Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure

Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America.
PLoS Computational Biology (Impact Factor: 4.62). 12/2009; 5(12):e1000585. DOI: 10.1371/journal.pcbi.1000585
Source: PubMed


Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (


Available from: Roman Aleksander Laskowski, Mar 11, 2014
Predicting Protein Ligand Binding Sites by Combining
Evolutionary Sequence Conservation and 3D Structure
John A. Capra
, Roman A. Laskowski
, Janet M. Thornton
, Mona Singh
*, Thomas A. Funkhouser
1 Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America, 2 Lewis-Sigler Institute for Integrative Genomics, Princeton
University, Princeton, New Jersey, United States of America, 3 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
Identifying a protein’s functional sites is an important step towards characterizing its molecular function. Numerous
structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small
molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-
based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain
protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand
binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct
comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely
complementary information, which can be combined to improve upon either approach alone. We also demonstrate that
ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms
and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our
understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of
proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.
Citation: Capra JA, Laskowski RA, Thornton JM, Singh M, Funkhouser TA (2009) Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence
Conservation and 3D Structure. PLoS Comput Biol 5(12): e1000585. doi:10.1371/journal.pcbi.1000585
Editor: Thomas Lengauer, Max-Planck-Institut fu
r Informatik, Germany
Received May 11, 2009; Accepted October 30, 2009; Published December 4, 2009
Copyright: ß 2009 Capra et al. 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: JAC has been supported by the Quantitative and Computational Biology Program NIH grant T32 HG003284. MS thanks the NSF for grant PECASE MCB-
0093399, and the NIH for grant GM076275. MS and TAF thank the NSF for grant IIS-0612231. This research has also been supported by the NIH Center of
Excellence grant P50 GM071508 and NIH grant CA041086. TAF also thanks the Leverhulme Trust and the BBSRC for funding his sabbatical at EBI. 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: (MS); (TAF)
Proteins’ functions are determined to a large degree by their
interactions with other molecules. Identifying which residues
participate in these interactions is an important component
of functionally characterizing a protein. Many computational
approaches based on analysis of protein sequences or structures
have been developed to predict a variety of protein functional sites,
including ligand binding sites [1–3], DNA-binding sites [4], catalytic
sites [2,5], protein-protein interaction interfaces (PPIs) [6,7] and
specificity determining positions [8–12]. In this paper, we focus on
the task of predicting small molecule binding sites from protein
sequences and structures. In addition to aiding in the functional
characterization of proteins, knowledge of these binding sites can
guide the design of inhibitors and antagonists and provide a scaffold
for targeted mutations. Over the past 15 years, a large number of
methods for predicting small molecule binding sites have been
developed. Structural approaches have used geometric and
energetic criteria to find concave regions on the protein surface
that likely bind ligands [1,13–21]. Sequence-based approaches, on
the other hand, have largely exploited sequence conservation, or the
tendency of functionally or structurally important sites to accept
fewer mutations relative to the rest of the protein [22].
We introduce ConCavity, a new approach for predicting 3D
ligand binding pockets and individual ligand binding residues. The
ConCavity algorithm directly integrates evolutionary sequence
conservation estimates with structure-based surface pocket pre-
diction in a modular three step pipeline. In the first step, we score
a grid of points surrounding the protein surface by combining
the output of a structure-based pocket finding algorithm (e.g.,
Ligsite [16], Surfnet [14], or PocketFinder [23]) with the sequence
conservation values of nearby residues. In the second step, we
extract coherent pockets from the grid using 3D shape analysis
algorithms to ensure that the predicted pockets have biologically
reasonable shapes and volumes. In the final step, we map from the
predicted pockets to nearby residues by assigning high scores to
residues near high scoring pocket grid points. Using this pipeline,
ConCavity is able to make predictions of both regions in space that
are likely to contain ligand atoms as well as protein residues likely
to contact bound ligands.
We demonstrate ConCavity’s excellent performance via extensive
testing and analysis. First, we show that ConCavity, by integrating
conservation and structure, provides significant improvement in
identifying ligand binding pockets and residues over approaches
that use either conservation alone or structure alone; this testing is
performed on the diverse, non-redundant LigASite database of
biologically relevant binding sites [24]. We find that ConCavity’s top
predicted residue is in contact with a ligand nearly 80% of the
time, while the top prediction of the tested structure-alone and
conservation-alone methods is correct in 67% and 57% of proteins
PLoS Computational Biology | 1 December 2009 | Volume 5 | Issue 12 | e1000585
Page 1
respectively. The notable improvement of ConCavity over the
conservation-alone approach demonstrates that there is significant
added benefit to considering structural information when it is
available. Second, we demonstrate that ConCavity significantly
outperforms current publicly available methods [1,19,25] that
identify ligand binding sites based on pocket finding. Third, we
show that ConCavity performs similarly when using a variety of
pocket detection algorithms [14,16,23] or sequence conservation
measures [2,26]. Fourth, we characterize ConCavity in a range of
situations, and compare its performance in identifying ligand
binding sites from apo vs. holo structures as well as in enzymes vs.
non-enzymes. Fifth, we test how well ConCavity can identify
catalytic sites and drug binding sites. Sixth, we examine
problematic cases for our approaches, and highlight the difficulty
that multi-chain proteins pose for structure-based methods for
identifying ligand binding sites. Finally, we demonstrate that our
methodological improvements in pocket extraction and residue
mapping give our implementations of existing methods a
significant gain in performance over the previous versions. In
fact, without these improvements, the previous structural ap-
proaches do not outperform a simple sequence conservation
approach when identifying ligand binding residues. Overall,
ConCavity significantly advances the state-of-the-art in uncovering
ligand binding sites. Our detailed analysis reveals much about the
relationship between sequence conservation, structure, and
function, and shows that sequence conservation and structure-
based attributes provide complementary information about
functional importance.
Further related work
Sequence-based functional site prediction has been dominated
by the search for residue positions that show evidence of
evolutionary constraint. Amino acid conservation in the columns
of a multiple sequence alignment of homologs is the most common
source of such estimates (see [22] for a review). Recent approaches
that compare alignment column amino acid distributions to a
background amino acid distribution outperform many existing
conservation measures [2,27]. However, the success of conserva-
tion-based prediction varies based on the type of functional residue
sought; sequence conservation has been shown to be strongly
correlated with ligand binding and catalytic sites, but less so with
residues in protein-protein interfaces (PPIs) [2]. A variety of
techniques have been used to incorporate phylogenetic informa-
tion into sequence-based functional site prediction, e.g., traversing
phylogenetic trees [28,29], statistical rate inference [26], analysis
of functional subfamilies [9,12], and phylogenetic motifs [30].
Recently, evolutionary conservation has been combined with
other properties predicted from sequence, e.g., secondary structure
and relative solvent accessibility, to identify functional sites [31].
Structure-based methods for functional site prediction seek to
identify protein surface regions favorable for interactions. Ligand
binding pockets and residues have been a major focus of these
methods [1,13–21]. Ligsite [16] and Surfnet [14] identify pockets by
seeking points near the protein surface that are surrounded in most
directions by the protein. CASTp [17,19] applies alpha shape
theory from computational geometry to detect and measure
cavities. In contrast to these geometric approaches, other methods
use models of energetics to identify potential binding sites
[23,25,32–34]. Recent algorithms have focused on van der Waals
energetics to create grid potential maps around the surface of the
protein. PocketFinder [23] uses an aliphatic carbon as the probe, and
Q-SiteFinder [25] uses a methyl group. Our work builds upon
geometry and energetics based approaches to ligand binding
pocket prediction, but it should be noted that there are other
structure-based approaches that do no fit in these categories (e.g.,
Theoretical Microscopic Titration Curves (THEMATICS) [35],
binding site similarity [36], phage display libraries [37], and
residue interaction graphs [38]). In contrast to sequence-based
predictions, structure-based methods often can make predictions
both at the level of residues and regions in space that are likely to
contain ligands.
Several previous binding site prediction algorithms have
considered both sequence and structure. ConSurf [39] provides
a visualization of sequence conservation values on the surface of a
protein structure, and the recent PatchFinder [40] method
automates the prediction of functional surface patches from
ConSurf. Spatially clustered residues with high Evolutionary
Trace values were found to overlap with functional sites [41], and
Panchenko et al. [42] found that averaging sequence conservation
across spatially clustered positions provides improvement in
functional site identification in certain settings. Several groups
have attempted to identify and separate structural and functional
constraints on residues [43,44]. Wang et al. [45] perform logistic
regression on three sequence-based properties and predict
functional sites by estimating the effect on structural stability of
mutations at each position. Though these approaches make use of
protein structures, they do not explicitly consider the surface
geometry of the protein in prediction. Geometric, chemical, and
evolutionary criteria have been used together to define motifs that
represent known binding sites for use in protein function
prediction [46]. Machine learning algorithms have been applied
to features based on sequence and structure [47,48] to predict
catalytic sites [5,49–51] and recently to predict drug targets [52]
and a limited set of ligand and ion binding sites [53–55]. Sequence
conservation has been found to be a dominant predictor in these
Most similar to ConCavity are two recent approaches to ligand
binding site identification that have used evolutionary conserva-
tion in a post-processing step to rerank [1] or refine [56] geometry
based pocket predictions. In contrast, ConCavity integrates
conservation directly into the search for pockets. This allows it
to identify pockets that are not found when considering structure
alone, and enables straightforward analysis of the relationship
Author Summary
Protein molecules are ubiquitous in the cell; they perform
thousands of functions crucial for life. Proteins accomplish
nearly all of these functions by interacting with other
molecules. These interactions are mediated by specific
amino acid positions in the proteins. Knowledge of these
‘‘functional sites’’ is crucial for understanding the molec-
ular mechanisms by which proteins carry out their
functions; however, functional sites have not been
identified in the vast majority of proteins. Here, we present
ConCavity, a computational method that predicts small
molecule binding sites in proteins by combining analysis
of evolutionary sequence conservation and protein 3D
structure. ConCavity provides significant improvement
over previous approaches, especially on large, multi-chain
proteins. In contrast to earlier methods which only predict
entire binding sites, ConCavity makes specific predictions
of positions in space that are likely to overlap ligand atoms
and of residues that are likely to contact bound ligands.
These predictions can be used to aid computational
function prediction, to guide experimental protein analy-
sis, and to focus computationally intensive techniques
used in drug discovery.
Ligand Binding Site Prediction with ConCavity
PLoS Computational Biology | 2 December 2009 | Volume 5 | Issue 12 | e1000585
Page 2
between sequence conservation, structural patterns, and functional
For simplicity of exposition, we begin by comparing ConCavity’s
performance to a representative structural method and a
representative conservation method. We use Ligsite
as the
representative structure-based method, and refer to it as
‘‘Structure’’. Ligsite
is our implementation (as indicated by
superscript ‘‘+’’) of a popular geometry based surface pocket
identification algorithm. We demonstrate in the Methods section
that Ligsite
provides a fair representation of these methods. We
choose Jensen-Shannon divergence (JSD) to represent conservation
methods and refer to it as ‘‘Conservation.’’ JSD has been previously
shown to provide state-of-the-art performance in identifying
catalytic sites and ligand binding sites [2]. We have developed
three versions of ConCavity that integrate evolutionary conservation
into different surface pocket prediction algorithms (Ligsite [16],
Surfnet [14], or PocketFinder [23]). When the underlying algorithm is
relevant, we refer to these versions as ConCavity
, ConCavity
, and
. However, for simplicity, we will use ConCavity
representative of these approaches and call it ‘‘ConCavity.’’
ConCavity and Structure produce predictions of ligand binding
pockets and residues. The pocket predictions are given as non-zero
values on a regular 3D grid that surrounds the protein; the score
associated with each grid point represents an estimated likelihood
that it overlaps a bound ligand atom. Similarly, each residue in the
protein sequence is assigned a score that represents its likelihood of
contacting a bound ligand. Conservation only makes residue-level
predictions, because it does not consider protein structure. All
methods are evaluated on 332 proteins from the non-redundant
LigASite 7.0 dataset [24]. To evaluate pocket identification
performance, we predict ligand locations on the the holo version
of the dataset, in order to use the bound ligands’ locations as
positives. When evaluating residue predictions, we predict ligand
binding residues on the apo structures, and the residues annotated
as ligand binding (as derived from the holo structures) are used as
We quantify the overall performance of each method’s
predictions in two ways. First, for both pocket and residue
prediction, we generate precision-recall (PR) curves that reflect
the ability of each method’s grid and residue scores to identify ligand
atoms and ligand binding residues, respectively. (Just as residues are
assigned a range of ligand binding scores, grid points in predicted
pockets get a range of scores, since there may be more evidence that
a ligand is bound in one part of a pocket than another.) Second, for
each set of predicted pockets (corresponding to groups of non-zero
values in the 3D grid), we consider how well they overlap known
ligands via the Jaccard coefficient. The Jaccard coefficient captures
the tradeoff between precision and recall by taking the ratio of the
intersection of the predicted pocket and the actual ligand over their
union. The Jaccard coefficient ranges between zero and one, and a
high value implies that the prediction covers the ligand well and has
a similar volume. We assess the significance of the difference in
performance of methods on the dataset with respect to a given
statistic via the Wilcoxon rank-sum test.
Integrating evolutionary sequence conservation and
structure-based pocket finding to predict ligand-binding
sites improves on either approach alone
Figure 1 compares ConCavity with its constituent structure and
conservation based components. Figure 1A shows that, within
Figure 1. Ligand binding site prediction performance. (A) PR curves for prediction of the spatial location of biologically relevant bound
ligands. (B) PR curves for ligand binding residue prediction. Our ConCavity algorithm, which combines sequence conservation with structure-based
predictors, significantly outperforms either of the constituent methods at both tasks. Prediction based on structural information alone outperforms
considering sequence conservation alone. Comparing (A) and (B), we see that accurately predicting the location of all ligand atoms is harder for the
methods than finding all the contacting residues. Random gives the expected performance of a method that randomly ranks grid points and residues.
Conservation could not be included in (A), because it only predicts at the residue level. The curves are based on binding sites in 332 proteins from the
non-redundant LigASite 7.0 dataset.
Ligand Binding Site Prediction with ConCavity
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Page 3
predicted pockets, grid points with higher scores are more likely to
overlap the ligand, and that the significant improvement of
ConCavity over Structure (p,2.2e216) exists across the range of
score thresholds. Figure 1B demonstrates that the superior
performance of ConCavity holds when predicting ligand binding
residues as well (p = 6.80e213). ConCavity’s ability to identify
ligand binding residues is striking: across this diverse dataset, the
first residue prediction of ConCavity will be in contact with a ligand
in nearly 80% of proteins. ConCavity also maintains high precision
across the full recall range: precision of 65% at 50% recall and
better than 30% when all ligand-binding residues have been
identified. As mentioned above, this large improvement exists
when predicting ligand locations as well; however, the PR curves
illustrate that fully identifying a ligand’s position is more difficult
for each of the methods than finding all contacting residues.
The ligand overlap statistics presented in Table 1 also
demonstrate the superior performance of ConCavity. In nearly
95% of structures, ConCavity’s predictions overlap with a bound
ligand. Structure’s predictions overlap ligands in nearly 92% of the
proteins considered. The differences between the methods become
more stark when we examine the magnitude of these overlaps. Both
ConCavity and Structure predict pockets with total volume (Prediction
Vol.) similar to that of all relevant ligands (Ligand Vol.), but
ConCavity’s pockets overlap a larger fraction of the ligand volume.
Thus ConCavity has a significantly higher Jaccard coefficient
(p,2.2e216). This suggests that the integration of sequence
conservation with structural pocket identification results in more
accurate pockets than when using structural features alone.
Figure 1B also provides a direct comparison of ligand binding site
prediction methods based on sequence conservation with those
based on structural features. Structure outperforms Conservation,astate-
of-the-art method for estimating sequence conservation. Protein
residues can be evolutionarily conserved for a number of reasons, so
it is not surprising that Conservation identifies many non-ligand-
binding residues, and thus, does not perform as well as Structure.
ConCavity’s improvement comes from integrating
complementary information from evolutionary sequence
conservation and structure-based pocket identification
Figures 2 and 3 present pocket and residue predictions of
Conservation, Structure, and ConCavity on three example proteins. In
general, different types of positions are predicted by Conservation
and Structure. If we consider the number of known ligand binding
residues for each protein in the dataset, and take this number of
top predictions for the Structure and Conservation methods, the
overlap is only 26%. The residues predicted by sequence
conservation are spread throughout the protein (Figure 2);
ligand-binding residues are often very conserved, but many other
positions are highly conserved as well due to other functional
constraints. In contrast, the structure-based predictions are
strongly clustered around surface pockets (Figure 3, left column);
many of these residues near pockets are not evolutionarily
conserved. However, these features provide largely complemen-
tary information about importance for ligand binding. Over the
entire dataset, 68% of residues predicted by both Conservation and
Structure are in contact with ligands, while only 16% and 43% of
those predicted by only conservation or structure respectively are
ligand binding. ConCavity takes advantage of this complementarity
to achieve its dramatic improvement; it gives high scores to
positions that show evidence of both being in a well-formed pocket
and being evolutionarily conserved.
The examples of Figures 2 and 3 illustrate this and highlight
several common patterns in ConCavity’s improved predictions. For
3CWK, a cellular retinoic acid-binding protein, Structure and
ConCavity’s residue predictions center on the main ligand binding
pocket (Figure 3A), while Conservation gives high scores to some
positions in the binding site, but also to some unrelated residues
(Figure 2A). Looking at the ligand location predictions (green
meshes in Figure 3A), Structure and ConCavity both find the pocket,
but the signal from conservation enables ConCavity to more
accurately trace the ligand’s location. This illustrates how the
pattern of functional conservation observed at the protein surface
influences the shape of the predicted pocket. Ligands often do not
completely fill surface pockets; if the contacting residues are
conserved, our approach can suggest a more accurate shape.
The results for 2CWH (Figure 3B) and 1G6C (Figure 3C)
demonstrate that ConCavity can predict dramatically different sets
of pockets than are obtained when considering structure alone. In
2CWH, both methods identify the ligands, but Structure over-
predicts the bottom left binding pocket and predicts an additional
pocket that does not have a ligand bound. ConCavity traces the
ligands more closely and does not predict any additional pockets.
Structure performs quite poorly on the tetramer 1G6C: it predicts
several pockets that do not bind ligands; it fails to completely
identify several ligands; and it misses one ligand entirely. In stark
contrast, ConCavity’s four predicted pockets each accurately trace a
ligand. The incorporation of conservation resulted in the accurate
prediction of a pocket in a region where no pocket was predicted
using structure alone. Images of predictions for all methods on all
proteins in the dataset are available in the Text S1 file, and
ConCavity’s predictions for all structures in the Protein Quaternary
Structure (PQS) database are available online.
ConCavity significantly outperforms available prediction
We now compare the performance of ConCavity to several
existing ligand binding site identification methods with publicly
Table 1. The overlap between predicted pockets and bound ligands in holo protein structures from the LigASite database.
Fraction with
Ligand Overlap
Prediction Vol.
Ligand Vol.
Prediction \
Prediction |
Structure 0.92 1806.8 1977.2 426.9 3357.1 0.197
ConCavity 0.95 1806.9 1977.2 647.6 3136.5 0.257
The first column gives the fraction of proteins for which a method’s predictions overlap a ligand. The second column (Prediction Vol.) lists the average volume of the
predicted pockets for each protein, while the third column (Ligand Vol.) lists the average volume of ligands observed in the structure. The next columns give the
average volumes of the Intersection and Union of the predictions and ligands and the Jaccard coefficient (Intersection/Union). ConCavity and Structure predict pockets
of similar sizes---both use a similar pocket volume threshold---but ConCavity’s predictions overlap more of the bound ligands. ConCavity’s higher Jaccard coefficient
demonstrates that it better manages the tradeoff between precision and recall.
Ligand Binding Site Prediction with ConCavity
PLoS Computational Biology | 4 December 2009 | Volume 5 | Issue 12 | e1000585
Page 4
available web servers. LigsiteCS [1] is an updated version of
geometry-based Ligsite, and LigsiteCSC [1] is a similar structural
method that considers evolutionary conservation information.
Q-SiteFinder [25] estimates van der Waals interactions between the
protein and a probe in a fashion similar to PocketFinder. CASTp [19]
is a geometry-based algorithm for finding pockets based on
analysis of the protein’s alpha shape. Each of the servers produces
a list of predicted pockets represented by sets of residues; however,
none of them provide a full 3D representation of a predicted
pocket. As a result, we assess their ability to predict ligand binding
residues. See the Methods section for more information on the
generation and processing of the servers’ predictions. In brief, the
residues predicted by each server are ranked according to the
highest ranking pocket to which they are assigned, i.e., all residues
from the first predicted pocket are given a higher score than those
from the second and so on. We re-implemented the conservation
component of LigsiteCSC, because the conservation-based
re-ranking option on the web server did not work for many of
the proteins in our dataset. We used JSD as the conservation
scoring method.
Figure 4 presents the ligand binding residue PR-curves for each
of these methods. ConCavity significantly outperforms LigsiteCS,
, Q-SiteFinder, and CASTp (p,2.2e216 for each).
Surprisingly, Conservation is competitive with these structure-based
approaches. Several of the servers did not produce predictions for
a small subset of the proteins in the database, e.g., the Q-SiteFinder
server does not accept proteins with more than 10,000 atoms.
Figure 4 is based on 234 proteins from the LigASite dataset for
which were able to obtain and evaluate predictions for all
methods. Thus the curve for ConCavity is slightly different than
those found in the other figures, but its performance does not
change significantly.
is the previous method most similar to ConCavity;it
uses sequence conservation to rerank the pockets predicted by
LigsiteCS. LigsiteCSC
provides slight improvement over LigsiteCS,
but the improvement is dwarfed by that of ConCavity over Structure
(Figure 1). This illustrates the benefit of incorporating conservation
information directly into the search for pockets in contrast to using
conservation information to post-process predicted pockets.
The poor performance of these previous methods at
identifying ligand binding residues is due in part to the fact
that they do not distinguish among the residues near a predicted
binding pocket. The entire pocket is a useful starting place for
analysis, but many residues in a binding pocket will not actually
contact the ligand. Knowledge of the specific ligand binding
residues is of most interest to researchers. The predictions of our
methods reflect this---residues within the same pocket can
receive different ligand binding scores. The inability of previous
methods to differentiate residues in a pocket from one another is
one reason why we elect to use our own implementations of
previous structure-based methods as representatives of these
approaches in all other comparisons. See the Methods section for
more details.
We tested an additional approach for combining sequence
conservation with structural information that was suggested by the
observation that clusters of conserved residues in 3D often overlap
with binding sites [41,42]. Briefly, the method performs a 3D
Gaussian blur of the conservation scores of each residue, and
assigns each residue the maximum overlapping value. Thus
residues nearby in space to other conserved residues get high
scores. This approach improved on considering conservation
alone, but was not competitive with ConCavity (Text S1). We also
considered the clusters of conserved residues generated by the
Evolutionary Trace (ET) Viewer [57]. The clusters defined at 25%
protein coverage were ranked by size, and residues within the
clusters were ranked by their raw ET score. This approach did not
perform as well as the above clustering algorithm (data not shown),
and was limited to single chain proteins, because ET returns
predictions for only one chain of multi-chain proteins.
Figure 2. Evolutionary sequence conservation mapped to the
surface of three example proteins. (A) Cellular retinoic acid-binding
protein II (PDB: 3CWK). (B) Delta1-piperideine-2-carboxylate reductase
(PDB: 2CWH). (C) Thiamin phosphate synthase (PDB: 1G6C). Warmer
colors indicate greater evolutionary conservation; the most conserved
residues are colored dark red, and the least conserved are colored dark
blue. Ligands are rendered with yellow sticks, and protein backbone
atoms are shown as spheres. In general, Conservation gives the highest
scores to residues near ligands, but high scoring residues are found
throughout each structure. The predictions of Structure and ConCavity
for these proteins are given in Figure 3.
Ligand Binding Site Prediction with ConCavity
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Page 5
ConCavity performs similarly for geometry and energetics
based grid creation methods
In the previous sections, we used ConCavity
, which integrates
evolutionary sequence conservation estimates from the Jensen-
Shannon divergence (JSD) into Ligsite
, to represent the perfor-
mance of the ConCavity approach. However, our strategy for
combining sequence conservation with structural predictions is
general; it can be used with a variety of grid-based surface pocket
identification algorithms and conservation estimation methods.
Figure 3. Comparison of the binding site predictions of
on three example proteins. The three proteins presented here
correspond to those shown in Figure 2. In each pane, ligand binding residue scores have been mapped to the protein surface. Warmer colors indicate a
higher binding score. Pocket predictions are shown as green meshes. (A) PDB: 3CWK. Both methods identify the binding site, but by considering
conservation information (Figure 2A), ConCavity more accurately traces the ligand. (B) PDB: 2CWH. Structure significantly overpredicts the extent of the ligand
in the bottom left corner as well as predicting an additional pocket on the reverse of the protein. ConCavity predicts only the two ligand binding pockets. (C)
PDB: 1G6C. In order to visualize the predictions more clearly, only the secondary structure diagram of the protein is shown. This example illustrates the
difficulty presented by multichain proteins; there are many cavities in the structure, but not all bind ligands. Structure identifies some of the relevant pockets,
but focuses on the large, non-binding central cavity formed between the chains. Referring to this protein’s conservation profile (Figure 2C), we see that the
ligand binding pockets exhibit high conservation while the non-binding pockets do not. As a result, ConCavity selects only the relevant binding pockets. In
each example, ConCavity selects the binding pocket(s) out of all potential pockets and more accurately traces the ligands’ locations in these pockets.
Ligand Binding Site Prediction with ConCavity
PLoS Computational Biology | 6 December 2009 | Volume 5 | Issue 12 | e1000585
Page 6
Figure 5 gives PR-curves that demonstrate that ConCavity
provides excellent performance whether the structural approaches
are based on geometric properties (Ligsite
, Surfnet
) or energetics
). The significant improvement holds for predicting
both ligand locations in space (p,2.2e216 for each pair)
(Figure 5A) and ligand binding residues (p = 6.802e213 for
,p,2.2e216 for PocketFinder
,p,2.2e216 for Surfnet
(Figure 5B). The three ConCavity versions perform similarly despite
the variation in performance between Ligiste
, Surfnet
, and
. In the following sections we will include performance
statistics for all three methods when space and clarity allow. When
not presented here, results for all methods are available in the
supplementary file Text S1.
We have also found that ConCavity achieves similar performance
when a different state-of-the-art method [26] is used to score
evolutionary sequence conservation (Text S1).
Structure-based methods have difficulty with multi-chain
Proteins consisting of multiple subunits generally have more
pockets than single-chain proteins due to the gaps that often form
between chains. To investigate the effect of structural complexity
on performance, we partitioned the dataset according to the
number of chains present in the structure predicted by the Protein
Quaternary Structure (PQS) server [58] and performed our
previous evaluations on the partitioned sets. Figure 6 gives these
statistics for ConCavity, Structure, and Conservation. To enable side-by-
side comparison, we report the area under the PR curves (PR-
AUC) rather than giving the full curves.
As the number of chains in the structure increases, there is a
substantial decrease in the performance of Structure. The pattern is
seen both when predicting ligand binding residues (Figure 6A) and
pockets (Figure 6B, C). This effect is so large that, for proteins with
five or more chains, Conservation outperforms Structure. The number
of chains in the protein has little effect on Conservation’s
performance. The performance of Random on proteins with a
small number of chains is slightly worse than on proteins with
many chains (e.g., Residue PR-AUC for 1 chain: 0.097, 2 chains:
0.110, 3 chains: 0.127, 4 chains: 0.119, 5+ chains: 0.142), so the
drop in Structure’s performance is not the result of the proportion of
positives in each set. These observations emphasize the impor-
tance of including multi-chain proteins in the evaluation.
The homo-tetramer 1G6C in Figure 3C provides an illustrative
example of the failure of Structure on multi-chain proteins. There is
a large gap between the chains in the center of the structure, and
several additional pockets are formed at the interface of pairs of
contacting chains. As seen in the figure, the large central cavity
does not bind a ligand; however, it is the largest pocket predicted
by Structure. This is frequently observed among the predictions.
While some pockets between protein chains are involved in ligand
binding, many of them are not. As the number of chains increases,
so does the number of such potentially misleading pockets.
By incorporating sequence conservation information, ConCavity
accurately identifies ligand binding pockets in multi-chain
proteins. The conservation profile on the surface of 1G6C
provides a clear example of this; the pockets that exhibit sequence
conservation are those that bind ligands (Figure 2C). 1G6C is
not an exception. ConCavity provides significant performance
improvement for each partition of the dataset in all three
evaluations, and greatly reduces the effect of the large number of
non-ligand-binding pockets in multi-chain proteins on perfor-
mance. ConCavity also provides improvement over Structure on the
set of one chain proteins. This is notable because these proteins
do not have between-chain gaps, so the improvement comes from
tracing ligands and selecting among intra-chain pockets more
accurately than using structural information alone (as in
Figure 3A).
ConCavity performs well on both apo and holo structures
The binding of a ligand induces conformational changes to a
protein [59]. As a result, the 3D structure of the binding site can
differ between structures of the same protein with a ligand bound
(holo) and not bound (apo). In the holo structures, the relevant
side-chains are in conformations that contact the ligand, and this
often defines the binding pocket more clearly than in apo
structures. To investigate the effect of the additional information
provided in holo structures on performance, we evaluated the
methods on both sets (Table 2).
As expected, all methods performed better on the holo (bound)
structures than the corresponding apo (unbound) structures.
However, all previous conclusions hold whether considering apo
structures or holo structures; the ranking of the methods is
consistent, and the improvement provided by considering
conservation is similarly large. PR curves for this comparison are
given in the supplementary file Text S1. We will continue to report
residue prediction results computed using the apo structures when
possible in order to accurately assess the performance of the
algorithms in the situation faced by ligand binding site prediction
methods in the real world.
Figure 4. Comparison of
with publicly available
ligand binding site prediction servers. ConCavity significantly
outperforms each previous method at the prediction of ligand binding
residues. The existing servers focus on the task of pocket prediction,
and return sets of residues that represent binding pocket predictions.
They do not give different scores to these individual residues. In
contrast, ConCavity assigns each residue a likelihood of binding, and
thus residues in the same predicted pocket can have different scores.
This ability and the direct integration of sequence conservation are the
major sources of ConCavity’s improvement. Conservation, the method
based solely on sequence conservation, is competitive with these
previous structural approaches. This figure is based on 234 proteins
from the LigASite apo dataset for which we were able to obtain
predictions from all methods.
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Page 7
The methods better identify ligand binding sites in
enzymes than non-enzymes
The LigASite apo dataset contains protein molecules that carry
out a range of different functions. Enzymes are by far the most
common; they make up 254 of the 332 proteins in the dataset. The
remaining 78 non-enzyme ligand binding proteins are involved in
a wide variety of functions, e.g., transport, signaling, nucleic acid
binding, and immune system response.
Table 3 compares the performance of the ligand binding site
prediction methods on enzymes and non-enzymes. There is more
variation within each method’s performance on non-enzyme
proteins, and all methods perform significantly better on the
enzymes (e.g., p = 3.336e24 for ConCavity
). Active sites in
enzymes are usually found in large clefts on the protein surface
and consistently exhibit evolutionary sequence conservation
[60,61], so even though enzymes bind a wide array of substrates,
these common features may simplify prediction when compared to
the variety of binding mechanisms found in other proteins.
Despite the drop in performance on non-enzyme proteins, the
main conclusions from the earlier sections still hold. However, the
improvement provided by ConCavity is not as great on the non-
enzymes. This could be the result of the more complex patterns of
conservation found in non-enzyme proteins, and the compara-
tively poor performance of Conservation in this setting. It is also
possible that Ligsite
’s approach is particularly well suited to
identifying binding sites in non-enzymes. Overall, these results
highlight the importance of using a diverse dataset to evaluate
functional site predictions.
ConCavity improves identification of drug binding sites
Knowledge of small molecule binding sites is of considerable use
in drug discovery and design. Many of the techniques used to
screen potential targets, e.g., docking and virtual screening, are
computationally intensive and feasible only when focused on a
specific region of the protein surface. Structure based surface
cavity identification algorithms can guide analysis in such
situations [52].
To test ConCavity’s ability to identify drug binding sites, we
evaluated it on a set of 98 protein-drug complexes [62]. The
superior performance provided by ConCavity over Structure on the
diverse set of proteins considered above suggests that ConCavity
would likely be useful in the drug screening pipeline. Table 4
compares the ligand overlap PR-AUC and Jaccard coefficient for
the three versions of ConCavity and their structure-based analogs.
Each ConCavity method significantly improves on the methods that
only consider structural features (e.g., p = 1.25e26 on overlap PR-
AUC and p = 2.06e26 on Jaccard for ConCavity
). While the
improvement is not quite as large on this dataset as that seen on
the more diverse LigASite dataset, it is still significant. It is possible
that this is due to the fact that drug compounds are not the
proteins’ natural ligands; the evolutionary conservation of the
residues in binding pockets may reflect the pressures related to
binding the actual ligands rather than the drugs.
Examples of difficult structures
While ConCavity signficantly outperforms previous approaches,
its performance is not flawless. In Figure 7, we give three example
structures that illustrate patterns observed when ConCavity
performs poorly. Handling these cases is likely to be important
for further improvements in ligand binding site prediction.
The first pattern common among these difficult cases is
evolutionary sequence conservation information leading predic-
tions away from actual ligand binding sites. Figure 7A provides an
example in which the ligand binding site is less conserved than
other parts of the protein. The ActR protein from Streptomyces
coelicolor (PDB: 3B6A) contains both a small molecule ligand-
binding and a DNA-binding domain [63]. The ligand-binding
domain is in the bottom, less-conserved half of the structure. The
Figure 5. Comparison of different versions of
ConCavity provides a general framework for binding site prediction. We use Ligsite
based ConCavity as representative, but it is possible to use other algorithms in ConCavity. This figure compares the PR curves for three versions
, ConCavity
, ConCavity
)---each based on integrating sequence conservation with a different grid creation strategy (Ligsite
, PocketFinder
or Surfnet
). All three versions perform similarly, and all significantly outperform the methods based on structure analysis alone (dashed lines). These
conclusions hold for both ligand binding pocket (A) and ligand binding residue (B) prediction.
Ligand Binding Site Prediction with ConCavity
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Page 8
DNA-binding domain is found in the more conserved top half of
the given structure. The greater conservation of this domain
causes ConCavity to focus on the DNA-binding site over the ligand
binding site. In other cases, conservation information is uninfor-
mative due to a lack of homologous sequences. Conservation
estimates based on low quality sequence alignments may harm
performance for some structures, but we have found that they still
provide a net performance gain overall (Text S1).
Figure 7 also provides two examples of another difficult case:
ligands bound outside of clearly defined, concave surface pockets.
In Figure 7B, ConCavity identifies the center of the ring-shaped
structure of the pentameric B-subunit of a shiga-like toxin (PDB:
1CQF) as the binding site. This protein binds to glycolipids, like
the globotriaosylceramide (Gb3) shown, via a relatively flat
interface that surrounds the center of the ring [64]. The center
cavity (ConCavity’s prediction) is filled by a portion of the A-subunit
of the toxin (not included in the structure) which after binding
breaks off and enters the host cell. Figure 7C shows the structure of
a dimeric noncatalytic carbohydrate binding module (CBM29)
from Piromyces equi complexed with mannohexaose (PDB: 1GWL).
The carbohydrate ligands bind in long flat clefts on the protein
Figure 6. Ligand-binding site identification performance by
number of chains in structure. (A) The average area under the
precision-recall curve (PR-AUC) for predicting ligand binding residues
on each set of structures. (B) The average PR-AUC for ligand binding
pocket identification. (C) The average Jaccard coefficient of the overlap
of the predicted pockets with bound ligands. Methods based on
structure alone have an increasingly difficult time distinguishing among
ligand-binding pockets and non-ligand-binding gaps between chains as
the number of chains in the protein increases. This trend is clear in each
evaluation. Conservation’s performance does not exhibit this effect (A).
In fact, Conservation outperforms Structure on proteins with five or
more chains. The integration of sequence conservation and pocket
prediction in ConCavity improves performance in each chain based
partition in each evaluation, and ConCavity sees only a modest decrease
in performance on proteins with multiple chains. Conservation alone
could not be included in (B) and (C), because it does not make pocket
predictions. Note that the y-axes in the figures do not all have the same
scale. The number of structures per chain group: 1 chain: 143, 2 chains:
112, 3 chains: 18, 4 chains: 35, 5 or more chains: 24.
Table 2. Area under the Precision-Recall curve (PR-AUC) for
ligand-binding residue prediction methods on apo (unbound)
and holo (bound) versions of LigASite.
Residue PR-AUC
Method apo holo
0.608 0.657
0.601 0.646
0.586 0.648
0.519 0.552
0.472 0.514
0.416 0.481
Random 0.109 0.095
All methods perform better on holo structures than apo structures, but the
drop in performance is not dramatic, and the relative ranking of the methods is
the same across both datasets.
Table 3. Ligand binding residue identification in enzymes
and non-enzymes (LigASite apo).
Residue PR-AUC
Method Enzyme Non-enzyme
0.647 0.480
0.642 0.466
0.624 0.461
0.541 0.451
0.494 0.399
0.430 0.370
Conservation 0.318 0.216
Random 0.104 0.123
All methods are better at identifying binding residues in enzymes than in non-
enzymes. The ConCavity methods achieve the best performance on both
datasets, but incorporating conservation information provides less
improvement in non-enzymes.
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Page 9
surface [65]. Even though these sites exhibit significant evolution-
ary conservation, their geometry prevents them from being
predicted. Instead, a less conserved pocket formed between the
chains is highlighted by ConCavity.
Overall, cases such as these are rare; ConCavity’s predictions fail
to overlap a ligand in only 5% of structures. In addition, some of
these ‘‘incorrect’’ predictions are actually functionally relevant
binding sites for other types of interactions as illustrated in
Figure 7.
Integrating conservation and structure improves
prediction of catalytic sites
Ligand-binding sites are not the only type of functional site of
interest to biologists. A large amount of attention has been given to
the problem of identifying catalytic sites. As noted above, the
majority of enzyme active sites are found in large clefts on the
protein surface, so even though the structural methods considered
in this paper were not intended to identify catalytic sites, they
could perform well at this task.
Table 5 gives the results of an evaluation of the methods’ ability
to predict catalytic sites (defined by the Catalytic Site Atlas [66]) in
the LigASite apo dataset. Compared to ligand binding site
prediction, the relative performance of the methods is different
in this context. The ConCavity approach still significantly
outperforms the others (p,2.2e216 for Structure, p = 8.223e24
for Conservation). Most surprisingly, Conservation significantly outper-
forms methods based on structure alone (p = 9.863e23 Ligsite
p = 4.694e26 Pocketfinder
, p = 1.171e26 Surfnet
). All the methods
have lower PR-AUC when predicting catalytic sites than
predicting ligand-binding residues (e.g., ConCavity
has PR-AUC
of 0.315 versus 0.608); this is due in large part to the considerably
smaller number of catalytic residues than ligand-binding residues
per protein sequences.
These results imply that being very evolutionarily conserved is
more indicative of a role in catalysis than being found in a surface
pocket. Though catalytic sites are usually found in pockets near
bound ligands, there are many fewer catalytic sites per protein
than ligand-binding residues. As a result simply searching for
residues in pockets identifies many non-catalytic residues. This is
consistent with earlier machine learning studies that found
conservation to be a dominant predictive feature [5,49,50], and
it suggests that new structural patterns should be sought to
improve the identification of catalytic sites.
Table 4. Drug binding site identification.
Method Grid Value PR-AUC Jaccard coefficient
0.271 0.240
0.263 0.222
0.278 0.236
0.217 0.207
0.195 0.191
0.170 0.183
Random 0.006 N/A
This table compares the average grid value precision-recall AUC and the
average Jaccard coefficient of prediction-ligand overlap for ConCavity and
methods based on structural analysis alone on a set of 98 protein-drug
complexes. Integrating sequence conservation and structure-based pocket
finding improves the identification of drug binding sites. Conservation is not
included in this evaluation, because it does not make pocket-level predictions.
Figure 7. Examples of difficult structures. For each structure,
evolutionary sequence conservation has been mapped to the surface of
the protein backbone (all atoms in pane (C)) with warmer colors
indicating greater conservation. Bound ligands are shown in yellow, and
the pocket predictions of ConCavity are represented by green meshes.
(A) The ActR protein (PDB: 3B6A) contains both a ligand-binding
(bottom half) and a more conserved DNA-binding domain (top half). (B)
The ring-shaped pentameric B-subunit of a shiga-like toxin (PDB: 1CQF)
binds globotriaosylceramide (Gb3) via a relatively flat interface that
surrounds the center of the ring. (C) The carbohydrate binding sites of
the CBM29 protein (PDB: 1GWL) are too long and flat to be detected by
ConCavity in the presence of a concave pocket between the chains. As
illustrated here, ConCavity’s inaccurate predictions are often the result
of misleading evolutionary sequence conservation information (A) or
ligands that bind partially or entirely outside of well-defined concave
surface pockets (B, C). In (A) and (B), ConCavity misses the ligands, but
identifies functionally relevant binding sites for other types of
interactions (DNA and protein).
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Page 10
Several previous methods have combined sequence conserva-
tion and structural properties in machine learning frameworks to
predict catalytic sites [5,50,51]. Direct comparison with these
methods is difficult because most datasets and algorithms are not
readily available. Tong et al. [51] compared the precision and
recall of several machine learning methods on different datasets in
an attempt to develop a qualitative understanding of their relative
performance. While it is not prudent to draw conclusions based on
cross-dataset comparisons, we note for completeness that Con-
Cavity’s catalytic site predictions the diverse LigASite dataset
achieve higher precision (23.8%) at full recall than the maximum
precision (over all recall levels) reported for methods in their
Evolutionary sequence conservation and protein 3D structures
have commonly been used to identify functionally important sites;
here, we integrate these two approaches in ConCavity , a new
algorithm for ligand binding site prediction. By evaluating a range
of conservation and structure-based prediction strategies on a
large, diverse dataset of ligand binding sites, we establish that
structural approaches generally outperform sequence conserva-
tion, and that by combining the two, ConCavity outperforms
conservation-alone and structure-alone on about 95% and 70% of
structures respectively. Overall, ConCavity’s first predicted residue
contacts a ligand in nearly 80% of the apo structures examined,
and it maintains high precision across all recall levels. These results
hold for the three variants of ConCavity we considered, each of
which uses a different underlying structure-based component. In
addition, ConCavity’s integrated approach provides significant
improvement over conservation and structure-based approaches
on the common task of identifying drug binding sites.
Combining sequence conservation-based methods with struc-
ture information is especially powerful in the case of multi-meric
proteins. Our analysis has shown that the performance of
structural approaches for identifying ligand binding sites dramat-
ically decreases as the number of chains in the structure increases;
conservation alone outperforms structure-based approaches on
proteins with five or more chains. It is difficult to determine from
structural attributes alone if a pocket formed at a chain interface
binds a ligand or not. However, ligand binding pockets usually
exhibit high evolutionary sequence conservation. ConCavity, which
takes advantage of this complementary information, performs very
well on multi-chain proteins; the presence of many non-ligand
binding pockets between chains has little effect on its performance.
While ConCavity outperforms previous approaches, we have
found two main causes of poor results: misleading evolutionary
sequence conservation information and ligands that bind partially
or entirely outside of well-defined concave surface pockets. Ligand
binding sites may lack strong conservation for a number of
reasons: the underlying sequence alignment may be of low quality,
there may be other more conserved functional regions in the
protein, and some sites are hypervariable for functional reasons
[67]. The alignment quality issue will become less relevant as
sequence data coverage and conservation estimation methods
improve. The second two cases may require the integration of
additional features to better distinguish different types of functional
sites. Similarly, finding biologically relevant ligands that bind
outside of concave surface pockets will likely require the
development of additional structural descriptors. Missing or
incomplete ligands also affect the apparent performance of the
methods, but such issues are unavoidable due to the nature of the
structural data.
In implementing and evaluating previous 3D grid-based ligand
binding site prediction approaches, we have found that the
methods used both to aggregate grid values into coherent pockets
as well as to map these pockets onto surface residues can have a
large effect on performance. In order to focus on the improvement
provided by considering evolutionary sequence conservation, the
results for previous structure-based methods presented above use
our new algorithms for these steps. We describe the details of our
approaches in the Methods section. On a high level, the new
methodologies we propose provide significant improvement by
predicting a flexible number of well-formed pockets for each
structure and by assigning each residue a likelihood of binding a
ligand based on its local environment rather than on the rank of
the entire pocket. We have used morphological properties of
ligands to guide pocket creation, but the most appropriate
algorithms for these steps depend strongly on the nature of the
prediction task. These steps have received considerably less
attention than computing grid values; our results suggest that
they should be given careful consideration in the future.
We have focused on the prediction of ligand binding sites, but
the direct synthesis of conservation and structure information is
likely to be beneficial for predicting other types of functionally
important sites. Our application of ConCavity to catalytic site
prediction illustrates the promise and challenges of such an
approach. Catalytic sites are usually found in surface pockets, but
considering structural evidence alone performs quite poorly---
worse than sequence conservation. Combining structure with
evolutionary conservation provides a modest gain in performance
over conservation alone. Protein-protein interface residues are
another appealing target for prediction; much can be learned
about a protein by characterizing its interactions with other
proteins. However, protein-protein interaction sites provide
additional challenges; they are usually large, flat, and often poorly
conserved [68]. ConCavity is not appropriate for this task. Other
types of functional sites also lack simple attributes that correlate
strongly with functional importance. Analysis of these sites’
geometries, physical properties, and functional roles will produce
more accurate predictors, and may also lead to new insights about
the general mechanisms by which proteins accomplish their
molecular functions.
In summary, this article significantly advances the state-of-the-
art in ligand binding site identification by improving the
philosophy, methodology, and evaluation of prediction methods.
It also increases our understanding of the relationship between
Table 5. Catalytic residue identification (LigASite apo).
Method PR-AUC
Conservation 0.249
Random 0.012
ConCavity identifies more catalytic sites than other methods. However, in
contrast to ligand binding residue prediction, Conservation outperforms the
structure-based approaches at detecting catalytic sites.
Ligand Binding Site Prediction with ConCavity
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Page 11
evolutionary sequence conservation, structural attributes of
proteins, and functional importance. By making our source code
and predictions available online, we hope to establish a platform
from which the prediction of functional sites and the integration of
sequence and structure data can be investigated further.
This section describes the components of the ConCavity
algorithm for predicting ligand binding residues from protein 3D
structures and evolutionary sequence conservation.
ConCavity proceeds in three conceptual steps: grid creation,
pocket extraction, and residue mapping (Figure 8). First, the
structural and evolutionary properties of a given protein are used
to create a regular 3D grid surrounding the protein in which the
score associated with each grid point represents an estimated
likelihood that it overlaps a bound ligand atom (Figure 8A).
Second, groups of contiguous, high-scoring grid points are
clustered to extract pockets that adhere to given shape and size
constraints (Figure 8B). Finally, every protein residue is scored
with an estimate of how likely it is to bind to a ligand based on its
proximity to extracted pockets (Figure 8C).
Grid-based strategies have been employed by several previous
systems for ligand binding site prediction (e.g., [14,16,23]).
However, our adaptations to the three steps significantly affect
the quality of predictions. First, we demonstrate how to integrate
evolutionary information directly into the grid creation step for
three different grid-based pocket prediction algorithms. Second,
we introduce a method that employs mathematical morphology
operators to extract well-shaped pockets from a grid. Third, we
provide a robust method for mapping grid-based ligand binding
predictions to protein residues based on Gaussian blurring. The
details of these three methods and an evaluation of their impacts
on ligand-binding predictions are described in the following
Grid creation. The first step of our process is to construct a
3D regular grid covering the free-space surrounding a protein.
The goal is to produce grid values that correlate with the
likelihoods of finding a bound ligand at each grid point.
Several methods have been proposed to produce grids of this
type. For example, Ligsite [16] produces a grid with values between
0 and 7 by scanning for the protein surface along the three axes
and the four cubic diagonals. For each grid point outside of the
protein, the number of scans that hit the protein surface in both
directions---so-called protein-solvent-protein (PSP) events---is the
value given to that point. A large number of PSP events indicate
that the grid point is surrounded by protein in many directions and
thus likely to be in a pocket.
Surfnet [14] assigns values to the grid by constructing spheres
that fill the space between pairs of protein atoms without
overlapping any other atoms. These sets of spheres are constructed
for all pairs of protein surface atoms within 10 A
of each other.
Spheres with a radius smaller than 1.5 A
are ignored, and spheres
are allowed to have a maximum radius of 4 A
. This procedure
results in a set of overlapping spheres that fill cavities and clefts in
the protein. Extending the original algorithm slightly, we assign
the value for each grid point to be the number of spheres that
overlap it (rather than simply one for overlap and zero for no
overlap as in the original algorithm). Thus, higher values are
generally associated with the positions in the ‘‘center’’ of a pocket.
PocketFinder [23] assigns values to grid points by calculating the
van der Waals interaction potential of an atomic probe with the
protein. The Lennard-Jones function is used to estimate the
interaction potential between the protein and a carbon atom
placed at each grid point. The potential at a grid point p is:
where C
and C
are constants (taken from AutoDock [69]) that
shape the Lennard-Jones function according to the interaction
energy between the carbon probe atom and protein atom a, and r
is the distance between the grid point p and a (interactions over
distances greater than 10 A
are ignored).
Figure 8.
prediction pipeline. The large gray shape represents a protein 3D structure; the triangles represent surface residues; and the
gray gradient symbolizes the varying sequence conservation values in the protein. Darker shades of each color indicate higher values. (A) The initial
grid values come from the combination of evolutionary sequence conservation information and a structural predictor, in this example Ligsite. The
algorithm proceeds similarly for PocketFinder and Surfnet. (B) The grid generated in (A) is thresholded based on morphological criteria so that only
well-formed pockets have non-zero values. For simplicity, only grid values near the pockets are shown. (C) Finally, the grid representing the pocket
predictions is mapped to the surface of the protein. We perform a 3D Gaussian blur (s~4A
) of the pockets, and assign each residue the highest
overlapping grid value. Residues near regions of space with very high grid values receive the highest scores.
Ligand Binding Site Prediction with ConCavity
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Page 12
Other grid creation methods have been proposed as well, but
these three (Ligsite, Surfnet, and PocketFinder) provide a representative
set for our study.
We augment these algorithms by integrating evolutionary
information into the grid creation process. Our methodology is
based on the observation that these (and other) grid creation
algorithms operate by accumulating evidence (‘‘votes’’) for ligand
binding at grid points according to spatial relationships to nearby
protein atoms. For PocketFinder, each protein atom casts a ‘‘vote’’
for nearby grid points with magnitude equal to the (opposite of the)
van der Waals potential. In Ligsite, every pair of protein atoms
‘‘votes’’ for solvent-accessible grid points on line segments between
them. In our implementation of Surfnet, pairs of atoms ‘‘vote’’ for
all the grid points overlapping a sphere covering the solvent
accessible region between them.
Based on this observation, we weight the ‘‘votes’’ as the grid is
created by an estimate of sequence conservation of the residue(s)
associated with the atom(s) that generate the votes. We tested
several schemes for scaling votes. If c
and c
are estimated
conservation scores associated with the relevant atoms (e.g.,
derived from their residues’ conservation in multiple sequence
alignments), we scaled the structure-based component by the
product (c
), the arithmetic mean (
), the geometric mean
), the product of exponentials (2
), and the product of
exponentials of transformed conservation values (2
Each of these schemes provides improvement for all methods, but
due to method specific differences, no single weighting scheme is
best for all methods. Specifically, for PocketFinder, which has only one
atom associated with each vote, we scale the vote (van der Waal’s
potential) of each atom linearly by c
. For Ligsite we scale the votes
by arithmetic mean of the conservation values and for Surfnet by the
product of the exponentials of the transformed conservation values.
In our study, conservation scores are calculated by the Jensen-
Shannon divergence (JSD) with sequence weighting and a gap
penalty [2]; however, any sequence conservation measure that
produces residue scores (which are then mapped to atoms within
the residues) could be incorporated.
Performance. The superior performance of our ConCavity grid
creation method at predicting ligand binding pockets and residues is
demonstrated in Figure 5 of the Results section. The only difference
between the ConCavity methods (ConCavity
, ConCavity
, ConCavity
and their counterparts based on structure alone (Ligsite
, Surfnet
) is the use of sequence conservation in the grid creation
step. For each grid creation strategy, considering evolutionary
conservation yields significant improvement.
Pocket extraction. The second step of our process is to
cluster groups of contiguous, high-scoring grid points into pockets
that most likely contain bound ligands.
Several methods have been previously proposed to address this
problem. The simplest is to apply a fixed threshold to the grid, i.e.,
eliminate all grid points below some given value. Then, the
remaining grid points can be clustered into pockets (e.g.,
connected components), and small pockets can be discarded. This
method, which we call ‘‘Threshold’’, has been used in previous
versions of Ligsite [1,16]. A problem with this approach is that the
threshold is set to the same value for all proteins, which provides
no control over the total number and size of pockets predicted by
the algorithm. In the worst case, when every grid value is below
the threshold, then the algorithm will predict no pockets. On the
other hand, if the threshold is too low, then there will be many
large pockets. Different proteins have different types of pockets, so
no one threshold can extract appropriately sized and shaped
pockets for all of them.
A slightly more adaptive method is used in PocketFinder [23]. In
‘‘StdDev’’ the mean and standard deviation of values in the grid are
used to determine a different threshold for every protein.
Specifically, the grid is blurred with s~2:6A
, and then the
threshold is set to be 4.6 standard deviations above the mean of the
grid values. This approach is problematic because the threshold
depends on the parameters of the grid; any change to how the
protein is embedded in the grid (e.g., orienting the protein
differently, changing the distance between the protein and the grid
boundary, etc.) will affect the mean and standard deviation of the
grid values, which in turn will affect the threshold chosen to
extract pockets. For example, simply making the extent of the grid
10% greater will include a large number of near-zero values in the
grid, which will bring the threshold down and make the extracted
pockets larger. Also, no control is provided over the number and
size of pockets; it is possible that for some proteins no grid values
are 4.6 standard deviations above the mean, in which case no
pockets will be predicted.
It is difficult to control the number, sizes, and shapes of
extracted pockets with Threshold and StdDev. In both methods a
threshold is applied to every grid point independently and clusters
are formed only on the basis of geometric proximity between grid
points, so it is possible to extract a set of pockets that have
biologically implausible shapes. For example, there is no way to
guarantee that the algorithm won’t extract one very large pocket
that covers a significant fraction of the protein surface, or many
small pockets distributed across the protein surface, and/or
pockets that contain long, thin regions where the cross-sectional
diameter is too small to fit a bound ligand. Of course, it is possible
to trim/discard such pockets after they have been extracted
according to geometric criteria using post-processing algorithms
[1,23,56]. However, unless there is feedback between the method
used to select a grid threshold and the method used to cull pockets,
then there is no way to guarantee that a biologicaly plausible set of
pockets is output, i.e., it is possible that none of the pockets
extracted with the chosen grid threshold meet the culling criteria.
In ConCavity, we integrate extraction and culling of pockets into
a single framework. We perform a binary search for the grid
threshold that produces a culled set of pockets that have specified
properties (maximum number of pockets, total volume of all
pockets, minimum volume for any pocket, minimum cross-
sectional radius for any pocket, and maximum distance from
protein surface). Specifically, for each step of the binary search, we
select a grid threshold, extract a set of pockets (connected
components of grid points having values above the threshold),
and then apply a sequence of culling algorithms to trim/discard
pockets based their sizes and shapes. The algorithm iterates,
adjusting the threshold up or down, if the set of pockets resulting
from the culling operations does not meet the specified global
properties. The binary search terminates when it has found a set of
pockets meeting all of the specified properties or determines that
none is possible. We call this method ‘‘Search’’.
Specifically, the culling steps are implemented with a series of
grid-based filters, each of which runs in compute time that grows
linearly with the size of the grid. Given a current guess for the grid
threshold, the first filter simply zeroes all grid points whose value is
below the threshold value.
The second filter zeroes grid points whose distance from the van
der Waal’s surface of the protein exceeds a given threshold,
max_protein_offset. This filter is computed by first rasterizing a
sphere for all atoms of the protein into a grid, setting every grid
point within the van der Waal’s radius of any protein atom to one
and the others to zero. Then, the square of the distance from each
grid point to the closest point on the van der Waal’s surface is
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Page 13
computed with three linear-time sweeps, and the resulting squared
distances are used to zero grid points of the original grid if the
squared distance is greater than max_protein_offset
The third filter ensures that no part of a pocket has cross-
sectional radius less than a given threshold, min_pocket_radius. This
filter is implemented with an ‘‘opening’’ operator from mathe-
matical morphology. Intuitively, the boundary of every pocket
(non-zero values of the grid) is ‘‘eroded’’ by min_pocket_radius and
then ‘‘dilated’’ by the same amount, causing regions with cross-
sectional radius less than the threshold to be removed, while the
others are unchanged. This operator is implemented with two
computations of the squared distances from pocket boundaries,
each of which takes linear time in the size of the grid.
The fourth filter constructs connected components of the grid
and then zeros out grid points within components whose volume is
less than a given threshold, min_pocket_volume. Connected compo-
nents are computed with a series of depth-first traversals of
neighboring non-zero grid points, which take linear time all
together, and pockets are sorted by volume using quicksort, which
takes O plogp
time for p pockets.
After these filters are executed for each iteration, the total
volume of all remaining pockets is computed and compared to a
given target volume, total_pocket_volume. If the total volume is
greater (less) than the target, the grid threshold is increased
(decreased) to a value half-way between the current threshold and
the maximum (minimum) possible threshold---initially the largest
(smallest) value in the grid---and the minimum (maximum) is set to
the current threshold. The process is repeated with the new
threshold until the total volume of all pockets is within e of the
given total_pocket_volume. Note that we perform a 1 A
Gaussian blur
on the Ligsite grid before beginning this search to provide finer
control over the predicted pockets than is provided by the Ligsite
integer grid values.
We set the parameters for these filters empirically. In previous
studies, it has been observed that the vast majority of bound ligand
atoms reside within 5 A
of the protein’s van der Waal’s surface,
thus we set max_protein_offset to 5 A
. In order to target binding sites
for biologically relevant ligands, we set min_pocket_radius to 1 A
min_pocket_volume to 100 A
. Based on the observation that the total
volume of all bound ligands is roughly proportional to the total
volume of the protein [17], we set total_pocket_volume to a given
fraction of the total protein volume---2% in our studies (Text S1).
Finally, we set the grid resolution to 1 A
and e to 1 A
Performance. To assess the impact of different pocket extraction
strategies on the precison and accuracy of binding site detection,
we implemented several alternative methods and compared how
well the pockets they predict overlap with ligands in holo
structures from the LigASite dataset. Table 6 shows the results
of several pocket extraction algorithms (second column) on three
different grids types (first column). In addition to Thresh and StdDev,
Largest(N) refers to zeroing all grid entries not in the largest N
pockets (connected components).
The statistics presented in Table 6 reflect various attributes of
the pockets predicted by each extraction technique. The Jaccard
coefficient (Intersection/Union) ranges between zero and one and
takes into account the natural tradeoff between recall and
precision by rewarding predictions that overlap the known ligands
(large Intersection) and penalizing methods that predict very large
pockets (large Union). Thus, it is a suitable measure for comparing
the overall performance of the pocket extraction methods. For
example, though the pockets of PocketFinder
with the StdDev
(meanzs) extraction method have very high recall (0.900), its
Jaccard coefficient is very low, because the predicted pockets have
a very large average volume (49x more than the ligands). For each
grid type, our Search pocket extraction method predicts pockets
with volumes close to the actual ligand volume and obtains the
best Jaccard coefficient. As a result, we use Search in ConCavity and
our implementations of previous grid based methods.
Residue mapping. The third step of our pipeline uses the
extracted set of pockets to generate ligand-binding predictions for
residues. Our goal is to score every residue based on its
relationship to the extracted pockets such that residues with
Table 6. Comparison of pocket extraction methods.
Grid Generation Pocket Extraction
Vol. (A
\ w/Lig.
Ligand \\/Prediction \\/Ligand
Thresh(6) 9385.9 767.3 10595.8 4.577 0.106 0.360 0.085
Thresh(6), Largest(3) 5919.7 674.8 7222.1 2.281 0.200 0.322 0.129
Search 1806.8 426.9 3357.1 1.250 0.332 0.338 0.197
- 44242.4 1729.3 44490.3 29.003 0.045 0.896 0.044
Search 1766.3 426.3 3317.2 1.218 0.300 0.287 0.166
69477.5 1742.1 69712.6 49.250 0.028 0.900 0.028
18317.1 1218.4 19075.9 12.026 0.094 0.652 0.085
8303.7 896.4 9384.4 5.117 0.170 0.489 0.130
3703.0 591.3 5088.8 2.150 0.270 0.326 0.148
Search 1807.0 436.0 3348.2 1.250 0.303 0.292 0.167
For three types of grids (first column), we ran different pocket extraction algorithms (second column) and compared how well the pockets overlap bound ligands in
holo PQS structures. The third column (‘‘Prediction Vol.’’) lists the average volume of all predicted pockets over each protein. For reference, the average volume of all
ligands observed in the PQS files (‘‘Ligand’’) is 1977.2 A
. The next two columns list the average volumes of the Intersection (Ligand \ Prediction) and Union (Ligand |
Prediction) of the Prediction and Ligand grids. Finally, the rightmost four columns list the average over-prediction factor (Prediction/Ligand), precision (Intersection/
Prediction), recall (Intersection/Ligand), and Jaccard coefficient (Intersection/Union). For the last three columns, values range between zero and one, and higher values
represent better performance. Comparing the average volume of the pockets predicted by each method, we see that Search’s pockets are closest to the actual ligand
volumes. Moreover, Search’s high Jaccard coefficient for each grid type indicates that it provides the best tradeoff between recall and precision among the methods
Ligand Binding Site Prediction with ConCavity
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Page 14
higher scores are more likely to bind ligands. This goal is more
ambitious than that of previous residue mapping approaches
which have sought only to identify the residues associated with
predicted pockets.
Perhaps the simplest and most common previous method is to
mark all residues within some distance threshold, d, of any pocket
as binding (e.g., score = 1) and the rest as not binding (e.g., score
= 0) [25]. We call this method ‘‘Dist-01.’’ Both the pocket surface
and geometric center have been taken as the reference point
previously; we use the pocket surface in Dist-01. This approach
ignores all local information about the predicted pockets. Two
related methods have incorporated attributes of predicted pockets
into Dist-01. The first assigns residues near pockets scores that
reflect the size of the closest pocket (‘‘Dist-Size’’) [1]; residues near
the largest pocket receive the highest score and so on. A similar
approach uses the average conservation of all residues near the
pocket (‘‘Dist-Cons’’) [1] to rank the pockets and assign rank-based
scores to residues.
In ConCavity, our goal is to assign scores to residues based on
their likelihood of binding a ligand. We use the original grid values
(which reflect the predicted likelihood of a ligand at every point in
space) to weight the scores assigned to nearby residues. Starting
with the grid values within the set of extracted pockets, we blur this
grid with a Gaussian filter (s~4A
), and then assign to every
residue the maximum grid value evaluated at the location of any of
its atoms. This method, which we call ‘‘Blur,’’ assigns residues in
the same pocket different scores, since some residues are in the
middle of a binding site, next to the part of a pocket with highest
grid values, while others are at the fringe of a site, near marginal
grid values. The score assigned by Blur reflects these differences in
the likelihood that an individual residue is ligand binding.
In contrast to Blur, none of the previous residue extraction
methods give different scores to residues in the same pocket. For
comparison, we developed a version of the Dist strategy that (like
Blur) considers the original grid values. Dist-Raw simply assigns to
each residue within d of a pocket, the value of the nearest pocket
grid point.
Performance. We analyze the performance of these residue
mapping approaches by comparing their PR-AUC on the task
of predicting ligand binding residues as defined in the LigASite
apo dataset. In each case we start with the same grid of extracted
pockets and apply a different residue mapping algorithm. We
consider all residue mapping strategies on three different pocket
grids: ConCavity
, ConCavity
, and ConCavity
. For all Dist approach-
es, we set d to 5 A
, and for Dist-Cons we consider the conservation
of all residues within 8 A
of the pocket (as in [1]).
The results presented in Table 7 demonstrate that Blur provides
better performance for each grid type than all versions of previous
residue mapping approaches. Thus, we use Blur in ConCavity and
our implementations of previous ligand binding site prediction
algorithms. The two methods that assign residues scores based on
the values of nearby grid points (Blur and Dist-Raw) provide better
performance in each case than those that assign all residues in a
pocket the same score based on a global property of the pocket
(Dist-Size and Dist-Cons). This suggests that the local environment
around residues should be considered when predicting binding
Previous methods
We have compared ConCavity to several methods for ligand
binding site prediction. Many of these methods lack publicly
accessible implementations, and those that are available output
different representations of predicted pockets and residues. In this
section, we describe of how we generate predictions for all
previous methods considered in our evaluations. In some cases we
have completely reimplemented strategies and in others we have
post-processed the output of existing implementations. Table 8
provides a summary of these details. As mentioned earlier, a ‘‘+’’
appended to the method name indicates that it is (at least in part)
our implementation, e.g., Ligsite
, Surfnet
, and Pocketfinder
. We developed new
implementations of the Ligsite, Surfnet, and Pocketfinder methods for
grid generation. This was necessary to allow us to fully integrate
sequence conservation with these methods. However, it also enabled
us to investigate the the effect of different pocket extraction and
residue mapping algorithms on overall performance.
By default, we use Search to extract pockets and Blur to map to
residues for Ligsite
, Surfnet
, and Pocketfinder
, because as was shown
above, these approaches yield the best performance. Our
implementations output representations of the predicted ligand
binding pockets and ranked lists of contacting residues, so they can
be included in both pocket and residue-based evaluations.
LigsiteCS, Q-SiteFinder, and CASTp. For our experiments,
we generate binding site predictions using three publicly available
web servers: LigsiteCS [1], QSiteFinder [25], and CASTp [19]. Each
of these servers produces a list of predicted pockets represented by
sets of residues. In each case, the residues do not have scores
associated with them. Thus to include these methods in the ligand
binding residue prediction evaluation, we must assign scores to the
residues. We tried two approaches. The first assigns all predicted
residues a score of one and all others a score of zero. The second
ranks the residues by the highest ranking pocket to which they are
assigned, i.e., all residues from the first predicted pocket are given
a higher score than those from the second and so on. These
approaches are similar to the residue mapping algorithms
discussed in the ConCavity section above; however, those exact
algorithms could not be applied here because the web servers do
not provide representations of the full extent of predicted pockets.
We found that residue ranking produces better results (data not
shown), so we use this approach. We consider the default number
of pockets predicted by each method: LigsiteCS returns three
pockets; Q-SiteFinder returns ten pockets; and CASTp returns a
variable number. The Q-SiteFinder web server would not accept
proteins with more than 10,000 atoms.
LigsiteCS, Q-SiteFinder, and CASTp do not provide a representa-
tion of each predicted pocket’s full extent, so they could not be
included in the ligand location prediction evaluation.
. The LigsiteCSC method is an extension of
LigsiteCS that uses the evolutionary sequence conservation of
residues surrounding predicted pockets to reorder the pocket
predictions. This feature on the LigsiteCS prediction server did not
Table 7. Comparison of residue mapping strategies.
Pocket Grid Source
Mapping Method
Blur 0.608 0.602 0.587
Dist-Raw 0.477 0.553 0.509
Dist-Size 0.442 0.486 0.474
Dist-Cons 0.426 0.473 0.437
Dist-01 0.404 0.455 0.414
We applied five residue mapping algorithms to three grids of predicted pockets
, ConCavity
, ConCavity
). This table lists the PR-AUC for identifying
ligand binding residues in the LigASite apo dataset for each combination. Our
Blur algorithm achieves the best performance for each grid type.
Ligand Binding Site Prediction with ConCavity
PLoS Computational Biology | 15 December 2009 | Volume 5 | Issue 12 | e1000585
Page 15
work for many PQS structures in our dataset, so we implemented
our own version on top of the LigsiteCS results. For each pocket, we
calculate the average conservation of all residues within 8 A
of the
pocket center. The JSD method on the HSSP alignments is used to
produce the conservation scores. The top three pockets in terms of
size are then ranked in terms of average conservation. This
implementation follows the published description of LigsiteCSC,
except for the use of JSD for conservation instead of ConSurf.
Jensen-Shannon Divergence. The Jensen-Shannon divergence
(JSD) is used to represent the performance of evolutionary sequence
conservation; it was recently shown to provide state-of-the-art
performance on a range of functional site prediction tasks [2]. It
compares the amino acid distribution observed in columns of a
multiple sequence alignment of homologs to a background
distribution. JSD scores range between zero and one. The code
provided in Capra and Singh [2] with the default sequence weighting
and gap penalty was used to score all alignments.
The prediction methods described in this paper take protein 3D
structures and/or multiple sequence alignments as input. Protein
structures were downloaded from the Protein Quaternary
Structure (PQS) server [58]. Predicted quaternary structures were
used (rather than the tertiary structures provided in PDB files) so as
to consider pockets and protein-ligand contacts for proteins in
their biologically active states. All alignments come from the
Homology-derived Secondary Structure of Proteins (HSSP)
database [70]. All images of 3D structures were rendered with
PyMol [71].
Ligand binding sites as defined by the non-redundant version of
the LigASite dataset (v7.0) [24] were used to evaluate method
predictions. This set consists of 337 proteins with apo (unbound)
structures, each having less than 25% sequence identity with any
other protein in the set. Five of the 337 structures were left out of
the evaluation: 1P5T, 1YJG, and 3DL3 lacked holo ligand
information in the database, and 2PCY and 3EZM, because their
corresponding holo structures are not in PQS or HSSP. Each apo
structure has at least one associated holo (bound) structure in
which biologically relevant ligands are identified in order to define
ligand binding residues and map them to the apo structure. If
multiple holo structures are available for the protein, the sets of
contacting residues are combined to define the binding residues for
the apo structure. We select the structures for our LigASite holo
evaluation set by taking the holo structure with the most ligand
contacting residues for each apo structure. The average number of
holo structures for each apo structure is 2.58, and the maximum
for any single structure is 32. The average chain length is 276
residues with a minimum of 59 and a maximum of 1023. The
average number of positives---sites contacting a biologically
relevant ligand---per chain is 25 residues (about 11% of the
chain). The apo dataset includes many proteins with multiple
chains; the average number of chains per protein is 2.22. The
chain distribution is: 1 chain: 143, 2 chains: 112, 3 chains: 18, 4
chains: 35, 5 or more chains: 24.
The drug dataset comes from a set of 100 non-redundant 3D
structures selected by [62]. This set contains a diverse set of high-
quality structures (resolution ,3A
) with drug or drug-like
molecules (molecular weight between 200 and 600, and 1212
rotatable bonds) bound. Structure 1LY7 has been removed from
the PDB, and 1R09 could not be parsed. We consider the 98
remaining structures.
The catalytic site annotations were taken from version 2.2.9 of
the Catalytic Site Atlas [66]. There are 153 proteins in the
LigASite apo dataset with entries in the Catalytic Site Atlas. These
proteins have an average of 3.2 catalytic sites per chain (just over
1% of all residues in the chain).
Predictions of ligand binding pockets are represented by non-
zero values in a regular 3D grid around the protein. These
represent regions in space thought to contain ligands. These
predictions are evaluated in two ways: on the pocket level by
computing their overlap with known ligands, and on the grid level
by analyzing how well the grid scores rank grid points that overlap
ligand atoms. We use a grid with rasterized van der Waals spheres
for ligand atoms from the PQS structure as the ‘‘positive’’ set of
Table 8. Implementation Details of Evaluated Methods.
Prediction Algorithm Steps
Name Grid Creation Pocket Extraction Residue Mapping Post-processing
Ligsite+Cons Search Blur -
PocketFinder+Cons Search Blur -
Surfnet+Cons Search Blur -
Ligsite Search Blur -
PocketFinder Search Blur -
Surfnet Search Blur -
LigsiteCS Residues Ranked by Pocket Rank
+ Residues Ranked by Pocket Conservation
This table summarizes the details of each step of the ligand binding site prediction process for the methods we evaluate. The new ConCavity methods are based entirely
on our code. We also developed new implementations (Ligsite
, PocketFinder
, and Surfnet
) of three previous methods. Predictions for the other previous methods
were obtained from the listed publicly accessible web servers. These servers output sets of residues associated with predicted binding pockets. For inclusion in the
residue prediction evaluation, the output of these servers was post-processed as specified. This step is not necessary for our methods, because Blur outputs ranked
residue predictions. A ‘‘+’’ appended to the method name indicates that it is based (at least in part) on our code. Implementation details of each algorithm are given in
the text, and code for our implementations is available online.
Ligand Binding Site Prediction with ConCavity
PLoS Computational Biology | 16 December 2009 | Volume 5 | Issue 12 | e1000585
Page 16
grid points. From this, we calculate the intersection and union of
the actual ligand atoms and the predictions. We compare methods
using the over-prediction factor (Prediction Volume/Ligand
Volume), precision (Intersection Volume/Prediction Volume),
recall (Intersection Volume/Ligand Volume), and Jaccard coeffi-
cient (Intersection Volume/Union Volume).
We also create precision-recall (PR) curves, which compare
precision (TP/(TP + FP)) on the y-axis with recall (TP/(TP + FN))
on the x-axis, to evaluate the ability of each method to predict
whether a ligand atom is present at a grid point. We consider grid
points that overlap a ligand atom as positives. To construct the PR
curve, we calculate the precision and recall at each cutoff of the
grid values in the pocket prediction grid. To summarize the
performance of each method, we construct a composite PR curve
[72] by averaging the precision at each recall level for each
structure in the dataset. As a reference point, we include the
performance of a random classifier averaged over all the structures
as well. The expected performance of a random method is the
number of positives over the number of all grid points. The
method and code of Davis and Goadrich [73] is used to calculate
the area under the PR curve (PR-AUC). The significance of the
difference between methods is assessed using the Wilcoxon signed-
rank test over paired performance statistics for all structures in the
dataset. The significance of the difference in performance of a
single method on different datasets is calculated with the Wilcoxon
rank-sum test.
For the residue-based evaluation, we consider how well each
method’s residue scores identify ligand binding residues. Positives
are those residues in contact with a ligand as defined by LigASite
database. PR curves were made by calculating, for each chain, the
precision and recall at each position on the ranked list of residue
scores. Composite PR curves were computed as described for the
grid point evaluation, but curves were first averaged over the
chains in a structure and then over structures. PR curves were
constructed similarly for the catalytic site analysis, but positives
were defined as those residues listed in the Catalytic Site Atlas.
Supporting Information
Text S1 Supplementary text, results, and analysis.
Found at: doi:10.1371/journal.pcbi.1000585.s001 (0.39 MB PDF)
Author Contributions
Conceived and designed the experiments: JAC MS TAF. Performed the
experiments: JAC TAF. Analyzed the data: JAC MS TAF. Wrote the
paper: JAC MS TAF. Provided methodological input: RAL JMT.
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  • Source
    • "Further, sequence conservation-based predictors do not require structural information [14,22,36] . However, since evolutionary methods are non-specific enough to be used for identification of any type of functional site165166167168169, this lack of specialization may reduce performance for PPIS prediction specifically [4] . Unsurprisingly, conservation is not helpful for interfaces selected by nonevolutionary means, such as antigen-antibody complexes [16,102] (which require separate specialized predictors [170]). "
    [Show abstract] [Hide abstract] ABSTRACT: Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.
    Full-text · Article · Dec 2015 · Algorithms for Molecular Biology
    • "Functionally important residues were predicted for proteins of known function as well as SG proteins using the POOL method [20] [21], with electrostatic and chemical properties from THE- MATICS [31] [32], phylogenetic tree information from INTREPID 2 R. Parasuram et al. / Methods xxx (2015) xxx–xxx Please cite this article in press as: R. Parasuram et al., Methods (2015), [33] [34], and geometric features from ConCavity [35] as input (Fig. 1, left). "
    [Show abstract] [Hide abstract] ABSTRACT: Thousands of protein structures of unknown or uncertain function have been reported as a result of high-throughput structure determination techniques developed by Structural Genomics (SG) projects. However, many of the putative functional assignments of these SG proteins in the Protein Data Bank (PDB) are incorrect. While high-throughput biochemical screening techniques have provided valuable functional information for limited sets of SG proteins, the biochemical functions for most SG proteins are still unknown or uncertain. Therefore, computational methods for the reliable prediction of protein function from structure can add tremendous value to the existing SG data. In this article, we show how computational methods may be used to predict the function of SG proteins, using examples from the six-hairpin glycosidase (6-HG) and the concanavalin A-like lectins/glucanases (CAL/G) superfamilies. Using a set of predicted functional residues, obtained from computed electrostatic and chemical properties for each protein structure, it is shown that these superfamilies may be sorted into functional families according to biochemical function. Within these superfamilies, a total of 18 SG proteins were analyzed according to their predicted, local functional sites: 13 from the 6-HG superfamily, five from the CAL/G superfamily. Within the 6-HG superfamily, an uncharacterized protein bacova_03626 from Bacteroides ovatus (PDB 3ON6) and a hypothetical protein BT3781 from Bacteroides thetaiotaomicron (PDB 2P0V) are shown to have very strong active site matches with exo-α-1,6-mannosidases, thus likely possessing this function. Also in this superfamily, it is shown that protein BH0842, a putative glycoside hydrolase from Bacteroides halodurans (PDB 2RDY), has a predicted active site that matches well with a known α-L-galactosidase. In the CAL/G superfamily, an uncharacterized glycosyl hydrolase family 16 protein from Mycobacterium smegmatis (PDB 3RQ0) is shown to have local structural similarity at the predicted active site with the known members of the GH16 family, with the closest match to the endoglucanase subfamily. The method discussed herein can predict whether an SG protein is correctly or incorrectly annotated and can sometimes provide a reliable functional annotation. Examples of application of the method across folds, comparing active sites between two proteins of different structural folds, are also given.
    No preview · Article · Nov 2015 · Methods
  • Source
    • "In LIGSITE csc [14] , a sequence conservation measure of neighboring residues was used to re-rank top-3 putative pockets calculated by LIGSITE cs , which lead to an improved success rate (considering top-1 pocket). In ConCavity [10] , unlike in LIGSITE csc , the sequence conservation information is used not only to re-rank pockets, but it is also integrated directly into the pocket detection procedure. An example of an evolutionary based method which takes into account the structural information is FINDSITE [22,23]. "
    [Show abstract] [Hide abstract] ABSTRACT: Protein-ligand binding site prediction from a 3D protein structure plays a pivotal role in rational drug design and can be helpful in drug side-effects prediction or elucidation of protein function. Embedded within the binding site detection problem is the problem of pocket ranking - how to score and sort candidate pockets so that the best scored predictions correspond to true ligand binding sites. Although there exist multiple pocket detection algorithms, they mostly employ a fairly simple ranking function leading to sub-optimal prediction results. We have developed a new pocket scoring approach (named PRANK) that prioritizes putative pockets according to their probability to bind a ligand. The method first carefully selects pocket points and labels them by physico-chemical characteristics of their local neighborhood. Random Forests classifier is subsequently applied to assign a ligandability score to each of the selected pocket point. The ligandability scores are finally merged into the resulting pocket score to be used for prioritization of the putative pockets. With the used of multiple datasets the experimental results demonstrate that the application of our method as a post-processing step greatly increases the quality of the prediction of Fpocket and ConCavity, two state of the art protein-ligand binding site prediction algorithms. The positive experimental results show that our method can be used to improve the success rate, validity and applicability of existing protein-ligand binding site prediction tools. The method was implemented as a stand-alone program that currently contains support for Fpocket and Concavity out of the box, but is easily extendible to support other tools. PRANK is made freely available at
    Full-text · Article · Apr 2015 · Journal of Cheminformatics
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