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Docking and scoring protein complexes: CAPRI 3rd Edition
Abstract
The performance of methods for predicting protein–protein interactions at the atomic scale is assessed by evaluating blind predictions performed during 2005–2007 as part of Rounds 6–12 of the community‐wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). These Rounds also included a new scoring experiment, where a larger set of models contributed by the predictors was made available to groups developing scoring functions. These groups scored the uploaded set and submitted their own best models for assessment. The structures of nine protein complexes including one homodimer were used as targets. These targets represent biologically relevant interactions involved in gene expression, signal transduction, RNA, or protein processing and membrane maintenance. For all the targets except one, predictions started from the experimentally determined structures of the free (unbound) components or from models derived by homology, making it mandatory for docking methods to model the conformational changes that often accompany association. In total, 63 groups and eight automatic servers, a substantial increase from previous years, submitted docking predictions, of which 1994 were evaluated here. Fifteen groups submitted 305 models for five targets in the scoring experiment. Assessment of the predictions reveals that 31 different groups produced models of acceptable and medium accuracy‐but only one high accuracy submission‐for all the targets, except the homodimer. In the latter, none of the docking procedures reproduced the large conformational adjustment required for correct assembly, underscoring yet again that handling protein flexibility remains a major challenge. In the scoring experiment, a large fraction of the groups attained the set goal of singling out the correct association modes from incorrect solutions in the limited ensembles of contributed models. But in general they seemed unable to identify the best models, indicating that current scoring methods are probably not sensitive enough. With the increased focus on protein assemblies, in particular by structural genomics efforts, the growing community of CAPRI predictors is engaged more actively than ever in the development of better scoring functions and means of modeling conformational flexibility, which hold promise for much progress in the future. Proteins 2007. © 2007 Wiley‐Liss, Inc.
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Cites
Global landscape of protein complexes in the yeast Saccharomyces cerevisiae
Krogan, N. J., Cagney, G., Yu, H., Zhong, G., Guo, X., Ignatchenko, A., Li, J., Pu, S., Datta, N., Tikuisis, A. P., et al. (2006) · Nature · doi:10.1038/nature04670 · Cited by 2,952
Self-citationBerman, H. M., Battistuz, T., Bhat, T. N., Bluhm, W. F., Bourne, P. E., Burkhardt, K., Feng, Z., Gilliland, G. L., Iype, L., Jain, S., et al. (2002) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444902003451 · Cited by 2,577
Proteome survey reveals modularity of the yeast cell machinery
Gavin, A.-C., Aloy, P., Grandi, P., Krause, R., Boesche, M., Marzioch, M., Rau, C., Jensen, L. J., Bastuck, S., Dümpelfeld, B., et al. (2006) · Nature · doi:10.1038/nature04532 · Cited by 2,575
The atomic structure of protein-protein recognition sites 1 1Edited by A. R. Fersht
Conte, L. L., Chothia, C., & Janin, J. (1999) · Journal of Molecular Biology · doi:10.1006/jmbi.1998.2439 · Cited by 1,964
Principles of docking: An overview of search algorithms and a guide to scoring functions
Halperin, I., Ma, B., Wolfson, H., & Nussinov, R. (2002) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10115 · Cited by 1,294
Coarse-grained normal mode analysis in structural biology
Bahar, I., & Rader, A. (2005) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2005.08.007 · Cited by 731
CAPRI: A Critical Assessment of PRedicted Interactions
Janin, J., Henrick, K., Moult, J., Eyck, L. T., Sternberg, M. J. E., Vajda, S., Vakser, I., & Wodak, S. J. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10381 · Cited by 703
Self-citationFrom words to literature in structural proteomics
Sali, A., Glaeser, R., Earnest, T., & Baumeister, W. (2003) · Nature · doi:10.1038/nature01513 · Cited by 475
Relating Three-Dimensional Structures to Protein Networks Provides Evolutionary Insights
Kim, P. M., Lu, L. J., Xia, Y., & Gerstein, M. B. (2006) · Science · doi:10.1126/science.1136174 · Cited by 472
Prediction of protein–protein interactions by docking methods
Smith, G. R., & Sternberg, M. J. E. (2002) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(02)00285-3 · Cited by 446
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Kozakov, D., Hall, D. R., Xia, B., Porter, K. A., Padhorny, D., Yueh, C., Beglov, D., & Vajda, S. (2017) · Nature Protocols · doi:10.1038/nprot.2016.169 · Cited by 3,310
The RosettaDock server for local protein-protein docking
Lyskov, S., & Gray, J. J. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn216 · Cited by 592
Variability in docking success rates due to dataset preparation.
Corbeil, C. R., Williams, C. I., & Labute, P. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9570-1 · Cited by 469
DockQ: A Quality Measure for Protein-Protein Docking Models
Basu, S., & Wallner, B. (2016) · PLOS ONE · doi:10.1371/journal.pone.0161879 · Cited by 460
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Janin, J., Bahadur, R. P., & Chakrabarti, P. (2008) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583508004708 · Cited by 398
Molecular docking as a popular tool in drug design, an in silico travel
de Ruyck, J., Brysbaert, G., Blossey, R., & Lensink, M. (2016) · Advances and Applications in Bioinformatics and Chemistry · doi:10.2147/aabc.s105289 · Cited by 346
Self-citationCPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK
de Vries, S. J., & Bonvin, A. M. J. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017695 · Cited by 324
SwarmDock: a server for flexible protein–protein docking
Torchala, M., Moal, I. H., Chaleil, R. A. G., Fernandez-Recio, J., & Bates, P. A. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt038 · Cited by 285
Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008
participants, G. D. 2., Michino, M., Abola, E., Brooks, C. L., Dixon, J. S., Moult, J., & Stevens, R. C. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2877 · Cited by 276
Protein−Nanoparticle Interaction: Identification of the Ubiquitin−Gold Nanoparticle Interaction Site
Calzolai, L., Franchini, F., Gilliland, D., & Rossi, F. (2010) · Nano Letters · doi:10.1021/nl101746v · Cited by 254
Docking, scoring, and affinity prediction in CAPRI
Lensink, M. F., & Wodak, S. J. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24428 · Cited by 249
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Lensink, M. F., & Wodak, S. J. (2010) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22818 · Cited by 226
Self-citationModeling protein–protein and protein–peptide complexes: CAPRI 6th edition
Lensink, M. F., Velankar, S., & Wodak, S. J. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25215 · Cited by 222
PRosettaC: Rosetta Based Modeling of PROTAC Mediated Ternary Complexes
Zaidman, D., Prilusky, J., & London, N. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00589 · Cited by 224
DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces
Réau, M., Renaud, N., Xue, L. C., & Bonvin, A. M. J. J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac759 · Cited by 190
Computational prediction of protein–protein binding affinities
Siebenmorgen, T., & Zacharias, M. (2020) · WIREs Computational Molecular Science · doi:10.1002/wcms.1448 · Cited by 184
“12,48,49”
Efficient flexible backbone protein-protein docking for challenging targets
Marze, N. A., Roy Burman, S. S., Sheffler, W., & Gray, J. J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty355 · Cited by 173
Blind prediction of homo- and hetero-protein complexes: the CASP13-CAPRI experiment
Lensink, M. F., Brysbaert, G., Nadzirin, N., Velankar, S., Chaleil, R. A. G., Gerguri, T., Bates, P. A., Laine, E., Carbone, A., Grudinin, S., et al. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25838 · Cited by 122
Self-citationHADDOCK3: A Modular and Versatile Platform for Integrative Modeling of Biomolecular Complexes
Giulini, M., Reys, V., Teixeira, J. M. C., Jiménez-García, B., V. Honorato, R., Kravchenko, A., Xu, X., Versini, R., Engel, A., Verhoeven, S., et al. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00969 · Cited by 51
Exploiting Conformational Ensembles in Modeling Protein–Protein Interactions on the Proteome Scale
Kuzu, G., Gursoy, A., Nussinov, R., & Keskin, O. (2013) · Journal of Proteome Research · doi:10.1021/pr400006k · Cited by 47
New benchmark metrics for protein‐protein docking methods
Gao, M., & Skolnick, J. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22987 · Cited by 42
Assessment of the CASP14 assembly predictions
Ozden, B., Kryshtafovych, A., & Karaca, E. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26199 · Cited by 41
Evaluation of model refinement in CASP14
Simpkin, A. J., Sánchez Rodríguez, F., Mesdaghi, S., Kryshtafovych, A., & Rigden, D. J. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26185 · Cited by 28
Protein-protein and peptide-protein docking and refinement using ATTRACT in CAPRI
Schindler, C. E. M., Chauvot de Beauchêne, I., de Vries, S. J., & Zacharias, M. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25196 · Cited by 20
Oda, T. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26551 · Cited by 17
Deep Local Analysis evaluates protein docking conformations with locally oriented cubes
Mohseni Behbahani, Y., Crouzet, S., Laine, E., & Carbone, A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac551 · Cited by 16
PANDORA v2.0: Benchmarking peptide-MHC II models and software improvements
Parizi, F. M., Marzella, D. F., Ramakrishnan, G., ‘t Hoen, P. A. C., Karimi-Jafari, M. H., & Xue, L. C. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1285899 · Cited by 14
Chakravarty, D., McElfresh, G., Kundrotas, P. J., & Vakser, I. A. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25875 · Cited by 13
Choi, J. (2023) · iScience · doi:10.1016/j.isci.2023.106911 · Cited by 1
Metadata
- Authors
- Lensink, M. F., Méndez, R., & Wodak, S. J.
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2007-10-31
- Volume
- 69
- Issue
- 4
- Pages
- 704-718
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- OpenAlex
- W2018410079
- Cited by
- 340
- References
- 24
- FWCI
- 10.06
- Type
- journal article
- Language
- en
- Open access
- closed
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