Updates to the integrated protein-protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2
Open accessAbstract
We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r=0.52 overall and r=0.72 for the rigid complexes.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302 · see citing works
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
Crystallography & NMR System: A New Software Suite for Macromolecular Structure Determination
Brünger, A. T., Adams, P. D., Clore, G. M., DeLano, W. L., Gros, P., Grosse‐Kunstleve, R. W., Jiang, J., Kuszewski, J. J., Nilges, M., Pannu, N., et al. (1998) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444998003254 · Cited by 15,720 · see citing works
A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules
Cornell, W. D., Cieplak, P., Bayly, C. I., Gould, I. R., Merz, K. M., Ferguson, D. M., Spellmeyer, D. C., Fox, T., Caldwell, J. W., & Kollman, P. A. (1995) · Journal of the American Chemical Society · doi:10.1021/ja00124a002 · Cited by 13,111 · see citing works
Murzin, A. G., Brenner, S. E., Hubbard, T., & Chothia, C. (1995) · Journal of Molecular Biology · doi:10.1016/s0022-2836(05)80134-2 · Cited by 6,412 · see citing works
Jorgensen, W. L., & Tirado‐Rives, J. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00214a001 · Cited by 5,018 · see citing works
HADDOCK: A Protein−Protein Docking Approach Based on Biochemical or Biophysical Information
Dominguez, C., Boelens, R., & Bonvin, A. M. J. J. (2003) · Journal of the American Chemical Society · doi:10.1021/ja026939x · Cited by 3,187 · see citing works
Self-citationStatistical potential for assessment and prediction of protein structures
Shen, M., & Šali, A. (2006) · Protein Science · doi:10.1110/ps.062416606 · Cited by 2,294 · see citing works
ZDOCK server: interactive docking prediction of protein–protein complexes and symmetric multimers
Pierce, B. G., Wiehe, K. J., Hwang, H., Kim, B., Vreven, T., & Weng, Z. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu097 · Cited by 1,767 · see citing works
Self-citationThe HADDOCK web server for data-driven biomolecular docking
de Vries, S. J., van Dijk, M., & Bonvin, A. M. J. J. (2010) · Nature Protocols · doi:10.1038/nprot.2010.32 · Cited by 1,404 · see citing works
Self-citationZDOCK: An initial‐stage protein‐docking algorithm
Chen, R., Li, L., & Weng, Z. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10389 · Cited by 1,393 · see citing works
Self-citationGabb, H. A., Jackson, R. M., & Sternberg, M. J. E. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.1203 · Cited by 820 · see citing works
Fox, N. K., Brenner, S. E., & Chandonia, J.-M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1240 · Cited by 762 · see citing works
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 · see citing works
FireDock: Fast interaction refinement in molecular docking
Andrusier, N., Nussinov, R., & Wolfson, H. J. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21495 · Cited by 680 · see citing works
Accelerating Protein Docking in ZDOCK Using an Advanced 3D Convolution Library
Pierce, B. G., Hourai, Y., & Weng, Z. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024657 · Cited by 645 · see citing works
Self-citationOn the binding affinity of macromolecular interactions: daring to ask why proteins interact
Kastritis, P. L., & Bonvin, A. M. J. J. (2012) · Journal of The Royal Society Interface · doi:10.1098/rsif.2012.0835 · Cited by 518 · see citing works
Self-citationProtein–protein docking benchmark version 4.0
Hwang, H., Vreven, T., Janin, J., & Weng, Z. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22830 · Cited by 476 · see citing works
Self-citationPrediction 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 · see citing works
Assessment of blind predictions of protein–protein interactions: Current status of docking methods
Mendez, R., Leplae, R., Maria, L. D., & Wodak, S. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10393 · Cited by 438 · see citing works
A protein–protein docking benchmark
Chen, R., Mintseris, J., Janin, J., & Weng, Z. (2003) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10390 · Cited by 345 · see citing works
Self-citationpyDock: Electrostatics and desolvation for effective scoring of rigid‐body protein–protein docking
Cheng, T. M. K., Blundell, T. L., & Fernández‐Recio, J. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21419 · Cited by 345 · see citing works
Integrating statistical pair potentials into protein complex prediction
Mintseris, J., Pierce, B., Wiehe, K., Anderson, R., Chen, R., & Weng, Z. (2007) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21502 · Cited by 334 · see citing works
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 323 · see citing works
Self-citationA structure‐based benchmark for protein–protein binding affinity
Kastritis, P., Moal, I., Hwang, H., Weng, Z., Bates, P. A., Bonvin, A., & Janin, J. (2011) · Protein Science · doi:10.1002/pro.580 · Cited by 318 · see citing works
Self-citationRecent Progress and Future Directions in Protein-Protein Docking
Ritchie, D. W. (2008) · Current Protein and Peptide Science · doi:10.2174/138920308783565741 · Cited by 317 · see citing works
Flexible protein-protein docking.
Bonvin, A. (2006) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2006.02.002 · Cited by 312 · see citing works
Self-citationA novel shape complementarity scoring function for protein‐protein docking
Chen, R., & Weng, Z. (2003) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10334 · Cited by 298 · see citing works
Self-citationSwarmDock: a server for flexible protein–protein docking
Torchala, M., Moal, I. H., Chaleil, R. A. G., Fernández‐Recio, J., & Bates, P. A. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt038 · Cited by 285 · see citing works
Self-citationProtein–protein docking benchmark 2.0: An update
Mintseris, J., Wiehe, K. J., Pierce, B. G., Anderson, R., Chen, R., Janin, J., & Weng, Z. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20560 · Cited by 278 · see citing works
Self-citationIdentification of Protein–Protein Interaction Sites from Docking Energy Landscapes
Fernández‐Recio, J., Totrov, M. M., & Abagyan, R. A. (2003) · Journal of Molecular Biology · doi:10.1016/j.jmb.2003.10.069 · Cited by 274 · see citing works
Kastritis, P., & Bonvin, A. (2010) · Journal of Proteome Research · doi:10.1021/pr9009854 · Cited by 267 · see citing works
Self-citationTunçbağ, N., Gürsoy, A., Nussinov, R., & Keskin, Ö. (2011) · Nature Protocols · doi:10.1038/nprot.2011.367 · Cited by 264 · see citing works
Protein–protein docking benchmark version 3.0
Hwang, H., Pierce, B. G., Mintseris, J., Janin, J., & Weng, Z. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22106 · Cited by 256 · see citing works
Self-citationLu, L. J., Lu, H. J., & Skolnick, J. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10222 · Cited by 240 · see citing works
PRISM: protein interactions by structural matching
Ogmen, U., Keskin, Ö., Aytuna, A. S., Nussinov, R., & Gürsoy, A. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki585 · Cited by 207 · see citing works
Liu, S., Zhang, C., Zhou, H., & Zhou, Y. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20019 · Cited by 193 · see citing works
Protein-Protein Complex Structure Predictions by Multimeric Threading and Template Recombination
Mukherjee, S., & Zhang, Y. (2011) · Structure · doi:10.1016/j.str.2011.04.006 · Cited by 174 · see citing works
Template-based structure modeling of protein–protein interactions
Szilágyi, A., & Zhang, Y. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.11.005 · Cited by 168 · see citing works
Accounting for conformational changes during protein-protein docking.
Zacharias, M. (2010) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2010.02.001 · Cited by 159 · see citing works
SwarmDock and the Use of Normal Modes in Protein-Protein Docking
Moal, I., & Bates, P. (2010) · International Journal of Molecular Sciences · doi:10.3390/ijms11103623 · Cited by 154 · see citing works
Self-citationA combination of rescoring and refinement significantly improves protein docking performance
Pierce, B. G., & Weng, Z. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21920 · Cited by 145 · see citing works
Self-citationKastritis, P., Rodrigues, J., Folkers, G., Boelens, R., & Bonvin, A. (2014) · Journal of Molecular Biology · doi:10.1016/j.jmb.2014.04.017 · Cited by 137 · see citing works
Self-citationKunik, V., Ashkenazi, S., & Ofran, Y. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks480 · Cited by 132 · see citing works
The scoring of poses in protein-protein docking: current capabilities and future directions
Moal, I. H., Torchala, M., Bates, P. A., & Fernández‐Recio, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-286 · Cited by 116 · see citing works
Self-citationProtein–protein binding affinity prediction on a diverse set of structures
Moal, I. H., Agius, R., & Bates, P. A. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr513 · Cited by 112 · see citing works
Self-citationProtein–protein interaction networks: the puzzling riches
Wodak, S. J., Vlasblom, J., Turinsky, A. L., & Pu, S. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.08.002 · Cited by 109 · see citing works
Integrative computational modeling of protein interactions
Rodrigues, J., & Bonvin, A. M. J. J. (2014) · FEBS Journal · doi:10.1111/febs.12771 · Cited by 106 · see citing works
Self-citationScoring functions for protein–protein interactions
Moal, I. H., Moretti, R., Baker, D. A., & Fernández‐Recio, J. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.06.017 · Cited by 91 · see citing works
Self-citationDocking by structural similarity at protein‐protein interfaces
Sinha, R., Kundrotas, P. J., & Vakser, I. A. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22812 · Cited by 85 · see citing works
D ockground resource for studying protein–protein interfaces
Douguet, D., Chen, H.-C., Tovchigrechko, A., & Vakser, I. A. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl447 · Cited by 84 · see citing works
A protein-DNA docking benchmark
van Dijk, M., & Bonvin, A. M. J. J. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn386 · Cited by 83 · see citing works
Self-citationCCharPPI web server: computational characterization of protein–protein interactions from structure
Moal, I. H., Jiménez‐García, B., & Fernández‐Recio, J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu594 · Cited by 78 · see citing works
Self-citationPrediction of protein–protein binding free energies
Vreven, T., Hwang, H., Pierce, B. G., & Weng, Z. (2012) · Protein Science · doi:10.1002/pro.2027 · Cited by 76 · see citing works
Self-citationSACS—self-maintaining database of antibody crystal structure information
Allcorn, L. C., & Martin, A. C. R. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.1.175 · Cited by 74 · see citing works
Pons, C., Talavera, D., de la Cruz, X., Orozco, M., & Fernández‐Recio, J. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci100353e · Cited by 66 · see citing works
Li, X., Moal, I. H., & Bates, P. A. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22770 · Cited by 61 · see citing works
Self-citationA protein‐RNA docking benchmark (II): Extended set from experimental and homology modeling data
Pérez‐Cano, L., Jiménez‐García, B., & Fernández‐Recio, J. (2012) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24075 · Cited by 51 · see citing works
Self-citationMolecular origins of binding affinity: seeking the Archimedean point
Kastritis, P. L., & Bonvin, A. M. J. J. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.07.001 · Cited by 43 · see citing works
Self-citationEvaluating template-based and template-free protein-protein complex structure prediction
Vreven, T., Hwang, H., Pierce, B. G., & Weng, Z. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbt047 · Cited by 41 · see citing works
Self-citationDesigning coarse grained-and atom based-potentials for protein-protein docking
Tobi, D. (2010) · BMC Structural Biology · doi:10.1186/1472-6807-10-40 · Cited by 39 · see citing works
Solvated protein–protein docking using Kyte‐Doolittle‐based water preferences
Kastritis, P. L., Visscher, K. M., van Dijk, A. D. J., & Bonvin, A. M. J. J. (2012) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24210 · Cited by 36 · see citing works
Self-citationMoal, I. H., & Fernández‐Recio, J. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400295z · Cited by 35 · see citing works
Self-citationNon-interacting surface solvation and dynamics in protein-protein interactions
Visscher, K. M., Kastritis, P. L., & Bonvin, A. M. J. J. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24741 · Cited by 29 · see citing works
Self-citationMoure, C. M., Bowman, B. R., Gershon, P. D., & Quiocho, F. A. (2006) · Molecular Cell · doi:10.1016/j.molcel.2006.03.015 · Cited by 29 · see citing works
Performance of ZDOCK in CAPRI rounds 20–26
Vreven, T., Pierce, B. G., Hwang, H., & Weng, Z. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24432 · Cited by 29 · see citing works
Self-citationDocking by Structural Similarity at Protein-Protein Interfaces
Kundrotas, P. J., Sinha, R., & Vakser, I. A. (2010) · Biophysical Journal · doi:10.1016/j.bpj.2009.12.1043 · Cited by 29 · see citing works
A minimal model of protein–protein binding affinities
Janin, J. (2014) · Protein Science · doi:10.1002/pro.2560 · Cited by 28 · see citing works
Inferring the microscopic surface energy of protein–protein interfaces from mutation data
Moal, I. H., Dapkūnas, J., & Fernández‐Recio, J. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24761 · Cited by 13 · see citing works
Self-citationBonvin, A. M. J. J., & Vangone, A. (2015) · Harvard SBGrid · doi:10.15785/sbgrid/131 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Improved prediction of protein-protein interactions using AlphaFold2
Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,025 · see citing works
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Gaínza, P., Sverrisson, F., Monti, F., Rodolà, E., Boscaini, D., Bronstein, M. M., & Correia, B. E. (2019) · Nature Methods · doi:10.1038/s41592-019-0666-6 · Cited by 811 · see citing works
Performance and Its Limits in Rigid Body Protein-Protein Docking
Desta, I., Porter, K. A., Xia, B., Kozakov, D., & Vajda, S. (2020) · Structure · doi:10.1016/j.str.2020.06.006 · Cited by 697 · see citing works
Key Topics in Molecular Docking for Drug Design
Torres, P. H. M., Sodero, A. C. R., Jofily, P., & Silva, F. P. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184574 · Cited by 670 · see citing works
Protein interface prediction using graph convolutional networks
Fout, A., Ben‐Hur, A., Anderson, C., Chitsaz, H., & Zhou, W. (2017) · Digital Collections of Colorado (Colorado State University) · doi:10.25675/3.021826 · Cited by 606 · see citing works
Graph convolutional networks for computational drug development and discovery
Sun, M., Zhao, S., Gilvary, C. M., Elemento, O., Zhou, J., & Wang, F. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz042 · Cited by 414 · see citing works
Jankauskaitė, J., Jiménez-García, B., Dapkūnas, J., Fernández-Recio, J., & Moal, I. H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty635 · Cited by 389 · see citing works
Self-citationPredicting Protein–Protein Interactions from the Molecular to the Proteome Level
Keskin, O., Tuncbag, N., & Gursoy, A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00683 · Cited by 369 · see citing works
Benchmarking <scp>AlphaFold</scp> for protein complex modeling reveals accuracy determinants
Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2022) · Protein Science · doi:10.1002/pro.4379 · Cited by 335 · see citing works
Self-citationBenchmarking AlphaFold for protein complex modeling reveals accuracy determinants
Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2021) · doi:10.1101/2021.10.23.465575 · Cited by 335 · see citing works
Self-citationPiersimoni, L., Kastritis, P. L., Arlt, C., & Sinz, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00786 · Cited by 308 · see citing works
Self-citationTubiana, J., Schneidman-Duhovny, D., & Wolfson, H. J. (2022) · Nature Methods · doi:10.1038/s41592-022-01490-7 · Cited by 274 · see citing works
Vangone, A., Schaarschmidt, J., Koukos, P. I., Geng, C., Citro, N., Trellet, M. E., Xue, L. C., & Bonvin, A. M. J. J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty816 · Cited by 256 · see citing works
Self-citationDe novo design of protein interactions with learned surface fingerprints
Gaínza, P., Wehrle, S., Hall‐Beauvais, A. V., Marchand, A., Scheck, A., Harteveld, Z., Buckley, S., Ni, D., Tan, S., Sverrisson, F., et al. (2023) · Nature · doi:10.1038/s41586-023-05993-x · Cited by 241 · see citing works
Atomic-level characterization of protein–protein association
Pan, A. C., Jacobson, D., Yatsenko, K., Sritharan, D., Weinreich, T. M., & Shaw, D. E. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1815431116 · Cited by 204 · see citing works
Protein–protein interaction prediction with deep learning: A comprehensive review
Soleymani, F., Paquet, E., Viktor, H. L., Michalowski, W. J., & Spinello, D. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.070 · Cited by 195 · see citing works
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 · see citing works
Self-citationEfficient 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 · see citing works
PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces
Krapp, L. F., Abriata, L. A., Rodríguez, F. C., & Peraro, M. D. (2023) · Nature Communications · doi:10.1038/s41467-023-37701-8 · Cited by 159 · see citing works
PRODIGY: A Contact-based Predictor of Binding Affinity in Protein-protein Complexes
Vangone, A., & Bonvin, A. M. J. J. (2017) · BIO-PROTOCOL · doi:10.21769/bioprotoc.2124 · Cited by 154 · see citing works
Self-citationYin, R., & Pierce, B. G. (2024) · Protein Science · doi:10.1002/pro.4865 · Cited by 145 · see citing works
Self-citationLightDock: a new multi-scale approach to protein–protein docking
Jiménez‐García, B., Roel‐Touris, J., Romero‐Durana, M., Vidal, M., Jiménez-González, D., & Fernández‐Recio, J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx555 · Cited by 139 · see citing works
Self-citationLearning context-aware structural representations to predict antigen and antibody binding interfaces
Pittala, S., & Bailey‐Kellogg, C. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa263 · Cited by 138 · see citing works
Jones, G. S., Jindal, A., Ghani, U., Kotelnikov, S., Egbert, M. E., Hashemi, N., Vajda, S., Padhorny, D., & Kozakov, D. (2022) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798322002741 · Cited by 136 · see citing works
Kurkcuoglu, Z., Koukos, P. I., Citro, N., Trellet, M. E., Rodrigues, J., Moreira, I. S., Roel‐Touris, J., Melquiond, A. S. J., Geng, C., Schaarschmidt, J., et al. (2017) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-017-0049-y · Cited by 130 · see citing works
Self-citationProtein–protein interactions: scoring schemes and binding affinity
Gromiha, M. M., Yugandhar, K., & Jemimah, S. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.10.016 · Cited by 127 · see citing works
Acoplamiento Molecular: Avances Recientes y Retos
Prieto-Martínez, F. D., Arciniega, M., & Medina‐Franco, J. L. (2018) · TIP Revista Especializada en Ciencias Químico-Biológicas · doi:10.22201/fesz.23958723e.2018.0.143 · Cited by 126 · see citing works
DeepRank: a deep learning framework for data mining 3D protein-protein interfaces
Renaud, N., Geng, C., Georgievska, S., Ambrosetti, F., Ridder, L., Marzella, D. F., Réau, M. F., Bonvin, A. M. J. J., & Xue, L. C. (2021) · Nature Communications · doi:10.1038/s41467-021-27396-0 · Cited by 125 · see citing works
Self-citationGuest, J. D., Vreven, T., Zhou, J., Moal, I. H., Jeliazkov, J. R., Gray, J. J., Weng, Z., & Pierce, B. G. (2020) · Structure · doi:10.1016/j.str.2021.01.005 · Cited by 119 · see citing works
Self-citationWhat method to use for protein–protein docking?
Porter, K. A., Desta, I., Kozakov, D., & Vajda, S. (2019) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2018.12.010 · Cited by 116 · see citing works
Functional Sites Induce Long-Range Evolutionary Constraints in Enzymes
Jack, B. R., Meyer, A. G., Echave, J., & Wilke, C. O. (2016) · PLoS Biology · doi:10.1371/journal.pbio.1002452 · Cited by 114 · see citing works
Review and comparative assessment of sequence-based predictors of protein-binding residues
Zhang, J., & Kurgan, L. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx022 · Cited by 114 · see citing works
Protein interaction interface region prediction by geometric deep learning
Dai, B., & Bailey‐Kellogg, C. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab154 · Cited by 100 · see citing works
Modeling Antibody-Antigen Complexes by Information-Driven Docking
Ambrosetti, F., Jiménez‐García, B., Roel‐Touris, J., & Bonvin, A. M. J. J. (2019) · Structure · doi:10.1016/j.str.2019.10.011 · Cited by 97 · see citing works
Self-citationBIPSPI: a method for the prediction of partner-specific protein–protein interfaces
Sánchez-García, R., Sorzano, C. Ó. S., Carazo, J. M., & Segura, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty647 · Cited by 93 · see citing works
Discovering functionally important sites in proteins
Cagiada, M., Bottaro, S., Lindemose, S., Schenstrøm, S. M., Stein, A., Hartmann‐Petersen, R., & Lindorff‐Larsen, K. (2023) · Nature Communications · doi:10.1038/s41467-023-39909-0 · Cited by 84 · see citing works
Rui, H., Ashton, K. S., Min, J., Wang, C. W., & Potts, P. R. (2023) · RSC Chemical Biology · doi:10.1039/d2cb00207h · Cited by 82 · see citing works
A survey of geometric graph neural networks: data structures, models and applications
Han, J., Cen, J., Wu, L., Li, Z., Kong, X., Jiao, R., Yu, Z., Xu, T., Wu, F., Wang, Z., et al. (2025) · Frontiers of Computer Science · doi:10.1007/s11704-025-41426-w · Cited by 82 · see citing works
Kuroda, D., & Gray, J. J. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw197 · Cited by 82 · see citing works
Dockground: A comprehensive data resource for modeling of protein complexes
Kundrotas, P. J., Anishchenko, I. V., Dauzhenka, T. A., Kotthoff, I., Mnevets, D., Copeland, M. M., & Vakser, I. A. (2017) · Protein Science · doi:10.1002/pro.3295 · Cited by 82 · see citing works
Machine learning solutions for predicting protein–protein interactions
Casadio, R., Martelli, P. L., & Savojardo, C. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1618 · Cited by 80 · see citing works
Recent advances in predicting and modeling protein–protein interactions
Durham, J., Zhang, J., Humphreys, I. R., Pei, J., & Cong, Q. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2023.03.003 · Cited by 80 · see citing works
Recent advances in ion-imprinted membranes: separation and detection via ion-selective recognition
Lu, J., Qin, Y., Wu, Y., Meng, M., Yan, Y., & Li, C. (2019) · Environmental Science Water Research & Technology · doi:10.1039/c9ew00465c · Cited by 76 · see citing works
A Review of Deep Learning Methods for Antibodies
Graves, J., Byerly, J., Priego, E., Makkapati, N., Parish, S., Medellin, B. P., & Berrondo, M. (2020) · Antibodies · doi:10.3390/antib9020012 · Cited by 73 · see citing works
ATOM3D: Tasks On Molecules in Three DimensionsPreprint
Townshend, R. J. L., Vögele, M., Suriana, P., Derry, A., Powers, A. S., Laloudakis, Y. D., Balachandar, S., Jing, B., Anderson, B., Eismann, S., et al. (2020) · arXiv (Cornell University) · doi:10.13140/rg.2.2.30339.73765 · Cited by 72 · see citing works
iScore: a novel graph kernel-based function for scoring protein–protein docking models
Geng, C., Jung, Y. G., Renaud, N., Honavar, V., Bonvin, A. M. J. J., & Xue, L. C. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz496 · Cited by 71 · see citing works
Self-citationImproved Docking of Protein Models by a Combination of Alphafold2 and ClusPro
Ghani, U., Desta, I., Jindal, A., Khan, O., Jones, G., Hashemi, N., Kotelnikov, S., Padhorny, D., Vajda, S., & Kozakov, D. (2021) · doi:10.1101/2021.09.07.459290 · Cited by 68 · see citing works
Accurate prediction of inter-protein residue–residue contacts for homo-oligomeric protein complexes
Yan, Y., & Huang, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab038 · Cited by 68 · see citing works
Challenges in structural approaches to cell modeling
Im, W., Liang, J., Olson, A. J., Zhou, H., Vajda, S., & Vakser, I. A. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.05.024 · Cited by 66 · see citing works
Integration of pre-trained protein language models into geometric deep learning networks
Wu, F., Wu, L., Radev, D., Xu, J., & Li, S. Z. (2023) · Communications Biology · doi:10.1038/s42003-023-05133-1 · Cited by 61 · see citing works
TCRmodel: high resolution modeling of T cell receptors from sequence
Gowthaman, R., & Pierce, B. G. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky432 · Cited by 61 · see citing works
Self-citationAdvances to tackle backbone flexibility in protein docking
Harmalkar, A., & Gray, J. J. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.11.011 · Cited by 60 · see citing works
Modeling Protein Complexes Using Restraints from Crosslinking Mass Spectrometry
Bullock, J. M. A., Sen, N., Thalassinos, K., & Topf, M. (2018) · Structure · doi:10.1016/j.str.2018.04.016 · Cited by 58 · see citing works
Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design
Kuroda, D., & Tsumoto, K. (2020) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2020.01.011 · Cited by 58 · see citing works
Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes
Eismann, S., Townshend, R. J. L., Thomas, N., Jagota, M., Jing, B., & Dror, R. O. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26033 · Cited by 58 · see citing works
Less Is More: Coarse-Grained Integrative Modeling of Large Biomolecular Assemblies with HADDOCK
Roel‐Touris, J., Don, C. G., Honorato, R. V., Rodrigues, J., & Bonvin, A. M. J. J. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00310 · Cited by 57 · see citing works
Self-citationWang, J., Ishchenko, A., Zhang, W., Razavi, A., & Langley, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-05875-8 · Cited by 55 · see citing works
Challenges in antibody structure prediction
Fernández-Quintero, M. L., Kokot, J., Waibl, F., Fischer, A.-L. M., Quoika, P. K., Deane, C. M., & Liedl, K. R. (2023) · mAbs · doi:10.1080/19420862.2023.2175319 · Cited by 55 · see citing works
Exploring the potential of 3D Zernike descriptors and SVM for protein–protein interface prediction
Daberdaku, S., & Ferrari, C. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2043-3 · Cited by 55 · see citing works
Liu, J., & Gong, X. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3199-1 · Cited by 55 · see citing works
Xie, Z., Deng, X., & Shu, K. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21020467 · Cited by 55 · see citing works
Antibody design using deep learning: from sequence and structure design to affinity maturation
Joubbi, S., Micheli, A., Milazzo, P., Maccari, G., Ciano, G., Cardamone, D., & Medini, D. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae307 · Cited by 54 · see citing works
Park, T., Baek, M., Lee, H., & Seok, C. (2019) · Journal of Computational Chemistry · doi:10.1002/jcc.25874 · Cited by 54 · see citing works
Yang, Y. X., Huang, J. Y., Wang, P., & Zhu, B. T. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01499 · Cited by 53 · see citing works
Integrative modeling of membrane-associated protein assemblies
Roel‐Touris, J., Jiménez‐García, B., & Bonvin, A. M. J. J. (2020) · Nature Communications · doi:10.1038/s41467-020-20076-5 · Cited by 53 · see citing works
Self-citationNative or Non-Native Protein–Protein Docking Models? Molecular Dynamics to the Rescue
Jandová, Z., Vargiu, A. V., & Bonvin, A. M. J. J. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00336 · Cited by 52 · see citing works
Self-citationProtein–Protein Docking: Past, Present, and Future
Sunny, S., & Jayaraj, P. B. (2021) · The Protein Journal · doi:10.1007/s10930-021-10031-8 · Cited by 50 · see citing works
Malik, F., & Guo, J. (2022) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26313 · Cited by 50 · see citing works
Ambreen, S., Umar, M., Noor, A., Jain, H., & Ali, R. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.117164 · Cited by 50 · see citing works
Energy‐based graph convolutional networks for scoring protein docking models
Cao, Y., & Shen, Y. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25888 · Cited by 50 · see citing works
Deep Learning of High-Order Interactions for Protein Interface Prediction
Liu, Y., Yuan, H., Cai, L., & Ji, S. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403110 · Cited by 48 · see citing works
Camilloni, C., & Pietrucci, F. (2018) · Advances in Physics X · doi:10.1080/23746149.2018.1477531 · Cited by 48 · see citing works
proABC-2: PRediction of AntiBody contacts v2 and its application to information-driven docking
Ambrosetti, F., Olsen, T. H., Olimpieri, P. P., Jiménez‐García, B., Milanetti, E., Marcatili, P., & Bonvin, A. M. J. J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa644 · Cited by 48 · see citing works
Self-citationFinding correct protein–protein docking models using ProQDock
Basu, S., & Wallner, B. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw257 · Cited by 47 · see citing works
A non-redundant protein-RNA docking benchmark version 2.0
Nithin, C., Mukherjee, S., & Bahadur, R. P. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25211 · Cited by 47 · see citing works
LightDock goes information-driven
Roel‐Touris, J., Bonvin, A. M. J. J., & Jiménez‐García, B. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz642 · Cited by 47 · see citing works
Self-citationZhu, H., Xiu-quan, D., & Yao, Y. (2019) · Current Bioinformatics · doi:10.2174/1574893614666191105155713 · Cited by 46 · see citing works
CoDockPP: A Multistage Approach for Global and Site-Specific Protein–Protein Docking
Kong, R., Wang, F., Zhang, J., Wang, F., & Chang, S. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00445 · Cited by 46 · see citing works
The ClusPro AbEMap web server for the prediction of antibody epitopes
Desta, I., Kotelnikov, S., Jones, G. S., Ghani, U., Abyzov, M., Kholodov, Y. A., Standley, D. M., Beglov, D., Vajda, S., & Kozakov, D. (2023) · Nature Protocols · doi:10.1038/s41596-023-00826-7 · Cited by 45 · see citing works
pyDockDNA: A new web server for energy-based protein-DNA docking and scoring
Rodríguez‐Lumbreras, L. A., Jiménez‐García, B., Giménez‐Santamarina, S., & Fernández‐Recio, J. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.988996 · Cited by 44 · see citing works
Self-citationProtein–protein interaction prediction methods: from docking-based to AI-based approaches
Tsuchiya, Y., Yamamori, Y., & Tomii, K. (2022) · Biophysical Reviews · doi:10.1007/s12551-022-01032-7 · Cited by 44 · see citing works
Li, S., Wu, S., Wang, L., Li, F., Jiang, H., & Bai, F. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102344 · Cited by 42 · see citing works
Feng, S., Chen, Z., Zhang, C., Xie, Y., Ovchinnikov, S., Gao, Y. Q., & Liu, S. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00873-z · Cited by 41 · see citing works
Giulini, M., Schneider, C., Cutting, D., Desai, N., Deane, C. M., & Bonvin, A. M. J. J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae583 · Cited by 41 · see citing works
Self-citationSAXS Data Alone can Generate High-Quality Models of Protein-Protein Complexes
Schindler, C., de Vries, S. J., Sasse, A., & Zacharias, M. (2016) · Structure · doi:10.1016/j.str.2016.06.007 · Cited by 41 · see citing works
Yu, J., & Guérois, R. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw533 · Cited by 41 · see citing works
Rosetta FunFolDes – A general framework for the computational design of functional proteins
Bonet, J., Wehrle, S., Schriever, K., Yang, C., Billet, A., Sesterhenn, F., Scheck, A., Sverrisson, F., Veselková, B., Vollers, S. S., et al. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006623 · Cited by 41 · see citing works
Flexible protein–protein docking with a multitrack iterative transformer
Chu, L., Ruffolo, J. A., Harmalkar, A., & Gray, J. J. (2024) · Protein Science · doi:10.1002/pro.4862 · Cited by 41 · see citing works
Chen, S., & Zacharias, M. (2023) · ACS Central Science · doi:10.1021/acscentsci.3c00003 · Cited by 40 · see citing works
Artificial intelligence approaches to human-microbiome protein–protein interactions
Lim, H., Cankara, F., Tsai, C.-J., Keskin, O., Nussinov, R., & Gursoy, A. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102328 · Cited by 39 · see citing works
(2023) · doi:10.1093/bioadv/vbad191 · Cited by 37 · see citing works
Self-citationProtein-Protein Interaction Interface Residue Pair Prediction Based on Deep Learning Architecture
Zhao, Z., & Gong, X. (2017) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2017.2706682 · Cited by 37 · see citing works
Structure-based cross-docking analysis of antibody–antigen interactions
Kilambi, K. P., & Gray, J. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-08414-y · Cited by 37 · see citing works
Machine learning methods for protein-protein binding affinity prediction in protein design
Guo, Z., & Yamaguchi, R. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.1065703 · Cited by 37 · see citing works
PPI-HotspotDB: Database of Protein–Protein Interaction Hot Spots
Chen, Y. C., Chen, Y., Wright, J. D., & Lim, C. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00025 · Cited by 36 · see citing works
A comprehensive survey of scoring functions for protein docking models
Shirali, A., Stebliankin, V., Karki, U., Shi, J., Chapagain, P. P., & Narasimhan, G. (2025) · BMC Bioinformatics · doi:10.1186/s12859-024-05991-4 · Cited by 36 · see citing works
RapidRMSD: rapid determination of RMSDs corresponding to motions of flexible molecules
Neveu, É., Popov, P. A., Hoffmann, A., Migliosi, A., Besseron, X., Danoy, G., Bouvry, P., & Grudinin, S. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty160 · Cited by 36 · see citing works
Artificial intelligence based methods for hot spot prediction
Ovek, D., Abali, Z., Zeylan, M. E., Keskin, Ö., Gürsoy, A., & Tunçbağ, N. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.11.003 · Cited by 36 · see citing works
- Authors
- Vreven, T., Moal, I. H., Vangone, A., Pierce, B. G., Kastritis, P. L., Torchala, M., Chaleil, R., Jiménez-García, B., Bates, P. A., Fernandez-Recio, J., Bonvin, A. M. J. J., & Weng, Z.
- Affiliations
- University of Massachusetts Chan Medical School (US)
- Barcelona Supercomputing Center (ES)
- Utrecht University (NL)
- The Francis Crick Institute (GB)
- Corresponding author
- Bates, Paul A.
- Bonvin, Alexandre M.J.J.
- Weng, Zhiping
- ORCID
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2015-09
- Volume
- 427
- Issue
- 19
- Pages
- 3031-3041
- ISSN
- 0022-2836 (ISSN-L)
- PMC
- PMC4677049
- OpenAlex
- W1030883578
- Cited by
- 424
- References
- 70
- Citations by year
- 2026: 28 · 2025: 40 · 2024: 42 · 2023: 45 · 2022: 58 · 2021: 43 · 2020: 35 · 2019: 39 · 2018: 40 · 2017: 30 · 2016: 23
- FWCI
- 13.45
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Molecular Biology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (acceptedVersion, other-oa)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (acceptedVersion, other-oa)
- eScholarship@UMassChan (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, public-domain)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- Cancer Research UK (10748)
- National Institute of General Medical Sciences (R01GM084884)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein-protein docking, binding affinity prediction, docking benchmark, protein-protein complex, immune complexes, rigid-body docking
- MeSH terms
- Algorithms, Animals, Humans, Polynucleotide Adenylyltransferase, Protein Binding, Protein Conformation, Proteins, Software, Thermodynamics, Vaccinia virus, Viral Proteins, Protein Interaction Mapping, Molecular Docking Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
