Proteins form the executive machinery underlying all
the biological processes that occur within and between cells, from
DNA replication to protein degradation. Although genome-scale
technologies enable to clarify their large, intricate and highly
dynamics networks, they fail to elucidate the detailed molecular
mechanism that underlies the protein association process. Therefore,
one of the most challenging objectives in biological research is to
functionally characterize protein interactions by solving 3D complex
structures.
This is, however, not a trivial task as confirmed by the large gap
that exist between the number of complexes identified by large-scale
proteomics efforts and those for which high-resolution 3D
experimental structures are available. For these reasons,
computational docking methods, aimed to predict the binding mode
of two proteins starting from the coordinates of the individual
subunits, are bound to become a complementary approach to solve
the structural interactome.
Given its importance, the field of protein docking has
experienced an explosion in recent years partially propelled by
CAPRI (http://www.ebi.ac.uk/msd-srv/capri/). CAPRI (Critical
Assessment of PRedicted Interaction) is a community-wide blind
experiment aimed at objectively assessing the performance of
computational methods for modeling protein interactions by inviting
developers to test their algorithms on the same target system and
quantitatively evaluating the results.
In order to test pyDock,1 a docking scoring algorithm developed
in our group, the PID (Protein Interaction and Docking) group of the
BSC Life Science Department, we have participated in all the 15
targets (T46 to T58) of the 5th CAPRI edition (2010-2012). Our
automated protocol confirmed to be highly successful to provide
correct models in easy-to-medium difficulty protein-protein docking
cases placing among the Top5 ranked groups out of more than 60
participants.
Key words: Complex structure, CAPRI, protein-protein
docking, pyDock, protein interactions

Fernandez-Recio, Juan

Jiménez-García, Brian

Pallara, Chiara

Romero, Miguel

Proteins form the executive machinery underlying all

the biological processes that occur within and between cells, from

DNA replication to protein degradation. Although genome-scale

technologies enable to clarify their large, intricate and highly

dynamics networks, they fail to elucidate the detailed molecular

mechanism that underlies the protein association process. Therefore,

one of the most challenging objectives in biological research is to

functionally characterize protein interactions by solving 3D complex

structures.

This is, however, not a trivial task as confirmed by the large gap

that exist between the number of complexes identified by large-scale

proteomics efforts and those for which high-resolution 3D

experimental structures are available. For these reasons,

computational docking methods, aimed to predict the binding mode

of two proteins starting from the coordinates of the individual

subunits, are bound to become a complementary approach to solve

the structural interactome.

Given its importance, the field of protein docking has

experienced an explosion in recent years partially propelled by

CAPRI (http://www.ebi.ac.uk/msd-srv/capri/). CAPRI (Critical

Assessment of PRedicted Interaction) is a community-wide blind

experiment aimed at objectively assessing the performance of

computational methods for modeling protein interactions by inviting

developers to test their algorithms on the same target system and

quantitatively evaluating the results.

In order to test pyDock,1 a docking scoring algorithm developed

in our group, the PID (Protein Interaction and Docking) group of the

BSC Life Science Department, we have participated in all the 15

targets (T46 to T58) of the 5th CAPRI edition (2010-2012). Our

automated protocol confirmed to be highly successful to provide

correct models in easy-to-medium difficulty protein-protein docking

cases placing among the Top5 ranked groups out of more than 60

participants.

Key words: Complex structure, CAPRI, protein-protein

docking, pyDock, protein interactions

UPCommons

  
 
 
~ 99 ~ 
pyDock performance in 5th CAPRI edition:  
from docking and scoring to binding affinity predictions and other challenges 
 
Chiara Pallara, Brian Jiménez-García, Miguel Romero, and Juan Fernandez-Recio  
Joint BSC-IRB Research Programme in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain  
chiara.pallara@bsc.es 
Abstract- Proteins form the executive machinery underlying all 
the biological processes that occur within and between cells, from 
DNA replication to protein degradation. Although genome-scale 
technologies enable to clarify their large, intricate and highly 
dynamics networks, they fail to elucidate the detailed molecular 
mechanism that underlies the protein association process. Therefore, 
one of the most challenging objectives in biological research is to 
functionally characterize protein interactions by solving 3D complex 
structures.  
This is, however, not a trivial task as confirmed by the large gap 
that exist between the number of complexes identified by large-scale 
proteomics efforts and those for which high-resolution 3D 
experimental structures are available. For these reasons, 
computational docking methods, aimed to predict the binding mode 
of two proteins starting from the coordinates of the individual 
subunits, are bound to become a complementary approach to solve 
the structural interactome.  
Given its importance, the field of protein docking has 
experienced an explosion in recent years partially propelled by 
CAPRI (http://www.ebi.ac.uk/msd-srv/capri/). CAPRI (Critical 
Assessment of PRedicted Interaction) is a community-wide blind 
experiment aimed at objectively assessing the performance of 
computational methods for modeling protein interactions by inviting 
developers to test their algorithms on the same target system and 
quantitatively evaluating the results. 
In order to test pyDock,1 a docking scoring algorithm developed 
in our group, the PID (Protein Interaction and Docking) group of the 
BSC Life Science Department, we have participated in all the 15 
targets (T46 to T58) of the 5th CAPRI edition (2010-2012). Our 
automated protocol confirmed to be highly successful to provide 
correct models in easy-to-medium difficulty protein-protein docking 
cases placing among the Top5 ranked groups out of more than 60 
participants. 
Key words: Complex structure, CAPRI, protein-protein 
docking, pyDock, protein interactions. 
 
I. INTRODUCTION 
One of the major challenges in structural biology is to 
provide structural data for all complexes formed between 
proteins and other macromolecules. Current structural 
coverage of protein-protein interactions (i.e. available 
experimental structures plus potential models based on 
homologous complex structures) is below 4% of the estimated 
number of possible complexes formed between human 
proteins.2 The pace of experimental determination of complex 
structures is still behind the determination of individual 
protein structures. In addition, many of these interactions will 
never be determined by x-ray crystallography because of their 
transient nature. For these reasons, computational docking 
methods aim to become a complementary approach to solve 
the structural interactome. The field of protein docking has 
experienced an explosion in recent years, partially propelled 
by the CAPRI (http://www.ebi.ac.uk/msd-srv/capri/). CAPRI 
(Critical Assessment of PRedicted Interaction) is a 
community-wide blind experiment aimed at objectively 
assessing the performance of computational methods for 
modeling protein interactions by inviting developers to test 
their algorithms on the same target system and quantitatively 
evaluating the results. This involves sampling putative 
association modes and modeling their atomic structure (the 
docking problem), and identifying those likely to be stable out 
of a very large pool of decoys (the scoring problem). Models 
submitted by participants are finally evaluated in comparison 
with experimental coordinates made available by their authors 
to the CAPRI assessors according to some criteria as described 
in Figure 1 of Lensink et al. Proteins 2007 69:704. 
In order to test pyDock,1 a docking scoring algorithm 
developed in our group, we have participated in all the 15 
targets of the 5th CAPRI edition (2010-2012). In addition to 
the standard prediction of protein-protein targets, this edition 
has entered into related areas including binding affinity 
predictions and free energy changes upon mutation, as well as 
prediction of sugar binding and interface water molecules. Our 
overall experience has been highly rewarding and we describe 
here the details of our participation and the key factors of our 
success. 
 
II. MATERIALS AND METHODS 
A. Generation of rigid-body docking poses for the 
predictors experiment 
In all targets, we used FTDock3 and ZDOCK 2.14 to generate 
10,000 and 2,000 rigid-body docking poses, respectively. For 
the final four targets of this edition (T53, T54, T57 and T58) 
we generated an additional pool of flexible docking poses 
using SwarmDock. 5 
B. Scoring of rigid-body docking poses for both the 
predictors and the scorers experiment 
We scored the docking models generated by the above 
described methods with our pyDock protocol, based on energy 
terms previously optimized for rigid-body docking. The 
binding energy is basically composed of ASA-based 
desolvation, Coulombic electrostatics and van der Waals energy 
(with a weighting factor of 0.1 to reduce the noise of the scoring 
function). Cofactors, water molecules and solvent ions were not 
considered for scoring. 
C. Removal of redundant docking poses 
After scoring, we eliminated redundant predictions in order 
to increase the variability of the predictions and maximize the 
success chances by using a simple clustering algorithm with a 
distance cutoff of 4.0 Å, as previously described. 6 
D. Minimization of final models 
The final ten selected docking poses were minimized in order 
to improve the quality of the docking models and reduce the 
number of interatomic clashes. In the majority of the targets 
we used TINKER. In targets T53 and T54 we used 
  
 
 
~ 100 ~ 
CHARMM7 while in target T58 we used AMBER10 with 
AMBER parm99 forcefield. 8 
E. Modeling of subunits with no available structure 
For several targets, the structures of the subunits were not 
available and needed to be modeled. We used Modeller 9v69 
with default parameters based on the template/s suggested by 
the organizers or on other homologue proteins found by 
BLAST search (http://blast.ncbi.nlm.nih.gov/Blast.cgi). The 
final selected model was that with the lowest DOPE score.  
III. RESULTS AND DISCUSSION 
In this CAPRI edition we submitted predictions for all the 
proposed targets. Our results for the standard protein-protein 
docking assessment are summarized in Table I and Fig.1.  
TABLE I 
Target Typea Predictors 
Submission 
rankb 
Qualityc Successful 
Groupsd 
T46 HH - - 2 (40) 
T47 HU 1 *** 25 (29) 
T48 UU 3 * 14 (32) 
T49 UU 4 * 14 (33) 
T50 UH 1 ** 18 (40) 
T51 DHD - - 3 (46) 
T53 UH 3f ** 20 (42) 
T54 UH - -
 
4 (41) 
T58 UU 5 ** 11 (23) 
Table I. Results of pyDock protocol for all protein-protein targets in 
predictors. 
a B: bound; U: unbound; H: homology-based model. 
b Rank of the best model within our submission to CAPRI. 
c Quality of our best model according to CAPRI criteria: acceptable (*), 
medium (**), and high (***) 
d Number of successful groups for each target; in brackets, total number of 
participants. 
e Model rank 1 had acceptable accuracy (*). 
 
 
Fig. 1. Representation of our best models for targets T47, T48, T49, T50, T53, 
T57 and T58. For each target, receptors are superimposed and shown in white. 
Ligand in our best model as predictors is shown in red, and as scorers in blue. 
For comparison, the structure of the experimental complex (if available) is 
represented in green. 
For the generation of docking poses, the better grid resolution 
used for FTDock and the use of flexible SwarmDock for the 
last targets were key for the success. In selected targets (T47, 
T48 and T58), distance restraints were used, but in most cases 
this did not make a difference. In the target T58, SAXS data 
was used for complementary scoring with pyDockSAXS,10 
which slightly improved the scoring. We obtained consistently 
good models for all non-difficult cases, although they were far 
from being trivial, since their subunits were unbound or 
needed to be modeled based on homology templates. In all 
cases but one our successful models were ranked within our 
first five submitted solutions, being ranked 1st in several 
cases.  
IV. CONCLUSIONS 
In this CAPRI edition we learned that our automated protocol 
is useful to provide correct models in easy-to-medium 
difficulty protein-protein docking cases, but we need further 
methodological development for difficult cases, especially 
when subunits need to be modeled based on homologues with 
low sequence identity. Our overall experience has been highly 
rewarding, pyDock docking scheme confirmed its high 
performance in protein complexes prediction placing among 
the Top5 ranked groups out of more than 60 participants 
(Table II). 
TABLE II 
Rank Group Summary:  
#Targets / *** + ** + * 
1 Bonvin 9 / 1 *** + 3 ** + 5 * 
2 Bates 8 / 2 ** + 6 * 
3 Vakser 7 / 1 *** + 6 * 
4 Vajda 6 / 2 *** + 3 ** +  1 * 
5 Fernandez-Recio 6 / 1 *** + 3 ** + 2 * 
5 Shen 6 / 1 *** + 3 ** + 2 * 
7 Zou 6 / 1 *** + 2 ** + 3 * 
8 Zacharias 6 / 1 *** + 5 * 
9 ClusPro 6 / 4 ** + 2 * 
10 Eisenstein 5 / 1 *** + 2 ** + 2 * 
Table II. Overall pyDock performance among the Top10 ranked groups. 
Predictions are classified as acceptable (*), medium (**), and high (***). 
REFERENCES 
1. Cheng, T. M.-K., Blundell, T. L., & Fernandez-Recio, J. (2007). 
Proteins 68, 503-15. 
2. Venkatesan, K., Rual, J.-F., Vazquez, A., Stelzl, U., Lemmens, I., 
et al. (2009). Nat Methods 6, 83-90. 
3. Gabb, H. A., Jackson, R. M., & Sternberg, M. J. (1997). J Mol Biol 
272, 106-20. 
4. Chen, R., Li, L., & Weng, Z. (2003). Proteins 52, 80-7. 
5. Moal, I. H. & Bates, P. A. (2010). Int J Mol Sci 11, 3623-48. 
6. Pons, C., Solernou, A., Perez-Cano, L., Grosdidier, S., & 
Fernandez-Recio, J. (2010). Proteins 78, 3182-8. 
7. Brooks, B. R., Brooks, 3rd, C. L., Mackerell, Jr, A. D., Nilsson, L., 
et al (2009). J Comput Chem 30, 1545-614. 
8. Case, D. A., Cheatham, 3rd, T. E., Darden, T., Gohlke, H., Luo, R., 
Merz, Jr, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, 
R. J. (2005). J Comput Chem 26, 1668-88. 
9. Sali, A. & Blundell, T. L. (1993). J Mol Biol 234, 779-815. 
10. Pons, C., D'Abramo, M., Svergun, D. I., Orozco, M., Bernadó, P., 
& Fernández-Recio, J. (2010). J Mol Biol 403, 217-30. 


English

pyDock performance in 5th CAPRI edition: from docking and scoring to binding affinity predictions and other challenges

UPCommons. Portal del coneixement obert de la UPC

    ~ 99 ~ pyDock performance in 5th CAPRI edition:  from docking and scoring to binding affinity predictions and other challenges  Chiara Pallara, Brian Jiménez-García, Miguel Romero, and Juan Fernandez-Recio  Joint BSC-IRB Research Programme in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain  chiara.pallara@bsc.es Abstract- Proteins form the executive machinery underlying all the biological processes that occur within and between cells, from DNA replication to protein degradation. Although genome-scale technologies enable to clarify their large, intricate and highly dynamics networks, they fail to elucidate the detailed molecular mechanism that underlies the protein association process. Therefore, one of the most challenging objectives in biological research is to functionally characterize protein interactions by solving 3D complex structures.  This is, however, not a trivial task as confirmed by the large gap that exist between the number of complexes identified by large-scale proteomics efforts and those for which high-resolution 3D experimental structures are available. For these reasons, computational docking methods, aimed to predict the binding mode of two proteins starting from the coordinates of the individual subunits, are bound to become a complementary approach to solve the structural interactome.  Given its importance, the field of protein docking has experienced an explosion in recent years partially propelled by CAPRI (http://www.ebi.ac.uk/msd-srv/capri/). CAPRI (Critical Assessment of PRedicted Interaction) is a community-wide blind experiment aimed at objectively assessing the performance of computational methods for modeling protein interactions by inviting developers to test their algorithms on the same target system and quantitatively evaluating the results. In order to test pyDock,1 a docking scoring algorithm developed in our group, the PID (Protein Interaction and Docking) group of the BSC Life Science Department, we have participated in all the 15 targets (T46 to T58) of the 5th CAPRI edition (2010-2012). Our automated protocol confirmed to be highly successful to provide correct models in easy-to-medium difficulty protein-protein docking cases placing among the Top5 ranked groups out of more than 60 participants. Key words: Complex structure, CAPRI, protein-protein docking, pyDock, protein interactions.  I. INTRODUCTION One of the major challenges in structural biology is to provide structural data for all complexes formed between proteins and other macromolecules. Current structural coverage of protein-protein interactions (i.e. available experimental structures plus potential models based on homologous complex structures) is below 4% of the estimated number of possible complexes formed between human proteins.2 The pace of experimental determination of complex structures is still behind the determination of individual protein structures. In addition, many of these interactions will never be determined by x-ray crystallography because of their transient nature. For these reasons, computational docking methods aim to become a complementary approach to solve the structural interactome. The field of protein docking has experienced an explosion in recent years, partially propelled by the CAPRI (http://www.ebi.ac.uk/msd-srv/capri/). CAPRI (Critical Assessment of PRedicted Interaction) is a community-wide blind experiment aimed at objectively assessing the performance of computational methods for modeling protein interactions by inviting developers to test their algorithms on the same target system and quantitatively evaluating the results. This involves sampling putative association modes and modeling their atomic structure (the docking problem), and identifying those likely to be stable out of a very large pool of decoys (the scoring problem). Models submitted by participants are finally evaluated in comparison with experimental coordinates made available by their authors to the CAPRI assessors according to some criteria as described in Figure 1 of Lensink et al. Proteins 2007 69:704. In order to test pyDock,1 a docking scoring algorithm developed in our group, we have participated in all the 15 targets of the 5th CAPRI edition (2010-2012). In addition to the standard prediction of protein-protein targets, this edition has entered into related areas including binding affinity predictions and free energy changes upon mutation, as well as prediction of sugar binding and interface water molecules. Our overall experience has been highly rewarding and we describe here the details of our participation and the key factors of our success.  II. MATERIALS AND METHODS A. Generation of rigid-body docking poses for the predictors experiment In all targets, we used FTDock3 and ZDOCK 2.14 to generate 10,000 and 2,000 rigid-body docking poses, respectively. For the final four targets of this edition (T53, T54, T57 and T58) we generated an additional pool of flexible docking poses using SwarmDock. 5 B. Scoring of rigid-body docking poses for both the predictors and the scorers experiment We scored the docking models generated by the above described methods with our pyDock protocol, based on energy terms previously optimized for rigid-body docking. The binding energy is basically composed of ASA-based desolvation, Coulombic electrostatics and van der Waals energy (with a weighting factor of 0.1 to reduce the noise of the scoring function). Cofactors, water molecules and solvent ions were not considered for scoring. C. Removal of redundant docking poses After scoring, we eliminated redundant predictions in order to increase the variability of the predictions and maximize the success chances by using a simple clustering algorithm with a distance cutoff of 4.0 Å, as previously described. 6 D. Minimization of final models The final ten selected docking poses were minimized in order to improve the quality of the docking models and reduce the number of interatomic clashes. In the majority of the targets we used TINKER. In targets T53 and T54 we used     ~ 100 ~ CHARMM7 while in target T58 we used AMBER10 with AMBER parm99 forcefield. 8 E. Modeling of subunits with no available structure For several targets, the structures of the subunits were not available and needed to be modeled. We used Modeller 9v69 with default parameters based on the template/s suggested by the organizers or on other homologue proteins found by BLAST search (http://blast.ncbi.nlm.nih.gov/Blast.cgi). The final selected model was that with the lowest DOPE score.  III. RESULTS AND DISCUSSION In this CAPRI edition we submitted predictions for all the proposed targets. Our results for the standard protein-protein docking assessment are summarized in Table I and Fig.1.  TABLE I Target Typea Predictors Submission rankb Qualityc Successful Groupsd T46 HH - - 2 (40) T47 HU 1 *** 25 (29) T48 UU 3 * 14 (32) T49 UU 4 * 14 (33) T50 UH 1 ** 18 (40) T51 DHD - - 3 (46) T53 UH 3f ** 20 (42) T54 UH - - 4 (41) T58 UU 5 ** 11 (23) Table I. Results of pyDock protocol for all protein-protein targets in predictors. a B: bound; U: unbound; H: homology-based model. b Rank of the best model within our submission to CAPRI. c Quality of our best model according to CAPRI criteria: acceptable (*), medium (**), and high (***) d Number of successful groups for each target; in brackets, total number of participants. e Model rank 1 had acceptable accuracy (*).   Fig. 1. Representation of our best models for targets T47, T48, T49, T50, T53, T57 and T58. For each target, receptors are superimposed and shown in white. Ligand in our best model as predictors is shown in red, and as scorers in blue. For comparison, the structure of the experimental complex (if available) is represented in green. For the generation of docking poses, the better grid resolution used for FTDock and the use of flexible SwarmDock for the last targets were key for the success. In selected targets (T47, T48 and T58), distance restraints were used, but in most cases this did not make a difference. In the target T58, SAXS data was used for complementary scoring with pyDockSAXS,10 which slightly improved the scoring. We obtained consistently good models for all non-difficult cases, although they were far from being trivial, since their subunits were unbound or needed to be modeled based on homology templates. In all cases but one our successful models were ranked within our first five submitted solutions, being ranked 1st in several cases.  IV. CONCLUSIONS In this CAPRI edition we learned that our automated protocol is useful to provide correct models in easy-to-medium difficulty protein-protein docking cases, but we need further methodological development for difficult cases, especially when subunits need to be modeled based on homologues with low sequence identity. Our overall experience has been highly rewarding, pyDock docking scheme confirmed its high performance in protein complexes prediction placing among the Top5 ranked groups out of more than 60 participants (Table II). TABLE II Rank Group Summary:  #Targets / *** + ** + * 1 Bonvin 9 / 1 *** + 3 ** + 5 * 2 Bates 8 / 2 ** + 6 * 3 Vakser 7 / 1 *** + 6 * 4 Vajda 6 / 2 *** + 3 ** +  1 * 5 Fernandez-Recio 6 / 1 *** + 3 ** + 2 * 5 Shen 6 / 1 *** + 3 ** + 2 * 7 Zou 6 / 1 *** + 2 ** + 3 * 8 Zacharias 6 / 1 *** + 5 * 9 ClusPro 6 / 4 ** + 2 * 10 Eisenstein 5 / 1 *** + 2 ** + 2 * Table II. Overall pyDock performance among the Top10 ranked groups. Predictions are classified as acceptable (*), medium (**), and high (***). REFERENCES 1. Cheng, T. M.-K., Blundell, T. L., & Fernandez-Recio, J. (2007). Proteins 68, 503-15. 2. Venkatesan, K., Rual, J.-F., Vazquez, A., Stelzl, U., Lemmens, I., et al. (2009). Nat Methods 6, 83-90. 3. Gabb, H. A., Jackson, R. M., & Sternberg, M. J. (1997). J Mol Biol 272, 106-20. 4. Chen, R., Li, L., & Weng, Z. (2003). Proteins 52, 80-7. 5. Moal, I. H. & Bates, P. A. (2010). Int J Mol Sci 11, 3623-48. 6. Pons, C., Solernou, A., Perez-Cano, L., Grosdidier, S., & Fernandez-Recio, J. (2010). Proteins 78, 3182-8. 7. Brooks, B. R., Brooks, 3rd, C. L., Mackerell, Jr, A. D., Nilsson, L., et al (2009). J Comput Chem 30, 1545-614. 8. Case, D. A., Cheatham, 3rd, T. E., Darden, T., Gohlke, H., Luo, R., Merz, Jr, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005). J Comput Chem 26, 1668-88. 9. Sali, A. & Blundell, T. L. (1993). J Mol Biol 234, 779-815. 10. Pons, C., D'Abramo, M., Svergun, D. I., Orozco, M., Bernadó, P., & Fernández-Recio, J. (2010). J Mol Biol 403, 217-30. 

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pyDock performance in 5th CAPRI edition: from docking and scoring to binding affinity predictions and other challenges

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