol binding protein II produced in Escherichia
coli. An analysis of four tryptophan substitution mutants and
their interactions with all-trans-retinol. J Biol Chem 1990,
265:11549-11554.
36. Li E, Qian SJ, Winter NS, d'Avignon A, Levin MS, Gordon Jl: Fluorine
nuclear magnetic resonance analysis of the ligand binding
properties of two homologous rat cellular retinol-binding
proteins expressed in Escherichia coli. j Biol Chem 1991,
266:3622-3629.
37. Roll B: Multiple origin of diurnality in geckos: evidence from
eye lens crystallins. Naturwissenschaften 2001, 88:293-296.
38. Bateman A, Coin L, Durbin R, Finn RD, Hollich V, Griffiths-Jones S,
Khanna A, Marshall M, Moxon S, Sonnhammer EL, Studholme DJ,
Yeats C, Eddy SR: The Pfam protein families database. Nucleic
Acids Res 2004, 32:D 138-4 1.
39. Whelan S, de Bakker PI, Goldman N: Pandit: a database of pro-
tein and associated nucleotide domains with inferred trees.
Bioinformatics 2003, 19:1556-1563.
40. Gowri VS, Pandit SB, Karthik PS, Srinivasan N, Balaji S: Integration
of related sequences with protein three-dimensional struc-
tural families in an updated version of PALI database. Nucleic
Acids Res 2003, 31:486-488.
41. Eisen JA: Phylogenomics: improving functional predictions for
uncharacterized genes by evolutionary analysis. Genome Res
1998, 8:163-167.
42. Sjolander K: Phylogenomic inference of protein molecular
function: advances and challenges. Bioinformatics 2004,
20:170-179.
43. Gonnet GH, Korostensky C, Benner S: Evaluation measures of
multiple sequence alignments. j Comput Biol 2000, 7:261-276.
44. Chothia C, Lesk AM: The relation between the divergence of
sequence and structure in proteins. Embo 1986, 5:823-826.
45. Brocchieri L: Phylogenetic inferences from molecular
sequences: review and critique. Theor Popul Biol 2001, 59:27-40.
46. HuelsenbeckJP, BollbackJP: Empirical and hierarchical Bayesian
estimation of ancestral states. Syst Biol 2001, 50:351-366.
47. Parisi G, Echave J: Generality of the structurally constrained
protein evolution model: assessment on representatives of
the four main fold classes. Gene 2005, 345:45-53.
48. Koshi JM, Goldstein RA: Probabilistic reconstruction of ances-
tral protein sequences. j Mol Evol 1996, 42:313-320.
49. Zhang j, Nei M: Accuracies of ancestral amino acid sequences
inferred by the parsimony, likelihood, and distance methods.
j Mol Evol 1997, 44 Suppl I:S 139-46.
50. Cai W, Pei J, Grishin NV: Reconstruction of ancestral protein
sequences and its applications. BMC Evol Biol 2004, 4:33.
51. Wu CH, Huang H, Nikolskaya A, Hu Z, Barker WC: The iProClass
integrated database for protein functional analysis. Comput
Biol Chem 2004, 28:87-96.
52. Wang G, Dunbrack RLJ: PISCES: a protein sequence culling
server. Bioinformatics 2003, 19:1589-1591.
53. Via A, Zanzoni A, Helmer-Citterich M: Seq2Struct: a resource for
establishing sequence-structure links. Bioinformatics 2005,
21:551-553.
54. Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A,
Gasteiger E, Martin MJ, Michoud K, O'Donovan C, Phan I, Pilbout S,
Schneider M: The SWISS-PROT protein knowledgebase and
its supplement TrEMBL in 2003. Nucleic Acids Res 2003,
31:365-370.


55. Kabsch W, Sander C: Dictionary of protein secondary struc-
ture: pattern recognition of hydrogen-bonded and geometri-
cal features. Biopolymers 1983, 22:2577-2637.
56. Chothia C: The nature of the accessible and buried surfaces in
proteins. j Mol Biol 1976, 105:1-12.
57. Thompson JD, Higgins DG, Gibson TJ: CLUSTAL W: improving
the sensitivity of progressive multiple sequence alignment
through sequence weighting, position-specific gap penalties
and weight matrix choice. Nucleic Acids Res 1994, 22:4673-4680.
58. Benner SA, Cohen MA, Gonnet GH: Empirical and structural
models for insertions and deletions in the divergent evolu-
tion of proteins. j Mol Biol 1993, 229:1065-1082.
59. Whelan S, Goldman N: A general empirical model of protein
evolution derived from multiple protein families using a
maximum-likelihood approach. Mol Biol Evol2001, 18:691-699.
60. Pupko T, Pe'er I, Hasegawa M, Graur D, Friedman N: A branch-and-
bound algorithm for the inference of ancestral amino-acid
sequences when the replacement rate varies among sites:
Application to the evolution of five gene families. Bioinformat-
ics 2002, 18:1116-1123.
61. Benner SA, Cohen MA, Gonnet GH: Amino acid substitution
during functionally constrained divergent evolution of pro-
tein sequences. Protein Eng 1994, 7:1323-1332.
62. Gonnet GH, Benner SA: Computational Biochemistry Research
at ETH. In Technical Report 154 Department Informatik Zurich, Swiss
Federal Institute of Technology; 1991.


Page 18 of 18
(page number not for citation purposes)


Publish with BioMed Central and every
scientist can read your work free of charge
"BioMed Central will be the most significant development for
disseminating the results of biomedical research in our lifetime."
Sir Paul Nurse, Cancer Research UK
Your research papers will be:
*available free of charge to the entire biomedical community
peer reviewed and published immediately upon acceptance
cited in PubMed and archived on PubMed Central
yours you keep the copyright

Submit your manuscript here: BioMedcentral
http://www.biomedcentral.com/info/publishing adv.asp


BMC Bioinformatics 2006, 7:89




University of Florida Home Page
© 2004 - 2010 University of Florida George A. Smathers Libraries.
All rights reserved.

Acceptable Use, Copyright, and Disclaimer Statement
Last updated October 10, 2010 - - mvs