Updates to the integrated protein-protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2

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

The Protein Data Bank

Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196

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 15,723

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

SCOP: A structural classification of proteins database for the investigation of sequences and structures

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 6,435

The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin

Jorgensen, W. L., & Tirado‐Rives, J. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00214a001 · Cited by 5,060

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

Statistical potential for assessment and prediction of protein structures

Shen, M., & Šali, A. (2006) · Protein Science · doi:10.1110/ps.062416606 · Cited by 2,294

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

ZDOCK: 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

Exploring protein‐protein interactions using the site‐identification by ligand competitive saturation methodology

Yu, W., Jo, S., Lakkaraju, S. K., Weber, D. J., & MacKerell, A. D. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25650 · Cited by 35

Methods for sequence and structural analysis of B and T cell receptor repertoires

Teraguchi, S., Saputri, D. S., Llamas-Covarrubias, M. A., Davila, A., Díez, D., Nazlica, S. A., Rozewicki, J., Ismanto, H. S., Wilamowski, J., Xie, J., et al. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.07.008 · Cited by 35

A benchmark testing ground for integrating homology modeling and protein docking

Bohnuud, T., Luo, L., Wodak, S. J., Bonvin, A. M. J. J., Weng, Z., Vajda, S., Schueler‐Furman, O., & Kozakov, D. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25063 · Cited by 33

Modeling Peptide-Protein Structure and Binding Using Monte Carlo Sampling Approaches: Rosetta FlexPepDock and FlexPepBind

Alam, N., & Schueler‐Furman, O. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-6798-8_9 · Cited by 33

Protein–protein interaction specificity is captured by contact preferences and interface composition

Nadalin, F., & Carbone, A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx584 · Cited by 33

Local Interaction Signal Analysis Predicts Protein-Protein Binding Affinity

Raucci, R., Laine, É., & Carbone, A. (2018) · Structure · doi:10.1016/j.str.2018.04.006 · Cited by 32

Enhanced antibody-antigen structure prediction from molecular docking using AlphaFold2

Gaudreault, F., Corbeil, C. R., & Sulea, T. I. (2023) · Scientific Reports · doi:10.1038/s41598-023-42090-5 · Cited by 32

ISPRED4: interaction sites PREDiction in protein structures with a refining grammar model

Savojardo, C., Fariselli, P., Martelli, P. L., & Casadio, R. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx044 · Cited by 34

IRaPPA: information retrieval based integration of biophysical models for protein assembly selection

Moal, I. H., Barradas‐Bautista, D., Jiménez‐García, B., Torchala, M., van der Velde, A., Vreven, T., Weng, Z., Bates, P. A., & Fernández‐Recio, J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx068 · Cited by 33

InterEvDock3: a combined template-based and free docking server with increased performance through explicit modeling of complex homologs and integration of covariation-based contact maps

Quignot, C., Postic, G., Bret, H., Rey, J., Granger, P., Murail, S., Chacón, P., Andréani, J., Tufféry, P., & Guérois, R. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab358 · Cited by 34

Beyond sequence: Structure-based machine learning

Durairaj, J., de Ridder, D., & van Dijk, A. D. J. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.12.039 · Cited by 32

AbAdapt: an adaptive approach to predicting antibody–antigen complex structures from sequence

Davila, A., Xu, Z., Li, S., Rozewicki, J., Wilamowski, J., Kotelnikov, S., Kozakov, D., Teraguchi, S., & Standley, D. M. (2022) · Bioinformatics Advances · doi:10.1093/bioadv/vbac015 · Cited by 33

Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model

Anishchenko, I., Kundrotas, P. J., & Vakser, I. A. (2018) · Biophysical Journal · doi:10.1016/j.bpj.2018.07.035 · Cited by 29

Quantification of biases in predictions of protein–protein binding affinity changes upon mutations

Tsishyn, M., Pucci, F., & Rooman, M. J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad491 · Cited by 33

Docking approaches for modeling multi-molecular assemblies

Rosell, M., & Fernández‐Recio, J. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.05.016 · Cited by 28

A gated graph transformer for protein complex structure quality assessment and its performance in CASP15

Chen, X., Morehead, A., Liu, J., & Cheng, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad203 · Cited by 29

ProPOSE: Direct Exhaustive Protein–Protein Docking with Side Chain Flexibility

Hogues, H., Gaudreault, F., Corbeil, C. R., Deprez, C., Sulea, T. I., & Purisima, E. O. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00225 · Cited by 27

Protein–Protein Interaction Interfaces and their Functional Implications

Schreiber, G. (2020) · Royal Society of Chemistry eBooks · doi:10.1039/9781788016544-00001 · Cited by 29

The Subtle Trade-Off between Evolutionary and Energetic Constraints in Protein–Protein Interactions

Marchetti, F., Capelli, R., Rizzato, F., Laio, A., & Colombo, G. (2019) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.9b00191 · Cited by 26

Application of site-identification by ligand competitive saturation in computer-aided drug design

Goel, H., Hazel, A. J., Yu, W., Jo, S., & MacKerell, A. D. (2021) · New Journal of Chemistry · doi:10.1039/d1nj04028f · Cited by 25

Mapping of antibody epitopes based on docking and homology modeling

Desta, I., Kotelnikov, S., Jones, G. S., Ghani, U., Abyzov, M., Kholodov, Y. A., Standley, D. M., Sabitova, M., Beglov, D., Vajda, S., et al. (2022) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26420 · Cited by 25

Challenges in predicting PROTAC-mediated protein–protein interfaces with AlphaFold reveal a general limitation on small interfaces

Pereira, G. P., Gouzien, C., Souza, P. C. T., & Martin, J. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf056 · Cited by 26

Paratope states in solution improve structure prediction and docking

Fernández‐Quintero, M. L., Vangone, A., Loeffler, J. R., Seidler, C. A., Georges, G. J., & Liedl, K. R. (2021) · Structure · doi:10.1016/j.str.2021.11.001 · Cited by 25

The Current Limits in Virtual Screening and Property Prediction

Hutter, M. C. (2018) · Future Medicinal Chemistry · doi:10.4155/fmc-2017-0303 · Cited by 24

Protein–Protein Interaction Methods and Protein Phase Separation

Savojardo, C., Martelli, P. L., & Casadio, R. (2020) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-011720-104428 · Cited by 26

Neural message passing for joint paratope-epitope predictionPreprint

Vecchio, A. D., Deac, A., Lió, P., & Veličković, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00757 · Cited by 31

DIPS-Plus: The enhanced database of interacting protein structures for interface prediction

Morehead, A., Chen, C., Sedova, A., & Cheng, J. (2023) · Scientific Data · doi:10.1038/s41597-023-02409-3 · Cited by 26

PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery

Liu, H., Chen, P., Zhai, X., Huo, K.-G., Zhou, S., Han, L., & Fan, G. (2024) · Scientific Data · doi:10.1038/s41597-024-03997-4 · Cited by 30

A membrane protein complex docking benchmark

Koukos, P. I., Faro, I., van Noort, C. W., & Bonvin, A. M. J. J. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.11.005 · Cited by 23

A Structure-Based B-cell Epitope Prediction Model Through Combing Local and Global Features

Lu, S., Li, Y., Ma, Q., Nan, X., & Zhang, S. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.890943 · Cited by 23

Discriminating physiological from non‐physiological interfaces in structures of protein complexes: A community‐wide study

Schweke, H., Xu, Q., Tauriello, G., Pantolini, L., Schwede, T. F., Cazals, F., Lhéritier, A., Fernández‐Recio, J., Rodríguez‐Lumbreras, L. A., Schueler‐Furman, O., et al. (2023) · PROTEOMICS · doi:10.1002/pmic.202200323 · Cited by 23

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

Barradas‐Bautista, D., Rosell, M., Pallara, C., & Fernández‐Recio, J. (2017) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2017.06.003 · Cited by 24

Different protein-protein interface patterns predicted by different machine learning methods

Wang, W., Yang, Y. X., Yin, J., & Gong, X. (2017) · Scientific Reports · doi:10.1038/s41598-017-16397-z · Cited by 23

Integrating Cross-Linking Experiments with Ab Initio Protein–Protein Docking

Vreven, T., Schweppe, D. K., Chavez, J. D., Weisbrod, C. R., Shibata, S., Zheng, C., Bruce, J. E., & Weng, Z. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.04.010 · Cited by 23

Deep Local Analysis deconstructs protein–protein interfaces and accurately estimates binding affinity changes upon mutation

Mohseni Behbahani, Y., Laine, E., & Carbone, A. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad231 · Cited by 23

Binding free energy analysis of protein-protein docking model structures by evERdock

Takemura, K., Matubayasi, N., & Kitao, A. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019864 · Cited by 22

PepPro: A Nonredundant Structure Data Set for Benchmarking Peptide–Protein Computational Docking

Xu, X., & Zou, X. (2019) · Journal of Computational Chemistry · doi:10.1002/jcc.26114 · Cited by 22

Importance of interface and surface areas in protein-protein binding affinity prediction: A machine learning analysis based on linear regression and artificial neural network

Yang, Y. X., Wang, P., & Zhu, B. T. (2021) · Biophysical Chemistry · doi:10.1016/j.bpc.2022.106762 · Cited by 23

Rotation invariance and equivariance in 3D deep learning: a survey

Fei, J., & Deng, Z. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10741-2 · Cited by 22

Coarse‐grained and atomic resolution biomolecular docking with the ATTRACT approach

Glashagen, G., de Vries, S. J., Uciechowska‐Kaczmarzyk, U., Samsonov, S. A., Murail, S., Tufféry, P., & Zacharias, M. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25860 · Cited by 23

Topological links in predicted protein complex structures reveal limitations of AlphaFold

(2023) · doi:10.1038/s42003-023-05489-4 · Cited by 21

Deep Learning for Flexible and Site-Specific Protein Docking and DesignPreprint

McPartlon, M., & Xu, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.01.535079 · Cited by 24

Structural analogue-based protein structure domain assembly assisted by deep learning

Peng, C.-X., Zhou, X.-G., Xia, Y.-H., Liu, J., Hou, M.-H., & Zhang, G.-J. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac553 · Cited by 21

ComDock: A novel approach for protein-protein docking with an efficient fusing strategy

Meng, Q., Guo, F., Wang, E., & Tang, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107660 · Cited by 20

Integrating ab initio and template-based algorithms for protein–protein complex structure prediction

Vangaveti, S., Vreven, T., Zhang, Y., & Weng, Z. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz623 · Cited by 20

Information-Driven Docking for TCR-pMHC Complex Prediction

Peacock, T., & Chain, B. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.686127 · Cited by 20

TRScore: a 3D RepVGG-based scoring method for ranking protein docking models

Guo, L., He, J., Lin, P., Huang, S., & Wang, J. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac120 · Cited by 19

Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure

Zhao, N., Wu, T., Wang, W., Zhang, L., & Gong, X. (2024) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-024-00626-x · Cited by 20

Computational Methods to Predict Conformational B-Cell Epitopes

Carroll, M., Rosenbaum, E., & Viswanathan, R. (2024) · Biomolecules · doi:10.3390/biom14080983 · Cited by 19

PEPSI-Dock: a detailed data-driven protein–protein interaction potential accelerated by polar Fourier correlation

Neveu, É., Ritchie, D. W., Popov, P. A., & Grudinin, S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw443 · Cited by 19

A structure-based model for the prediction of protein–RNA binding affinity

Nithin, C., Mukherjee, S., & Bahadur, R. P. (2019) · RNA · doi:10.1261/rna.071779.119 · Cited by 19

Enhanced sampling of protein conformational states for dynamic cross‐docking within the protein‐protein docking server SwarmDock

Torchala, M., Gerguri, T., Chaleil, R. A. G., Gordon, P., Russell, F., Keshani, M., & Bates, P. A. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25851 · Cited by 19

Geometric Transformers for Protein Interface Contact PredictionPreprint

Morehead, A., Chen, C., & Cheng, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02423 · Cited by 19

Protein—protein binding supersites

Viswanathan, R., Fajardo, E. J., Steinberg, G., Haller, M. D., & Fiser, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006704 · Cited by 18

Template‐based modeling by ClusPro in CASP13 and the potential for using co‐evolutionary information in docking

Porter, K. A., Padhorny, D., Desta, I., Ignatov, M., Beglov, D., Kotelnikov, S., Sun, Z., Alekseenko, A. E., Anishchenko, I. V., Cong, Q., et al. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25808 · Cited by 18

A knowledge-based scoring function to assess quaternary associations of proteins

Dhawanjewar, A. S., Roy, A., & Madhusudhan, M. S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa207 · Cited by 18

Predicting Protein Functional Motions: an Old Recipe with a New Twist

Grudinin, S., Laine, É., & Hoffmann, A. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.03.020 · Cited by 18

Are transient protein-protein interactions more dispensable?

Ghadie, M. A., & Xia, Y. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010013 · Cited by 19

Estimating Absolute Protein–Protein Binding Free Energies by a Super Learner Model

Chaves, E. J. F., Sartori, J., Santos, W. M., da Cruz, C. H. B., Mhrous, E. N., Nacimento-Filho, M. F., Ferraz, M. V. F., & Lins, R. D. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01641 · Cited by 17

Spatom: a graph neural network for structure-based protein–protein interaction site prediction

Wu, H., Han, J., Zhang, S., Xin, G., Mou, C., & Liu, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad345 · Cited by 17

ABAG-docking benchmark: a non-redundant structure benchmark dataset for antibody–antigen computational docking

Zhao, N., Han, B., Zhao, C., Xu, J., & Gong, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae048 · Cited by 19

EpiScan: accurate high-throughput mapping of antibody-specific epitopes using sequence information

Wang, C., Wang, J., Song, W., Luo, G., & Jiang, T. (2024) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00432-7 · Cited by 17

TIMP2-mediated mitochondrial fragmentation and glycolytic reprogramming drive renal fibrogenesis following ischemia-reperfusion injury

Pang, J., Xu, D., Zhang, X., Qu, J., Jiang, J., Suo, J., Li, T., Li, Y., & Peng, Z. (2025) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2025.02.020 · Cited by 17

Rigid-Docking Approaches to Explore Protein–Protein Interaction Space

Matsuzaki, Y., Uchikoga, N., Ohue, M., & Akiyama, Y. (2016) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2016_41 · Cited by 18

GalaxyHeteromer: protein heterodimer structure prediction by template-based and ab initio docking

Park, T., Won, J., Baek, M., & Seok, C. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab422 · Cited by 17

Performance of ZDOCK and IRAD in CAPRI rounds 28–34

Vreven, T., Pierce, B. G., Borrman, T. M., & Weng, Z. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25186 · Cited by 17

Update of the ATTRACT force field for the prediction of protein-protein binding affinity

Chéron, J.-B., Zacharias, M., Antonczak, S., & Fiorucci, S. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24836 · Cited by 17

On the mechanisms of protein interactions: predicting their affinity from unbound tertiary structures

Marín-López, M. A., Planas-Iglesias, J., Aguirre‐Plans, J., Bonet, J., Garcı́a-Garcı́a, J., Fernández‐Fuentes, N., & Oliva, B. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx616 · Cited by 16

MVSF-AB: accurate antibody–antigen binding affinity prediction via multi-view sequence feature learning

Li, M., Shi, Y., Hu, S., Hu, S., Guo, P., Wan, W., Zhang, L. Y., Pan, S., Li, J., Sun, L., et al. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae579 · Cited by 16

Protein Docking Using a Single Representation for Protein Surface, Electrostatics, and Local Dynamics

Rudden, L. S. P., & Degiacomi, M. T. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00474 · Cited by 16

A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community)

Orengo, C. A., Velankar, S. S., Wodak, S. J., Zoete, V., Bonvin, A. M. J. J., Elofsson, A., Feenstra, K. A., Gerloff, D. L., Hamelryck, T., Hancock, J. M., et al. (2020) · F1000Research · doi:10.12688/f1000research.20559.1 · Cited by 16

Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles

Saldinger, J. C., Raymond, M., Elvati, P., & Violi, A. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00438-x · Cited by 16

Induced fit with replica exchange improves protein complex structure prediction

Harmalkar, A., Mahajan, S. P., & Gray, J. J. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010124 · Cited by 16

Protein Interaction Energy Landscapes are Shaped by Functional and also Non-functional Partners

Schweke, H., Mucchielli, M.-H., Sacquin-Mora, S., Bei, W., & Lopes, A. (2020) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.12.047 · Cited by 15

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

MPAD: A Database for Binding Affinity of Membrane Protein–protein Complexes and their Mutants

Ridha, F., Kulandaisamy, A., & Gromiha, M. M. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167870 · Cited by 15

A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond

Jia, P., Zhang, F., Wu, C., & Li, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae162 · Cited by 15

Conformational changes in antibody Fab fragments upon binding and their consequences on the performance of docking algorithms

Barozet, A., Bianciotto, M., Siméon, T., Minoux, H., & Cortés, J. (2018) · Immunology Letters · doi:10.1016/j.imlet.2018.06.002 · Cited by 15

Performance of ZDOCK and IRAD in CAPRI rounds 39‐45

Vreven, T., Vangaveti, S., Borrman, T. M., Gaines, J. C., & Weng, Z. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25873 · Cited by 15

Transmembrane Protein Docking with JabberDock

Rudden, L. S. P., & Degiacomi, M. T. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01315 · Cited by 15

Hidden partners: Using cross‐docking calculations to predict binding sites for proteins with multiple interactions

Lagarde, N., Carbone, A., & Sacquin‐Mora, S. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25506 · Cited by 15

Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength

Zheng, F., Jiang, X., Wen, Y., Yang, Y., & Li, M. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.12.018 · Cited by 14

GDockScore: a graph-based protein–protein docking scoring function

McFee, M. C., & Kim, P. M. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad072 · Cited by 14

PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models

Xie, J., Zhang, Y., Wang, Z., Jin, X., Lü, X., Ge, S., & Min, X. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06123-2 · Cited by 15

Galaxy InteractoMIX: An Integrated Computational Platform for the Study of Protein–Protein Interaction Data

Bota, P. M., Aguirre‐Plans, J., Meseguer, A., Cristiano, G., Segura, J., Planas-Iglesias, J., Garcı́a-Garcı́a, J., Güney, E., Oliva, B., & Fernández‐Fuentes, N. (2020) · Journal of Molecular Biology · doi:10.1016/j.jmb.2020.09.015 · Cited by 14

Machine-learning-assisted universal protein activation in living mice

Wang, X., Liu, Y., Wang, Z., Zeng, X., Ngai, W. S. C., Wang, J., Zhang, H., Xie, X., Zhu, R., Fan, X., et al. (2025) · Cell · doi:10.1016/j.cell.2025.05.006 · Cited by 14

Benchmarking of structure refinement methods for protein complex models

Verburgt, J. C., & Kihara, D. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26188 · Cited by 14

MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein–Protein Docking Conformations

Jung, Y. G., Geng, C., Bonvin, A. M. J. J., Xue, L. C., & Honavar, V. (2023) · Biomolecules · doi:10.3390/biom13010121 · Cited by 13

Integrating diverse experimental information to assist protein complex structure prediction by GRASP

Xie, Y., Zhang, C., Li, S., Du, X., Lu, Y., Wang, M., Hu, Y., Chen, Z., Liu, S., & Gao, Y. Q. (2025) · Nature Methods · doi:10.1038/s41592-025-02820-1 · Cited by 14

A random forest classifier for protein–protein docking models

Barradas‐Bautista, D., Cao, Z. Z., Vangone, A., Oliva, R., & Cavallo, L. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab042 · Cited by 13

From complete cross-docking to partners identification and binding sites predictions

Dequeker, C., Behbahani, Y. M., David, L., Laine, É., & Carbone, A. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009825 · Cited by 13

Plausible blockers of Spike RBD in SARS-CoV2—molecular design and underlying interaction dynamics from high-level structural descriptors

Basu, S., Chakravarty, D., Bhattacharyya, D., Saha, P., & Patra, H. K. (2021) · Journal of Molecular Modeling · doi:10.1007/s00894-021-04779-0 · Cited by 13

Deciphering interaction fingerprints from protein molecular surfacesPreprint

Gaínza, P., Sverrisson, F., Monti, F., Rodolà, E., Bronstein, M., & Correia, B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/606202 · Cited by 13

Residue-Frustration-Based Prediction of Protein–Protein Interactions Using Machine Learning

Zhou, X., Song, H., & Li, J. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c10525 · Cited by 12

DockNet: high-throughput protein–protein interface contact prediction

Williams, N. P., Rodrigues, C. H. M., Truong, J. Q., Ascher, D. B., & Holien, J. K. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac797 · Cited by 12

DeepBindPPI: Protein–Protein Binding Site Prediction Using Attention Based Graph Convolutional Network

Sunny, S., Prakash, P. B., Gopakumar, G., & Jayaraj, P. B. (2023) · The Protein Journal · doi:10.1007/s10930-023-10121-9 · Cited by 12

Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking

Sasse, A., de Vries, S. J., Schindler, C., de Beauchêne, I. C., & Zacharias, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170625 · Cited by 12

A systematic analysis of scoring functions in rigid‐body protein docking: The delicate balance between the predictive rate improvement and the risk of overtraining

Barradas‐Bautista, D., Moal, I. H., & Fernández‐Recio, J. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25289 · Cited by 12

Prediction of Biomolecular Complexes

Vangone, A., Oliva, R., Cavallo, L., & Bonvin, A. M. J. J. (2017) · Springer eBooks · doi:10.1007/978-94-024-1069-3_8 · Cited by 13

Identification and characterization of a neutralizing monoclonal antibody that provides complete protection against Yersinia pestis

Liu, W., Ren, J., Zhang, J., Song, X., Liu, S., Chi, X., Chen, Y., Wen, Z., Li, J., & Chen, W. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177012 · Cited by 12

Are Scoring Functions in Protein−Protein Docking Ready to Predict Interactomes? Clues from a Novel Binding Affinity Benchmark

Kastritis, P. L., & Bonvin, A. M. J. J. (2010) · Journal of Proteome Research · doi:10.1021/pr101118t · Cited by 21

Protein–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 474

Protein–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

An Expanded Benchmark for Antibody-Antigen Docking and Affinity Prediction Reveals Insights into Antibody Recognition Determinants

Guest, 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 124

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