Accuracy of protein-protein binding sites in high-throughput template-based modeling.
ABSTRACT The accuracy of protein structures, particularly their binding sites, is essential for the success of modeling protein complexes. Computationally inexpensive methodology is required for genome-wide modeling of such structures. For systematic evaluation of potential accuracy in high-throughput modeling of binding sites, a statistical analysis of target-template sequence alignments was performed for a representative set of protein complexes. For most of the complexes, alignments containing all residues of the interface were found. The full interface alignments were obtained even in the case of poor alignments where a relatively small part of the target sequence (as low as 40%) aligned to the template sequence, with a low overall alignment identity (<30%). Although such poor overall alignments might be considered inadequate for modeling of whole proteins, the alignment of the interfaces was strong enough for docking. In the set of homology models built on these alignments, one third of those ranked 1 by a simple sequence identity criteria had RMSD<5 A, the accuracy suitable for low-resolution template free docking. Such models corresponded to multi-domain target proteins, whereas for single-domain proteins the best models had 5 A<RMSD<10 A, the accuracy suitable for less sensitive structure-alignment methods. Overall, approximately 50% of complexes with the interfaces modeled by high-throughput techniques had accuracy suitable for meaningful docking experiments. This percentage will grow with the increasing availability of co-crystallized protein-protein complexes.
- [Show abstract] [Hide abstract]
ABSTRACT: Protein-protein interactions lie at the heart of most cellular processes. Many experimental and computational studies aim to deepen our understanding of these interactions and improve our capacity to predict them. In this respect, the evolutionary perspective is most interesting, since the preservation of structure and function puts constraints on the evolution of proteins and their interactions. However, uncovering these constraints remains a challenge, and the description and detection of evolutionary signals in protein-protein interactions is currently a very active field of research. Here, we review recent works dissecting the mechanisms of protein-protein interaction evolution and exploring how to use evolutionary information to predict interactions, both at the global level of the interactome and at the detailed level of protein-protein interfaces. We first present to what extent protein-protein interactions are found to be conserved within interactomes and which properties can influence their conservation. We then discuss the evolutionary and co-evolutionary pressures applied on protein-protein interfaces. Finally, we describe how the computational prediction of interfaces can benefit from evolutionary inputs.Archives of Biochemistry and Biophysics 07/2014; · 3.37 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Understanding the molecular basis of protein function remains a central goal of biology, with the hope to elucidate the role of human genes in health and in disease, and to rationally design therapies through targeted molecular perturbations. We review here some of the computational techniques and resources available for characterizing a critical aspect of protein function - those mediated by protein-protein interactions (PPI). We describe several applications and recent successes of the Evolutionary Trace (ET) in identifying molecular events and shapes that underlie protein function and specificity in both eukaryotes and prokaryotes. ET is a part of analytical approaches based on the successes and failures of evolution that enable the rational control of PPI.Progress in Biophysics and Molecular Biology 05/2014; · 2.91 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Ultrafiltration and HPLC were employed to assess binding rates between rat plasma protein and two active compounds with lipid-regulating properties (alisol B 23-acetate and alisol A 24-acetate) from Alismaorientale rhizomes (Alismatis Rhizoma), a traditional Chinese medicine. SDS-PAGE was used for the evaluation of the binding between the alisol acetates and Hb in plasma. The fluorescence spectroscopy and circular dichroism spectroscopy were also combined with molecular modeling to explore binding mechanisms between Hb and the alisol acetates under imitative physiological condition. The ultrafiltration results show that alisol B 23-acetate bound more strongly than alisol A 24-acetate to plasma protein. SDS-PAGE results may suggest that alisols bind to Hb in plasma. The spectroscopy results are consisting with the molecular modeling results, and they indicate that the differences in plasma protein binding strength between the two compounds may be related to their side chains. A folded side chain/parent ring bound more strongly to Hb than an open side chain/parent ring.Bioorganic & medicinal chemistry letters. 07/2014;
Accuracy of Protein-Protein Binding Sites in High-
Throughput Template-Based Modeling
Petras J. Kundrotas, Ilya A. Vakser*
Center for Bioinformatics and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America
The accuracy of protein structures, particularly their binding sites, is essential for the success of modeling protein
complexes. Computationally inexpensive methodology is required for genome-wide modeling of such structures. For
systematic evaluation of potential accuracy in high-throughput modeling of binding sites, a statistical analysis of target-
template sequence alignments was performed for a representative set of protein complexes. For most of the complexes,
alignments containing all residues of the interface were found. The full interface alignments were obtained even in the case
of poor alignments where a relatively small part of the target sequence (as low as 40%) aligned to the template sequence,
with a low overall alignment identity (,30%). Although such poor overall alignments might be considered inadequate for
modeling of whole proteins, the alignment of the interfaces was strong enough for docking. In the set of homology models
built on these alignments, one third of those ranked 1 by a simple sequence identity criteria had RMSD,5 A˚, the accuracy
suitable for low-resolution template free docking. Such models corresponded to multi-domain target proteins, whereas for
single-domain proteins the best models had 5A˚,RMSD,10 A˚, the accuracy suitable for less sensitive structure-alignment
methods. Overall, ,50% of complexes with the interfaces modeled by high-throughput techniques had accuracy suitable
for meaningful docking experiments. This percentage will grow with the increasing availability of co-crystallized protein-
Citation: Kundrotas PJ, Vakser IA (2010) Accuracy of Protein-Protein Binding Sites in High-Throughput Template-Based Modeling. PLoS Comput Biol 6(4):
Editor: Ruth Nussinov, National Cancer Institute, United States of America and Tel Aviv University, Israel
Received May 26, 2009; Accepted March 1, 2010; Published April 1, 2010
Copyright: ? 2010 Kundrotas, Vakser. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The study was supported by R01 GM074255 grant from NIH. The funders had no role in study design, data collection and analysis, decision to publish,
or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com.
Protein interactions are a central component of life processes.
The structural characterization of these interactions is essential for
our ability to understand these processes and to utilize this
knowledge in biology and medicine. Experimental approaches,
primarily X-ray crystallography, are producing an increasing
number of protein structures (www.pdb.org), which to a certain
extent are representative of a significant part of the ‘‘protein
universe.’’ However, the overall number of proteins by far exceeds
the capabilities of the experimental structure-determination ap-
proaches [1,2]. The answer to this discrepancy is computational
modeling of protein structures. The modeling not only can supply
the vast majority of protein structures, but also, importantly, is
indispensable for understanding the fundamental principles of
protein structure and function.
Computational structure prediction methodology historically
started with ab initio approaches based on approximation of
fundamental physical principles, and continues to develop in this
direction for the goal of learning the principles of protein structure
and function. However, for the purpose of predicting protein
structures, it has largely evolved to comparative techniques based
on experimentally determined structural templates (to a significant
extent due to the increasing availability of such templates). Such
approaches are faster, more reliable, and provide accuracy
increasingly comparable with experimental approaches .
A similar trend is underway in structural modeling of protein
interactions - protein docking [4,5]. Because of the nature of the
problem, the ab initio structure-based methods in docking
(prediction of the complex from known separate structures) are
relatively more reliable than those in individual protein modeling
(docking rigid-body approximation has only six degrees of freedom
and has an established record of practical applications). However,
the knowledge-based docking approaches, including the template
based ones, are rapidly developing, following the increasing
availability of the experimentally determined structures of protein-
protein complexes, which generally are more difficult to determine
than the structures of individual proteins [6–8]. It was established
by studies based on different sets of proteins that proteins similar in
sequence, fold and/or function share similar binding sites [9–12].
Quantitative guidelines for quality of homology modeling of
protein complexes were provided by Aloy and others  where it
was demonstrated that sequence identities .40% yield high
similarity of protein-protein binding sites.
The modeling techniques for proteins and protein complexes
applicable to entire genomes have to be high-throughput by
design. This reason, along with the still limited availability of
templates, causes the modeling techniques to combine high-
resolution approaches, when available and computationally
feasible, with low-resolution capabilities, for broad coverage of
the proteome/interactome. Such low-resolution approaches still
are capable of predicting essential structural characteristics of
PLoS Computational Biology | www.ploscompbiol.org1 April 2010 | Volume 6 | Issue 4 | e1000727
proteins and protein interactions, including the binding sites
[14–16], macromolecular assemblies  and binding modes for
protein-protein [18,19] and protein-ligand  complexes.
For template based docking (based on co-crystallized protein-
protein templates), the degree of similarity to the templates is key
to the accuracy of the docking. For ab initio, as well as some
knowledge/template based docking techniques, the accuracy of
the resulting structures is directly dependent on the accuracy of the
individual participating proteins, which in its turn is based on the
similarity to the templates of individual proteins. In both cases, the
critical component affecting the docking outcome is the ability to
model the structures of the binding sites. Although one can argue
that the structure of the whole proteins is important in general, the
binding sites are the parts that have a direct effect on the accuracy
of the predicted complex. Earlier estimates showed that the
binding site accuracy of ,6 A˚CaRMSD is sufficient for low-
resolution ab initio docking  (,3 A˚CaRMSD for small ligand-
receptor docking ), with even lower accuracy suitable for
meaningful docking prediction by template based docking (Sinha
et al. in preparation).
In the current study we present a systematic analysis of the
sequence alignment and subsequent modeling accuracy of known
protein-protein binding sites. The analysis is performed and
validated on the DOCKGROUND comprehensive dataset of co-
crystallized protein-protein complexes . According to the
purpose of this study (the assessment of high-throughput modeling
capabilities for genome-size systems) the modeling was deliberately
performed in a high-throughput fashion using standard alignment
(BLASTPGP ) and comparative modeling (NEST )
programs, as opposed to more detailed and sophisticated (but
also more computationally expensive) multi-template procedures.
The results show that for a significant part of the proteins the
binding sites can be modeled with accuracy that would ensure
meaningful docking, even in cases of alignments considered poor
for modeling of monomeric proteins. Thus, structural modeling of
protein-protein interactions can often be performed by means
simpler than those typically used for modeling of monomeric
proteins, despite the fact that protein-protein interactions in
general are on the next complexity level relative to individual
proteins. However, further advancement of large scale, high-
throughput docking requires progress in experimental determina-
tion of structural templates.
Interface Coverage in Local Alignments
To assess the potential quality of binding site modeling, the
sequences of 658 two-chain complexes (Table 1) were subjected to
PSI-BLAST search for homologous sequences in the PDB data
bank. The following alignments were excluded from the resulting
pool: (a) statistically insignificant alignments with expectation value
e.1 and (b) alignments with target/template difference ,10
residues. The latter allowed us to avoid a bias in alignment
statistics caused by overrepresentation of certain groups of the
proteins and their mutants in PDB. The resulting 66,706 align-
ments were further analyzed in terms of the target sequence
coverage q (see Methods, Eq. 1), and coverage of the target
interface residues qint(Eq. 2), with an emphasis on alignments with
qint=100% (hereafter referred to as full interface coverage, or FIC,
alignments). A residue of the target complex was assigned to the
interface if the distance between any atom of the residue and any
atom of the other subunit in the complex was less than the sum of
the van der Waals radii of the atoms plus the diameter of water
molecule 2.8 A˚. An alignment was considered FIC with a level of
tolerance that allowed one target interface residue to be missing in
the alignment. The analysis showed that 37,062 alignments, or
56.1% of the entire alignment pool, are FIC alignments. On the
other hand, FIC alignments were observed for both monomers in
alignments of 218 target complexes and for one of the monomers
in additional 101 targets, which together constitute most (97%) of
In the distribution of FIC alignments for different functional
classes of proteins (Table 2), notably, but not surprisingly,
antibody-antigen complexes representing a fraction (3.6%) of the
protein set, produce a significant part of all alignments (17.5%, or
,970 alignments per target complex), with FIC alignments for
both monomers in all 12 cases. Interestingly, in two other
functional classes (enzyme-inhibitor and cytokine receptor) the
FIC alignments were observed at least for one monomer in almost
100% of cases as well, with the only exception of 1e44, for which
PSI-BLAST did not find any homologous sequences in PDB. Out
of 11 cases in the ‘other’ functional class, for which no FIC
alignments were found, 8 cases had no statistically significant
alignments. In 3 complexes (1o6s, 1tt5, and 1zm2) the interface
consisted of terminal residues only. Thus the interface coverage
could have been significantly reduced by absence of these terminal
residues in an alignment, which is often the case in local
For further analysis we introduced parameter qmax, the maximal
target sequence coverage in a subgroup of alignments and counted
the number of alignments (all or FIC only) in subgroups
corresponding to q#qmax=40, 50, 60, 70, 80, 90, and 100% (the
entire alignment pool). The results in Figure 1 show that even
when the target sequence coverage does not exceed 40%, there is a
significant number of FIC alignments (191 out of 9,358 alignments
with qmax=40%). Although these FIC alignments constitute ,2%
of alignments with qmax=40%, they are still sufficient for statistical
analysis. The absolute lengths of these alignments range from 32 to
220 residues (for 86 and 631 residue proteins, respectively),
covering from 8 to 40 interfacial residues. The quality of the
alignments is rather poor (the range of the expectation values is
from 2610248to 1.0, the sequence identities vary from 6.5% to
Protein-protein interactions play a central role in life
processes at the molecular level. The structural information
on these interactions is essential for our understanding of
these processes and our ability to design drugs to cure
diseases. Limitations of experimental techniques to deter-
mine the structure of protein-protein complexes leave the
vast majority of these complexes to be determined by
computational modeling. The modeling is also important
for revealing the mechanisms of the complex formation.
The 3D modeling of protein complexes (protein docking)
relies on the structure of the individual proteins for the
prediction of their assembly. Thus the structural accuracy
of the individual proteins, which often are models
themselves, is critical for the docking. For the docking
purposes, the accuracy of the binding sites is obviously
essential, whereas the accuracy of the non-binding regions
is less critical. In our study, we systematically analyze the
accuracy of the binding sites in protein models produced
by high-throughput techniques suitable for large-scale
(e.g., genome-wide) studies. The results indicate that this
accuracy is adequate for the low- to medium-resolution
docking of a significant part of known protein-protein
Accuracy of Protein Binding Sites in Modeling
PLoS Computational Biology | www.ploscompbiol.org2 April 2010 | Volume 6 | Issue 4 | e1000727
39%, and the gaps constitute up to 32% of the alignments). Such
short alignments are generally considered poor in homology
modeling of monomeric proteins. However, they can arguably be
used for accurate modeling of protein-protein interfaces if all
residues of the target interface are present in the alignment. Such
interface modeling would provide accuracy sufficient not only for a
meaningful analysis of binding properties, but also for docking of
3D models of monomers. Such docking is important for large-scale
modeling of protein-protein complexes because modeling based on
homology to co-crystallized protein-protein complexes accounts
for only 15–20% of all known interactions [24,25].
Identity and Similarity of Interface Alignments
It is important to determine if FIC alignments have properties
that distinguish them from the whole pool of alignments. The
knowledge of such properties would help in ‘‘real’’ homology
modeling where interface residues are not known in advance and
only the information related to the alignment properties, such as
alignment expectation value e, and/or alignment identity aidenand
similarity asim(Eq. 3), is available. For this purpose we compared
the distributions of e, aidenand asimfor FIC alignments and for all
alignments with maximum target sequence coverage qmax (see
Figure 2). The results show that e-distributions (data not shown) do
not differ significantly between the FIC alignments and all
alignments, irrespective of qmaxvalues with a weak tendency of
the FIC alignments to have e values lower than those in the whole
pool of alignments. This difference is small and can be hardly used
in practical discrimination of the FIC alignments.
The pattern of distributions of other alignment parameters is
different (Figure 2). Whereas for the alignments with qmax=100%
there is no large difference between the FIC and all alignments
(Figure 2B, D), the FIC alignments with qmax=40% show a
distinguishable difference from all alignments (Figure 2A, C). For
example, the part of the FIC alignments with aidenbetween 15 and
20% (84 out of 191) is two times larger than for all alignments
(2124 out of 9358; Figure 2A). This difference is even more
pronounced for the asimdistributions (Figure 2C), where the part of
alignments with asimbetween 15 and 20% is four times larger for
the FIC alignments (33 out of 191 as opposed to 459 out of 9358
for all alignments). We can hypothesize that this is due to a larger
evolutionary distance between the target and the template proteins
in alignments containing only a small part of the target sequence.
Binding sites tend to be more conserved than the rest of the surface
in evolutionary related proteins . Such proteins usually
correspond to ‘‘good’’ alignments with high target sequence
coverage and alignment identity. This assumption is indirectly
supported by the distributions of all alignments shown in
Figure 2B, D where the fraction of the FIC alignments is larger
at higher values of alignment identities and similarities, whereas at
lower aidenand asimthe situation is opposite.
Figure 3 shows the distributions, similar to those in Figure 2, but
only for the residues that belong to the target binding site (these
residues do not necessary form continuous stretches of the protein
sequence). To avoid ambiguities in definition of interface identity
and similarity (Eq. 4) for the alignments with no or little interface
coverage, only FIC alignments are considered. The distributions of
interface identity iidenand similarity isimqualitatively are similar to
distributions of aidenand asim. The main difference is the positions of
distribution maxima, which are shifted towards smaller values,
compared to corresponding maxima positions in the aidenand asim
distributions. The largest difference is in the iidendistribution for
the short alignments, with the maximum for iidenbetween 5 and
10% as opposed to 15 to 20% for the aiden distribution. The
distributions for the interface residues are also slightly broader
than corresponding distributions for the whole alignments. For
example, the peak in aidenaccounts for ,20% of the alignments
while corresponding peak in the iidendistribution amounts only to
,15% of the alignments. This is consistent with the previous
assumption that alignments with small target sequence coverage
are observed for evolutionary distant proteins where interface
Table 1. Interacting chains with known structure used in
1acbEI 1e96AB 1h2sAB 1kxqAH 1otsAC 1t9gDS 1x3wAB 2ayoAB
1agrAE 1eaiBD 1h4lAD 1kz7AB 1oxbAB 1ta3BA 1x86AB 2b3tBA
1aroPL 1ebdBC 1h59AB 1kzyCA 1oyvAI 1tafAB 1xb2AB 2b59AB
1avaAC 1eerBA 1h6kAX 1l4dAB 1oyvBI 1tdqAB 1xd3AB 2b5iBA
1avgHI 1efnAB 1h9hEI 1l6xAB 1p5vAB 1te1AB 1xdkBA 2b5iCA
1avwAB 1ewyAC 1he1AC 1l7vAC 1p8vAC 1th1AC 1xdtTR 2bcjAQ
1axiBA 1f02IT 1he8AB 1ldjAB 1p9mCB 1th8AB 1xg2AB 2bfxAD
1ay7AB 1f34AB 1hl6BA 1lfdBA 1p9mAB 1tmqAB 1xk4AC 2bh1AX
1b0nAB 1f3vBA 1hx1AB 1lpbBA 1pk1AB 1tnrAR 1xl3AC 2bkhAB
1b34AB 1f5qAB 1i1rAB 1ltxAR 1ppfEI 1tocBR 1xouBA 2bkkAB
1b6cAB 1f60AB 1i2mBA 1m1eAB 1pqzAB 1tt5AB 1xqsAC 2bkrAB
1blxAB 1f6fBA 1i7wAB 1m27AC 1pvhAB 1tueAB 1xtgAB 2bo9AB
1bmlCA 1f6mAC 1i8lAC 1m2vBA 1pxvAC 1tx4AB 1xu1AR 2bseAE
1bndAB 1f93BE 1iarBA 1m9fAD 1qa9AB 1tx6AI 1y4hAC 2btfAP
1buhAB 1fbvAC 1ib1AE 1ma9AB 1qavBA 1txqAB 1y64AB 2c1mAB
1buiAC 1fccAC 1ibrBA 1mbxAC 1qbkBC 1tygAB 1y8xAB 2c5dAC
1bvnPT 1fleEI 1iraYX 1moxAC 1qo3AC 1u0sYA 1ycsAB 2ckhAB
1bzqAL 1fm9AD 1itbBA 1mq8AB 1r0rEI 1u7fAB 1yvbAI 2ey4AE
1c1yAB 1foeAB 1ixsBA 1mvfAE 1r1kAD 1uadAC 1z0jAB 2ey4AC
1c4zAD 1fqjAB 1j2jAB 1mzwAB 1r4aAE 1ueaAB 1z2cBA 2f9dAP
1c9pAB 1fqjCA 1jatAB 1n0wAB 1r8sAE 1ughEI 1z3eAB 2fi4EI
1cd9BA 1fr2BA 1jdhAB 1nexBA 1rp3AB 1ujwAB 1z3gHA 2g45AB
1choEI 1fs1BA 1jiwPI 1nf3AC 1s1qAB 1ukvGY 1z5yED 2gooAC
1clvAI 1fyhAB 1jk9BA 1nmuAB 1s3sBH 1ul1XA 1z92AB 2gy7AB
1cseEI 1g3nAB 1jmaAB 1npeAB 1s4yBA 1us7AB 1zbdAB 2hppHP
1cxzAB 1g3nAC 1jowBA 1nqlAB 1s6vAB 1usuAB 1zbxAB 2mtaCA
1d2zBA 1g4uSR 1jtdAB 1nt2BA 1sbbBC 1uuzAD 1zc3AD 2sniEI
1d3bAB 1g6vAK 1jtgAB 1nunBA 1sgfGB 1uw4BA 1zlhAB 2trcBP
1d4xAG 1g73AC 1jtpAL 1nvuSQ 1sgpEI 1uzxAB 1zm2AB 3fapAB
1d6rAI 1gc1GC 1jw9BD 1nw9BA 1shwAB 1v5iAB 2a19BA 3hhrCA
1devAB 1gcqAC 1k5dAB 1o6sAB 1shyBA 1v74AB 2a41AC 3proAC
1df9AC 1gh6BA 1k8rAB 1o94AC 1shzAC 1vetAB 2a42AB 3sicEI
1dfjEI 1ghqAB 1k90AD 1oc0AB 1sppAB 1vg0AB 2a5dBA 3ygsCP
1dhkAB 1gl0EI 1kacAB 1oeyJA 1sq0AB 1w1iAF 2a5tAB 4htcHI
1dkfBA 1gl1AI 1kg0BC 1ofhAG 1sq2LN 1w98AB 2a5yBA 4sgbEI
1dkgDA 1gl4AB 1kgyAE 1ofuAX 1stfEI 1wmhAB 2a78BA
1dmlAB 1glbFG 1ki1BA 1ohzAB 1sv0AC 1wmiAB 2ajfAE
1dn1AB 1go4AG 1kpsAB 1ol5AB 1svxBA 1wpxAB 2apoAB
1dowAB 1gpwAB 1kshAB 1oo0AB 1syxAB 1wq1RG 2assBA
1ds6AB 1gvnBA 1ktkEA 1ophAB 1t0fAC 1wr6AE 2assBC
1dtdAB 1gxdAC 1ktzBA 1or7AC 1t6bXY 1wrdAB 2auhAB
1e44BA 1gzsAB 1ku6AB 1oryAB 1t6gAC 1wywAB 2aw2AB
First four symbols are the PDB code followed by the IDs of interacting chains as
in the PDB file.
Accuracy of Protein Binding Sites in Modeling
PLoS Computational Biology | www.ploscompbiol.org3April 2010 | Volume 6 | Issue 4 | e1000727
conservation is not evident. It is important to note that there are
significant parts of the alignments with no identity in binding site
residues (,6% for the whole pool of FIC alignments in Figure 3B,
and ,15% for the short FIC alignments in Figure 3A) whereas
there are no alignments with zero alignment identity overall
(Figures 2A, B). This result by itself is not surprising since
alignments with no identical aligned residues have expectation
value so high that they are considered statistically insignificant and
are not included in the PSI-BLAST output. On the other hand,
there are no alignments with zero similarity (no similar residues at
all) for the short alignments (Figure 3C) and almost no such
alignments (,1%) for the whole alignment pool (Figure 3D). This
suggests that even for proteins distant in evolution the interface
conservation may play some role, although at more complex level
than simple amino acid preservation.
Probability to Find All Interface Residues in an Alignment
For practical modeling of protein complexes it is important to
estimate if the interface residues are inside an alignment based on
the alignment properties only. For this purpose we determined the
number of FIC alignments having certain range of alignment
identities/similarities (with a window of 5%) and the number of all
alignments having the same range of identities/similarities values.
The ratio of those two numbers gives a probability to find
all interface residues inside an alignment (or FIC alignment
probability) with given identity/similarity. The calculations
performed for the alignments with qmaxranging from 40% to
100% did not find significant differences in the resulting trends.
For better visualization (lower statistical noise) Figure 4 shows the
FIC alignment probability as a function of alignment identity and
similarity for the whole alignment pool (qmax=100%) only.
Because of representative nature of our dataset of complexes, we
can argue that the observed trends in this dataset will hold in the
general case. Thus, we can assume that for the alignments
with identity .40% (similarity .60%), the probability to find all
interface residues in a given alignment is $80%. This observation
relates to the above suggestion that in the alignments with higher
identity/similarity, proteins are closely evolutionary related. It was
demonstrated in previous studies of ion binding proteins ,
mitochondrial carriers , glycolitic enzymes , cyclic de-
pendent kinases , and other protein families [26,31] that the
binding sites in closely related proteins are more conserved than
the rest of the surface. Thus, the alignment programs (such as PSI-
BLAST used in this study) more reliably identify these highly
conserved regions, increasing chances to have full binding sites
inside an alignment irrespectively of the alignment length. One
can argue that this is a nonessential observation since it is well
Table 2. Number of structures with full interface coverage alignments, NFIC, for different types of complexes.
Total number of
Total number of BLAST
both monomersone monomer none of the monomers
All32966706 218 9912
Enzyme-inhibitor63 9441 42 201
Other22940425 145 73 11
Figure 1. Percentage of alignments with full interface coverage
(FIC alignments) in alignment pool produced by PSI-BLAST on
the representative set of 329 two-chain complexes at various
maximum target sequence coverage qmax.
Figure 2. Comparison of distributions of alignment identities
and similarities between alignments containing all interface
residues and all alignments. The distributions of alignments
containing all interface residues are shown by open bars and those of
all alignments are shown by closed bars. Panels A and C show
distributions for the alignments with maximum query sequence
coverage 40% and panels B and D show the distributions for the
whole alignment pool irrespectively of query sequence coverage.
Accuracy of Protein Binding Sites in Modeling
PLoS Computational Biology | www.ploscompbiol.org4 April 2010 | Volume 6 | Issue 4 | e1000727
established in homology modeling of individual proteins that
model building from the alignment with identity .40% is a trivial
task since the fraction of correctly aligned residues in such
alignments is approaching 100% (e.g., see Fig. 1B in Ref. ).
However, the importance of our finding is that it provides a simple
recipe for evaluating suitability of a particular alignment for
building partial homology model of a protein complex of interest
with good accuracy in the interface region.
Partial Structural Models
As mentioned above, there is a significant amount of alignments
with low target sequence coverage containing all residues
belonging to the interface of the target complex. To assess if such
short alignments are useful for structural modeling of protein
complexes, we built the structural models and estimated their
quality in terms of interface RMSD between the model and the
native structures (see Methods) for all FIC alignments with a
certain maximum target sequence coverage qmax. To avoid
ambiguities caused by possible absence of parts or even all of
the interface residues in partial models, the study is restricted to
FIC alignments and RMSD of the binding sites atoms. Also we
focused on the extreme case of qmax=40%, although modeling was
performed for the alignments with qmax=50% and 60% as well,
with results being qualitatively similar to those for the qmax=40%.
Among the alignments considered, there were no cases for direct
homology modeling where sequences of monomers in the target
complex are aligned with the sequences from a template complex.
The identities of aligned sequence parts in the alignments used to
build the models in all cases were well below 40%, which puts
them in the ‘‘twilight’’ zone of homology modeling of protein
There were 191 FIC alignments with qmax=40% for 26 target
sequences, among which two were from antibody-antigen
complexes, three from enzyme-inhibitor complexes, and the rest
from the ‘‘other’’ functional group. This distribution shows no
overrepresentation of functional groups compared to the entire
dataset. Models were built for all 191 alignments. However, for
further analysis we chose a single model per target sequence, based
on the highest identity of aligned sequence parts (top model). The
results are presented in Table 3. For seven target complexes
(,27%) the top model had interface RMSD,5 A˚, which is in line
with the estimates of the binding site accuracy needed for
meaningful docking predictions . For five complexes, interface
RMSD was between 5 A˚ and 10 A˚, which according to the
estimates of the docking funnel size , can produce near-native
matches. Thus we define them as acceptable accuracy models of
the monomers (not to be confused with the acceptable accuracy
models of the complexes in the CAPRI evaluation http://www.
ebi.ac.uk/msd-srv/capri). The FIC alignments were detected in
50% of the complexes with overall alignments considered
unsuitable for homology modeling of monomeric proteins.
Interestingly, the expectation value of the alignment does not
appear to be an appropriate parameter to assess the quality of the
resulting model, since in all cases the alignment for the best model
did not have the lowest e-value among FIC alignments, although
the lowest e-value observed for the top models alignments was
10247(1gxd, chain A). For 17 target sequences, the top model was
found to be also the best model, i.e. model with the lowest interface
RMSD. Among 9 cases with different top and best models, only in
two cases interface RMSD values were significantly different (the
top and the best models in different quality categories; data shown
in Table 3 in bold).
The data in Table 3 indicate that all FIC alignments for the top
models have low sequence and interface identity/similarity, which
suggests that target and template proteins in those alignments are
evolutionary remote (see discussion in previous sections). Thus, it is
interesting to analyze whether there is a preference of target and
template proteins in alignments to be from the same organism or
from different species. Our analysis suggests no such preference
since for good and acceptable models there were 6 target-template
pairs from the same organism and 9 pairs from different organisms
(corresponding numbers for the wrong models are 5 and 8). This
does not support a conclusion from an earlier study  that
protein-protein interactions are more conserved within one species
than across the species. However a statistical analysis on a much
larger pool of data is needed to reach a more definite assessment
(work currently in progress).
Figure 5 shows examples of the models, including those for
which the target and the template sequences are from the same
and from different organisms. One interesting similarity in both
cases (Figures 5A and 5B) is that the target proteins have two
clearly distinguishable domains and the model structure covers a
significant portion of one of the domains, which exclusively
Figure 3. Distributions of interface identities and similarities in
alignments containing all interface residues. Panels A and C show
the distributions for the alignments with maximum query sequence
coverage 40% and panels B and D show the distributions for the
alignments irrespectively of query sequence coverage. For the
definitions of interface identity and similarity see text.
Figure 4. Probability of finding all interface residues inside an
alignment as a function of alignment identity and similarity.
Curves are least-square polynomial fits to the data points obtained from
the analysis of PSI-BLAST alignments for the representative set of 329
complexes used in the study.
Accuracy of Protein Binding Sites in Modeling
PLoS Computational Biology | www.ploscompbiol.org5 April 2010 | Volume 6 | Issue 4 | e1000727
Table 3. Parameters of the top models produced on the basis of alignments with maximum 40% target sequence coverage and full interface coverage.
qdom, % (6)
Interface RMSD, A˚(9)
Detection of light
Ca, Zn binding
Ca, Zn binding
H ion transport
Mn ion binding
HIV virus I (V)
Mn ion binding
Ca ion binding
DNA damage repair
(1)First four symbols are the PDB code followed by ID of the chain as in the PDB file. Asterisk indicates that protein is a monomer in the PDB file.
(2)As provided in PDB file. Letters in parenthesis stand for higher levels of taxonomy classification (V: viruses; A: archaea; B: bacteria; F: fungi; P: plants; M: mammals).
(3)Extracted from PDB GO terms section.
(4)Logarithm of alignment expectation value (e-value).
(5)Entire target sequence coverage in the alignment of the model, as defined by equation (1).
(6)Coverage of the target binding domain (for multi-domain structures) in the alignment of the model.
(7)As defined by Eq. 3.
(8)As defined by Eq. 4.
(9)RMSD between Caatoms of the interface residues in the model and the native structure.
For some targets the parameters of the model with the smallest interface RMSD are shown if the best and the top models have substantially different interface RMSD values (in bold).
Accuracy of Protein Binding Sites in Modeling
PLoS Computational Biology | www.ploscompbiol.org6April 2010 | Volume 6 | Issue 4 | e1000727
participates in the interaction with the other monomer (not shown
for clarity). In fact, this feature is common to all good-accuracy
models (interface RMSD,5 A˚). The data on the binding domain
coverage is provided in Table 3 (where applicable). It shows that
there is no clear correlation between the binding domain coverage
(although it is higher than the entire sequence coverage) and the
model quality. Acceptable accuracy models are built for the single
domain proteins as well. Figure 5C shows an example of such
model. The implication for practical modeling is that if the target
protein is predicted to have a domain structure, then it is likely that
the accuracy of the homology models produced on the basis of the
‘‘bad’’ alignments will be sufficient to perform a meaningful
template-free docking. On the other hand, for homology models of
single-domain proteins, methods less sensitive to structural
inaccuracies (e.g., structural alignment) should be used. This
assessment is supported by a comprehensive study of the template
free docking ability to tolerate structural inaccuracies , which
showed that low-resolution structural features of protein–protein
interactions can be determined for a significant percentage of
complexes of highly inaccurate protein models (typically up to 6 A˚
RMSD from the native structure of the monomer). The results
were further supported by recent studies of antibody-antigen
docking of homology models, which concluded that the homology
models yield medium-to-high quality of docking predictions .
Further confirmation came in the recent study by Aloy et al. 
on the structural modeling of yeast interactome where it was found
that the use of homology models in docking does not lead to a
critical loss of accuracy (assessed by extrapolation of docking
results for the unbound X-ray structures).
Our preliminary results on the benchmarkingof the template free
docking of the modeled structures was performed using GRAMM
procedure, according to the goal of this study in the high-
throughput fashion that does not involve computationally expensive
scoring and structural refinement. The low-resolution criterion for
success was: a match with the ligand interface RMSD,8 A˚in the
top 100 predictions. This RMSD value corresponds to the
characteristic size of the binding funnel . Such low-resolution
predictions from the coarse-grained global scan are located within
the binding funnel and can be further locally refined within the
funnel. Higher-resolution docking, and the corresponding more
strict success criteria (such as those used in CAPRI), in addition to
longer computational times, require higher, non-high-throughput
accuracy of the binding site modeling, which is outside the scope of
this study. The current study is aimed at the models of poor
quality that still preserve the acceptable accuracy of the
binding site. According to the above criterion, the success rate for
the modeled proteins dropped to 23% from the similarly obtained
43% for the unbound X-ray proteins. However, such success rate is
significant for the genome-wide studies. A systematic assessment of
docking application to modeled structures of different accuracy is
currently in progress.
Table 3 also includes data on the failed modeling (interface
RMSD.10 A˚). Figure 6D shows an example of such model. The
target native structure has the domain structure similar to the
successful models described above. The main reason for the
incorrect modeling of the interface region is presence of a long
stretch of gaps on the template side in the alignment. This is the
reason for the incorrect loop (indicated by arrow in Figure 5D),
Figure 5. Examples of partial homology models. The models (white ribbons) are superimposed on the target native structures (gray ribbons).
(A) Good accuracy model (interface RMSD=5.0 A˚) in the case of target and template proteins from the same organism. Target is malaria transmission
blocking antibody 2A8 from mouse, (1z3g, chain H) and template is mouse BM3.3 T-cell receptor a-chain (1fo0, chain A). (B) Good accuracy model
(interface RMSD=3.7 A˚) in the case of target and template proteins from different organisms. Target is guanine nucleotide-binding protein alpa-1
subunit from bovine, (1fqj, chain A) and template is yeast RAS-related protein RAB-33 (2g77, chain B). (C) Acceptable accuracy model (interface
RMSD=8.6 A˚). Target is fibrillarin-like preRRNA processing protein from Archaeoglobus fulgidus (1nt2, chain A) and template is UDP-N-
acetylglucosamine 4-epimerase from Pseudomonas aeruginosa (1sb8, chain A). (D) Incorrect model (interface RMSD=16.9 A˚). Target is human MHC
Class II receptor HLA-DR1 (1kg0, chain B) and template is intron-encoded endonuclease from Desulfurococcus mobilis (1b24, chain A). Arrow indicates
an incorrect loop which is the cause for large interface RMSD in this model. Blue and yellow meshes indicate positions of the backbone atoms of the
interface residues in the model and the native structures, respectively. Other parameters of the models are presented in Table 3.
Accuracy of Protein Binding Sites in Modeling
PLoS Computational Biology | www.ploscompbiol.org7 April 2010 | Volume 6 | Issue 4 | e1000727
modeled without a template in the vicinity of the interface, which
resulted in position shift of the interface residues in the model
compared to the native structure (yellow and blue meshes in
Figure 5D). Another typical reason for large interface RMSD is the
native structure interface having no secondary structure elements
(e.g., a loop in enzyme-inhibitor complexes), but the fragment is
large difference between quaternary structures of the native target
and the template structures also may lead to large shift of interface
residues in the model, even if these residues belong to the same
secondary structure elements as in the native structure.
Analysis of organism and functional annotations (Table 3)
revealed that both target and template proteins are from the
species spanning the entire universe of life - viruses, archaea,
bacteria, lower (fungi) and higher (plants and mammals)
eukaryotes - and participate in a broad range of biochemical
processes. Moreover, there is no clear correlation between source
organisms of the target-template pair or the biochemical pathways
in which they participate. There are correct models with the target
and the template from evolutionary distant organisms (e.g.,
mammals and archaea), as well as incorrect models with the
target and the template from evolutionary close organisms or even
the same organism. Similarly, no such correlation was found for
the functions of the target and the template proteins, although the
functional assignment has limited reliability. This suggests that the
current ability to model complexes may not be restricted to certain
species and/or functions. However, statistical analysis of a much
larger protein interactions dataset, when it becomes available,
would be necessary to draw more definite conclusions.
For systematic evaluation of potential accuracy in high-
throughput modeling of binding sites, local sequence alignments
were performed in a representative set of protein-protein
complexes. The results indicate that for the majority (97%) of
the target sequences there is at least one alignment containing all
residues belonging to the interface of the target complex (FIC
alignments). Significant number of the FIC alignments was
observed even when only ,40% of the target sequence is aligned
against the template. The results suggest a simple graphical
function for evaluating the probability of finding all interface
residues inside a local alignment when only the alignment
information is known.
Homology models of the interfaces in target monomers were
built based on the FIC alignments with query target sequence
coverage ,40%. A simple scheme of model ranking based on the
alignment identity showed that in ,50% of cases the structural
models have accuracy high enough for protein docking. Align-
ments that contain only a small portion of the target sequence and
have low sequence identity are usually considered poor in
modeling of individual proteins. They are used primarily in
elaborate and computationally expensive techniques hardly
applicable on genome-wide scale. Our results suggest that for
the genome-wide structural modeling of protein interactions,
simpler and less computationally expensive techniques based on
the use of single, local sequence alignment, may yield satisfactory
results, given that the interface residues are reliably identified in
the alignment. Current methods for predicting protein-protein
binding sites based on sequence information alone have limited
accuracy (e.g. Refs. [37,38]). However, because of the on-going
significant community efforts in this direction, one may expect
emergence of more accurate methods in the near future.
A straightforward template-based modeling of protein com-
plexes is possible on the basis of a co-crystallized template
complex. However, previous studies [24,25] demonstrated that
this technique could account only for ,15–20% of all known
interactions, whereas the rest of the protein complexes have to be
modeled by other techniques. One possible direction is indepen-
dent modeling of individual monomers on different templates with
further application of docking (either template free or based on
structure alignment) to these models. Earlier studies (e.g. Refs
[19,35,39] and others), as well as the results of this work suggested
feasibility of this scenario. However more systematic and
comprehensive studies are needed for quantitative guidelines of
applicability of the homology models in large-scale structural
modeling of protein-protein interactions (study currently in the
Set of Proteins
Hetero-complexes with known 3D structures available in PDB
were used in the study. To avoid bias caused by overrepresentation
of certain protein families in PDB, we used the representative set of
protein complexes from the DOCKGROUND resource , manually
selected and purged at 30% sequence identity level. Out of 523
complexes in the dataset, we further excluded structures with
multi-chain interactions and those with large structural defects in
the vicinity of the interface, which allowed us to avoid ambiguities
in determining binding site residues. The final set consisted of 329
two-chain non-obligate complexes shown in Table 1 (63 enzyme-
inhibitor, 12 antibody-antigen, 25 cytokine receptors, and 229
other complexes). This set is based on all protein structures
available in PDB; thus the results are not dataset-dependent.
For 658 sequences in the dataset, the search for sequence
homologues was performed by PSI-BLAST  implemented in
the program BLASTPGP. To broaden the pool of potential
templates, the maximum number of hits was set to 2000, with all
other parameters set to default values. To obtain the checkpoint
file (the position specific scoring matrix PSSM) , the search
was performed against all sequences in the non-redundant
database of sequences (www.ncbi.nlm.nih.gov) with the substitu-
tion matrix BLOSUM62  with five iterations. The checkpoint
file was used in sequential PSI-BLAST run against all non-
redundant sequences in PDB.
The 3D models from the PSI-BLAST sequence alignments were
built by program NEST from the JACKAL package developed in
Honig’s lab  using default parameters. Large errors in some
template files were repaired by the program PROFIX from the
same package. The NEST program was chosen over other
popular modeling programs because it yields reliable models fast
enough to be used in large-scale calculations (e.g., according to
benchmarking of various homology modeling programs ) and
can be easily incorporated into automatic scripts for generating
and updating databases of structural models currently under
development in the lab.
Analysis of Results
Since sequence alignments produced by PSI-BLAST are local
by design , not all residues of the target sequence are present
in the alignment. Thus for the analysis of the alignments we
defined the target sequence coverage
Accuracy of Protein Binding Sites in Modeling
PLoS Computational Biology | www.ploscompbiol.org8 April 2010 | Volume 6 | Issue 4 | e1000727
and, similarly, the interface coverage
Where Naliand Ninter
the target interface residues, respectively, in the alignment; Ntot
are the total numbers of all residues and the interface
residues, correspondingly, in the entire target sequence. We did
not analyze whether the template is multi- or monomeric
(although the data is available in Table 3) since our goal was to
determine the general usefulness of short sequence alignments in
binding site modeling, rather than traditional homology modeling
of protein complexes where both target and template are
multimers. When the target had the multi-domain structure, we
also calculated the domain coverage qdomusing formula (1), where
Naliis the total number of the target residues inside the binding
The alignments were further analyzed with respect to the
alignment e-value as well as their identity and similarity, defined as
are the numbers of all target residues and
where Laliis the length of the alignment (number of target residues
in an alignment plus gaps in the aligned target sequence), Nidenis
the number of aligned identical residue pairs, and Npos is the
number of aligned residues pairs for which substitution matrix
displays a positive number (evolutionary favorable substitutions).
Similarly, the identity and similarity of the interface residues inside
an alignment was defined as
(positive) residue pairs where the residue on the target side belongs
to the target complex interface, and Ninter
the interface target residues in the alignment. To evaluate the
quality of the resulting homology model, we calculated the root-
mean square distance between Caatoms of the interface residues
(interface RMSD), with the native structure of the monomer and
its model superimposed by the program TM-align . This
measure is different from the RMSD used in the CAPRI
evaluation , where it is calculated between the interface atoms
of the ligand in the native and in the docked matches, after
structural superimposition of the receptors. Other widely used
modeling quality criteria, such as sensitivity and specificity, are not
applicable to our study because they involve true and false-
positive/negative predictions that can be defined either for binary
predictions of the fact of protein interactions (which is not the case
in our study) or in the case of full modeled complex structure with
both monomers present.
pos) are the number of aligned identical
is the total number of
Conceived and designed the experiments: PJK IAV. Performed the
experiments: PJK. Analyzed the data: PJK IAV. Wrote the paper: PJK.
1. Sali A (2001) Target practice. Nature Struct Biol 8: 482–484.
2. Friedberg I, Jaroszewski L, Ye Y, Godzik A (2004) The interplay of fold
recognition and experimental structure determination in structural genomics.
Curr Opin Struct Biol 14: 307–312.
3. Moult J, Fidelis K, Kryshtafovych A, Rost B, Hubbard T, et al. (2007) Critical
assessment of methods of protein structure prediction—Round VII. Proteins 69
(Suppl 8): 3–9.
4. Vakser IA, Kundrotas P (2008) Predicting 3D structures of protein-protein
complexes. Curr Pharm Biotech 9: 57–66.
5. Lensink MF, Mendez R, Wodak SJ (2007) Docking and scoring protein
complexes: CAPRI 3rd Edition. Proteins 69: 704–718.
6. Janin J (2007) Structural Genomics: Winning the second half of the game.
Structure 15: 1347–1349.
7. Aloy P, Russell RB (2006) Structural systems biology: Modelling protein
interactions. Nature Rev Mol Cell Biol 7: 188–197.
8. Russell RB, Alber F, Aloy P, Davis FP, Korkin D, et al. (2004) A structural
perspective on protein–protein interactions. Curr Opin Struct Biol 14: 313–324.
9. Aloy P, Quero E, Aviles FX, Sternberg MJE (2001) Automated structure-based
prediction of functional sites in proteins: Applications to assessing the validity of
inheriting protein function from homology in genome annotation and to protein
docking. J Mol Biol 311: 395–408.
10. Devos D, Valencia A (2000) Practical limits of function prediction. Proteins 41:
11. Han JH, Kerrison N, Chothia C, Teichmann SA (2006) Divergence of
interdomain geometry in two-domain proteins. Structure 14: 935–945.
12. Russell RB, Sasieni PD, Sternberg MJE (1998) Supersites within superfolds.
Binding site similarity in the absence of homology. J Mol Biol 282: 903–918.
13. Aloy P, Ceulemans H, Stark A, Russell RB (2003) The relationship between
sequence and interaction divergence in proteins. J Mol Biol 332: 989–998.
14. Brylinski M, Skolnick J (2008) A threading-based method (FINDSITE) for ligand
binding site prediction and functional annotation. Proc Natl Acad Sci USA 105:
15. Binkowski TA, Joachimiak A, Liang J (2005) Protein surface analysis for function
annotation in high-throughput structural genomics pipeline. Protein Sci 14:
16. Lijnzaad P, Argos P (1997) Hydrophobic patches on protein subunit interfaces:
Charactersitics and prediction. Proteins 28: 333–343.
17. Alber F, Dokudovskaya S, Veenhoff LM, Zhang W, Kipper J, et al. (2007)
Determining the architectures of macromolecular assemblies. Nature 450:
18. Vakser IA, Matar OG, Lam CF (1999) A systematic study of low-resolution
recognition in protein-protein complexes. Proc Natl Acad Sci USA 96:
19. Tovchigrechko A, Wells CA, Vakser IA (2002) Docking of protein models.
Protein Sci 11: 1888–1896.
20. Brylinski M, Skolnick J (2008) Q-Dock: Low-resolution flexible ligand docking
with pocket-specific threading restraints. J Comput Chem 29: 1574–1588.
21. Gao Y, Douguet D, Tovchigrechko A, Vakser IA (2007) DOCKGROUND
system of databases for protein recognition studies: Unbound structures for
docking. Proteins 69: 845–851.
22. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, et al. (1997) Gapped
BLAST and PSI-BLAST: A new generation of database programs. Nucleic
Acids Res 25: 3389–3402.
23. Petrey D, Xiang ZX, Tang CL, Xie L, Gimpelev M, et al. (2003) Using multiple
structure alignments, fast model building, and energetic analysis in fold
recognition and homology modeling. Proteins 53: 430–435.
24. Kundrotas PJ, Lensink MF, Alexov E (2008) Homology-based modeling of 3D
structures of protein-protein complexes using alignments of modified sequence
profiles. Int J Biol Macromol 43: 198–208.
25. Kundrotas PJ, Zhu Z, Vakser IA (2009) GWIDD: Genome-Wide Protein
Docking Database. Nucl Acid Res;doi:10.1093/nar/gkp1944.
26. Keskin O, Nussinov R (2007) Similar binding sites and different partners:
Implications to shared proteins in cellular pathways. Structure 15: 341–354.
27. Pils B, Copley RR, Schultz J (2005) Variation in structural location and amino
acid conservation of functional sites in protein domain families. BMC
Bioinformatics 6: 210.
28. Kunji ERS, Robinson AJ (2006) The conserved substrate binding site of
mitochondrial carriers. Biochim Biophys Acta Bioenergetics 1757: 1237–1248.
29. Forlemu NY, Waingeh VF, Ouporov IV, Lowe SL, Thomasson KA (2007)
Theoretical study of interactions between muscle aldolase and F-actin: Insight
into different species. Biopolymers 85: 60–71.
30. Bartova I, Koca J, Otyepka M (2008) Functional flexibility of human cyclin-
dependent kinase-2 and its evolutionary conservation. Protein Sci 17: 22–33.
31. Ma B, Elkayam T, Wolfson H, Nussinov R (2003) Protein-protein interactions:
Structurally conserved residues distinguish between binding sites and exposed
protein surfaces. Proc Natl Acad Sci USA 100: 5772–5777.
32. Vitkup D, Melamud E, Moult J, Sander C (2001) Completeness in structural
genomics. Nature Struct Biol 8: 559–566.
33. Hunjan J, Tovchigrechko A, Gao Y, Vakser IA (2008) The size of the
intermolecular energy funnel in protein-protein interactions. Proteins 72: 344–352.
Accuracy of Protein Binding Sites in Modeling
PLoS Computational Biology | www.ploscompbiol.org9 April 2010 | Volume 6 | Issue 4 | e1000727
34. Mika S, Rost B (2006) Protein-protein interactions more conserved within
species than across species. PLoS Comp Biol 2: 698–709.
35. Sivasubramanian A, Sircar A, Chaudhury S, Gray JJ (2009) Toward high-
resolution homology modeling of antibody F-v regions and application to
antibody-antigen docking. Proteins 74: 497–514.
36. Mosca R, Pons C, Fernandez-Recio J, Aloy P (2009) Pushing structural
information into the yeast interactome by high-throughput protein docking
experiments. PLoS Comp Biol 5: e1000490.
37. Kundrotas P, Alexov E (2007) Predicting interacting and interfacial residues
using continuous sequence segments. Int J Biol Macromol 41: 615–623.
38. Sikic M, Tomic S, Vlahovicek K (2009) Prediction of protein-protein interaction
sites in sequences and 3D structures by random forests. PLoS Comp Biol 5:
39. Mosca R, Pons C, FernA˜¡ndez-Recio J, Aloy P (2009) Pushing Structural
Information into the Yeast Interactome by High-Throughput Protein Docking
Experiments. Plos Computational Biology 5: e1000490.
40. Henikoff S, Henikoff J (1993) Performance evaluation of amino acid substitution
matrices. Proteins 17: 49–61.
41. Wallner B, Elofsson A (2005) All are not equal: A benchmark of different
homology modeling programs. Protein Sci 14: 1315–1327.
42. Zhang Y, Skolnick J (2005) TM-align: A protein structure alignment algorithm
based on the TM-score. Nucl Acid Res 33: 2303–2309.
Accuracy of Protein Binding Sites in Modeling
PLoS Computational Biology | www.ploscompbiol.org10 April 2010 | Volume 6 | Issue 4 | e1000727