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

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Crystallography & NMR System: A New Software Suite for Macromolecular Structure Determination

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HADDOCK: A Protein−Protein Docking Approach Based on Biochemical or Biophysical Information

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ZDOCK: An initial‐stage protein‐docking algorithm

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In-silico analysis of ligand-receptor binding patterns of α-MMC, TCS and MAP30 protein to LRP1 receptor

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Benchmark Sets for Binding Hot Spot Identification in Fragment-Based Ligand Discovery

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CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning

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Domain-Based Protein Docking with Extremely Large Conformational Changes

Christoffer, C. W., & Kihara, D. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167820 · Cited by 12

A memetic algorithm enables efficient local and global all-atom protein-protein docking with backbone and side-chain flexibility

Varela, D., Karlin, V., & André, I. (2022) · Structure · doi:10.1016/j.str.2022.09.005 · Cited by 12

Computational Assessment of Protein–Protein Binding Affinity by Reversely Engineering the Energetics in Protein Complexes

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Challenges and Advantages of Accounting for Backbone Flexibility in Prediction of Protein–Protein Complexes

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Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

Larrea, A., Jebari‐Benslaiman, S., García, U. G., Urteaga, A. S. J., Uribe, K. B., Benito‐Vicente, A., & Martı́n, C. (2023) · Current Atherosclerosis Reports · doi:10.1007/s11883-023-01154-7 · Cited by 12

ComplexQA: a deep graph learning approach for protein complex structure assessment

Zhang, L., Wang, S., Hou, J., Si, D., Zhu, J., & Cao, R. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad287 · Cited by 11

HOPMA: Boosting Protein Functional Dynamics with Colored Contact Maps

Laine, É., & Grudinin, S. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c11633 · Cited by 11

FPDock: Protein–protein docking using flower pollination algorithm

Sunny, S., & Jayaraj, P. B. (2021) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2021.107518 · Cited by 11

Hierarchical representation for PPI sites prediction

Quadrini, M., Daberdaku, S., & Ferrari, C. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04624-y · Cited by 11

Interface refinement of low- to medium-resolution Cryo-EM complexes using HADDOCK2.4

Neijenhuis, T., van Keulen, S. C., & Bonvin, A. M. J. J. (2022) · Structure · doi:10.1016/j.str.2022.02.001 · Cited by 12

RRDB: a comprehensive and non-redundant benchmark for RNA–RNA docking and scoring

Yan, Y., & Huang, S. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx615 · Cited by 11

Improving B-cell epitope prediction

Yu, H., Joseph‐McCarthy, D., & Vajda, S. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104489 · Cited by 11

Analysis of Protein-Protein Interaction Networks through Computational Approaches

Han, Y., Cheng, L., & Sun, W. (2019) · Protein and Peptide Letters · doi:10.2174/0929866526666191105142034 · Cited by 10

Transient association between proteins elicits alteration of dynamics at sites far away from interfaces

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Protein–Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Kurciński, M., Kmiecik, S., Zalewski, M., & Koliński, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22147341 · Cited by 10

INTERCAAT: identifying interface residues between macromolecules

Grudman, S., Fajardo, J. E., & Fiser, A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab596 · Cited by 10

IRAA : A statistical tool for investigating a protein–protein interaction interface from multiple structures

Belapure, J., Sorokina, M., & Kastritis, P. L. (2022) · Protein Science · doi:10.1002/pro.4523 · Cited by 10

Development of a New Benchmark for Assessing the Scoring Functions Applicable to Protein–Protein Interactions

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Computational Modeling of Designed Ankyrin Repeat Protein Complexes with Their Targets

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EROS-DOCK: protein–protein docking using exhaustive branch-and-bound rotational search

Echartea, M. E. R., de Beauchêne, I. C., & Ritchie, D. W. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz434 · Cited by 10

Predicting Protein–Protein Interfaces that Bind Intrinsically Disordered Protein Regions

Wong, E. T. C., & Gsponer, J. A. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.06.010 · Cited by 10

PDB-wide identification of physiological hetero-oligomeric assemblies based on conserved quaternary structure geometry

Dey, S., & Levy, E. D. (2021) · Structure · doi:10.1016/j.str.2021.07.012 · Cited by 10

Enhanced Molecular Dynamics Method to Efficiently Increase the Discrimination Capability of Computational Protein–Protein Docking

Scafuri, N., Soler, M. A., Spitaleri, A., & Rocchia, W. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00789 · Cited by 10

Identifying functionally informative evolutionary sequence profiles

Gil, N., & Fiser, A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx779 · Cited by 10

Dissecting water binding sites at protein–protein interfaces: a lesson from the atomic structures in the Protein Data Bank

Mukherjee, S., Nithin, C., Divakaruni, Y., & Bahadur, R. P. (2018) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2018.1453379 · Cited by 10

AF3Score: A Score-Only Adaptation of AlphaFold3 for Biomolecular Structure Evaluation

Liu, Y., Yu, Q., Wang, D., & Chen, M. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00653 · Cited by 9

ParaSurf: a surface-based deep learning approach for paratope–antigen interaction prediction

Papadopoulos, A.-M., Axenopoulos, A., Iatrou, A., Stamatopoulos, K., Alvarez, F., & Daras, P. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf062 · Cited by 9

Computational methods for modeling protein–protein interactions in the AI era: Current status and future directions

Li, H., Nithin, C., Kmiecik, S., & Huang, S. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104382 · Cited by 9

A non-redundant benchmark for symmetric protein docking

Yan, Y., & Huang, S. (2019) · Big Data Mining and Analytics · doi:10.26599/bdma.2018.9020035 · Cited by 9

CSM‐Potential2 : A comprehensive deep learning platform for the analysis of protein interacting interfaces

Rodrigues, C. H. M., & Ascher, D. B. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26615 · Cited by 9

Focused grid‐based resampling for protein docking and mapping

Mamonov, A. B., Moghadasi, M., Mirzaei, H., Zarbafian, S., Grove, L. E., Bohnuud, T., Vakili, P., Paschalidis, I. C., Vajda, S., & Kozakov, D. (2016) · Journal of Computational Chemistry · doi:10.1002/jcc.24273 · Cited by 9

A new probability method to understand protein-protein interface formation mechanism at amino acid level

Yang, Y. X., & Gong, X. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.09.026 · Cited by 9

Unique Physicochemical Patterns of Residues in Protein–Protein Interfaces

Lázár, T., Guharoy, M., Schád, É., & Tompa, P. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00270 · Cited by 10

ProInterVal: Validation of Protein–Protein Interfaces through Learned Interface Representations

Ovek, D., Keskin, O., & Gursoy, A. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01788 · Cited by 8

Quantum Chemistry-Based Protein–Protein Docking without Empirical Parameters

Kousaka, S., & Ishikawa, T. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00531 · Cited by 8

Pair-EGRET: enhancing the prediction of protein–protein interaction sites through graph attention networks and protein language models

Alam, R., Mahbub, S., & Bayzid, M. S. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae588 · Cited by 8

Inclusion of Desolvation Energy into Protein–Protein Docking through Atomic Contact Potentials

Li, H., Yan, Y., Zhao, X., & Huang, S. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01483 · Cited by 8

FTDMP : A Framework for Protein–Protein, Protein– DNA, and Protein– RNA Docking and Scoring

Olechnovič, K., Banciul, R., Dapkūnas, J., & Venclovas, Č. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26792 · Cited by 9

Dockground Tool for Development and Benchmarking of Protein Docking Procedures

Kundrotas, P. J., Kotthoff, I., Choi, S. W., Copeland, M. M., & Vakser, I. A. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0708-4_17 · Cited by 8

Improved cluster ranking in protein–protein docking using a regression approach

Sotudian, S., Desta, I., Hashemi, N., Zarbafian, S., Kozakov, D., Vakili, P., Vajda, S., & Paschalidis, I. C. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.04.028 · Cited by 9

Exploring effectiveness of ab-initio protein–protein docking methods on a novel antibacterial protein complex dataset

Zhang, W., Meng, Q., Tang, J., & Guo, F. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab150 · Cited by 8

Integrated structure-based protein interface prediction

Walder, M., Edelstein, E., Carroll, M., Lazarev, S., Fajardo, J. E., Fiser, A., & Viswanathan, R. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04852-2 · Cited by 8

Determining Complex Structures using Docking Method with Single Particle Scattering Data

Wang, H., & Liu, H. (2017) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2017.00023 · Cited by 8

Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes

Khazen, G., Gyulkhandanian, A., Issa, T., & Maroun, R. C. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.09.013 · Cited by 8

RosettaSurf—A surface-centric computational design approach

Scheck, A., Rosset, S., Defferrard, M., Loukas, A., Bonet, J., Vandergheynst, P., & Correia, B. E. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009178 · Cited by 8

Extension of research data repository system to support direct compute access to biomedical datasets: enhancing Dataverse to support large datasets

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Morphing and docking visualisation of biomolecular structures using Multi-Dimensional Scaling

Veevers, R., & Hayward, S. (2018) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2018.04.013 · Cited by 8

Reliable protein-protein docking with AlphaFold, Rosetta, and replica-exchangePreprint

Harmalkar, A., Lyskov, S., & Gray, J. J. (2024) · eLife · doi:10.7554/elife.94029.1 · Cited by 8

GeoNet enables the accurate prediction of protein-ligand binding sites through interpretable geometric deep learning

Han, J., Zhang, S., Guan, M., Li, Q., Gao, X., & Liu, J. (2024) · Structure · doi:10.1016/j.str.2024.10.011 · Cited by 7

Unified Sampling and Ranking for Protein Docking With DFMDock

Chu, L., Sarma, S., Xu, D., & Gray, J. J. (2026) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.70160 · Cited by 7

Predicting the Initial Steps of Salt-Stable Cowpea Chlorotic Mottle Virus Capsid Assembly with Atomistic Force Fields

Antal, Z., Szövérfi, J., & Fejer, S. N. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00078 · Cited by 7

Hierarchical Representation and Graph Convolutional Networks for the Prediction of Protein–Protein Interaction Sites

Quadrini, M., Daberdaku, S., & Ferrari, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64580-9_34 · Cited by 7

Scoring of protein–protein docking models utilizing predicted interface residues

Pozzati, G., Kundrotas, P. J., & Elofsson, A. (2022) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26330 · Cited by 9

Properties that rank protein:protein docking poses with high accuracy

Simões, I. C. M., Coimbra, J. T. S., Neves, R. P. P., Costa, I. P. D., Ramos, M. J., & Fernandes, P. A. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp03888k · Cited by 7

Atomic-level evolutionary information improves protein–protein interface scoring

Quignot, C., Granger, P., Chacón, P., Guérois, R., & Andréani, J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab254 · Cited by 9

Simplified geometric representations of protein structures identify complementary interaction interfaces

McCafferty, C. L., Marcotte, E. M., & Taylor, D. W. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26020 · Cited by 7

iScore: An MPI supported software for ranking protein–protein docking models based on a random walk graph kernel and support vector machines

Renaud, N., Jung, Y. G., Honavar, V., Geng, C., Bonvin, A. M. J. J., & Xue, L. C. (2020) · SoftwareX · doi:10.1016/j.softx.2020.100462 · Cited by 7

Poxvirus targeted by RFdiffusion peptide-bindersPreprint

Coll, J. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.14.654163 · Cited by 7

SpatConv Enables the Accurate Prediction of Protein Binding Sites by a Pretrained Protein Language Model and an Interpretable Bio-spatial Convolution

Guan, M., Han, J., Zhang, S., Zheng, H., & Liu, J. (2025) · Research · doi:10.34133/research.0773 · Cited by 6

Advances in protein-protein interaction prediction: a deep learning perspective

Alkhateeb, N. J., & Awad, M. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1710937 · Cited by 6

From Traditional Methods to Deep Learning Approaches: Advances in Protein–Protein Docking

Jiang, L., Zhang, K., Zhu, K., Zhang, H., Shen, C., & Hou, T. (2025) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70016 · Cited by 6

Discrimination and Prediction of Protein-Protein Binding Affinity Using Deep Learning Approach

Nikam, R., Yugandhar, K., & Gromiha, M. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-95933-7_89 · Cited by 6

More efficient screening of protein-protein complex model structures for reducing the number of candidates

Takemura, K., & Kitao, A. (2019) · Biophysics and Physicobiology · doi:10.2142/biophysico.16.0_295 · Cited by 6

Dockground scoring benchmarks for protein docking

Kotthoff, I., Kundrotas, P. J., & Vakser, I. A. (2022) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26306 · Cited by 6

LEVELNET to visualize, explore, and compare protein–protein interaction networks

Behbahani, Y. M., Saighi, P., Corsi, F., Laine, É., & Carbone, A. (2023) · PROTEOMICS · doi:10.1002/pmic.202200159 · Cited by 7

Specificity of broad protein interaction surfaces for proteins with multiple binding partners

Uchikoga, N., Matsuzaki, Y., Ohue, M., & Akiyama, Y. (2016) · Biophysics and Physicobiology · doi:10.2142/biophysico.13.0_105 · Cited by 6

Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein–Protein Complexes

Yugandhar, K., & Gromiha, M. M. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6406-2_16 · Cited by 7

Geometric and amino acid type determinants for protein‐protein interaction interfaces

Yang, Y. X., Wang, W., Lou, Y., Yin, J., & Gong, X. (2018) · Quantitative Biology · doi:10.1007/s40484-018-0138-5 · Cited by 6

MaSIF - Deciphering interaction fingerprints from protein molecular surfaces

Gaínza, P., Sverrisson, F., Monti, F., Rodolà, E., Bronstein, M. M., & Correia, B. E. (2019) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.2625419 · Cited by 6

Effect of Structural Descriptors on the Design of Cyclin Dependent Kinase Inhibitors Using Similarity‐based Molecular Evolution

Kawai, K., Karuo, Y., Tarui, A., Sato, K., & Omote, M. (2020) · Molecular Informatics · doi:10.1002/minf.201900126 · Cited by 6

Reliable protein-protein docking with AlphaFold, Rosetta, and replica-exchangePreprint

Harmalkar, A., Lyskov, S., & Gray, J. J. (2025) · eLife · doi:10.7554/elife.94029.2 · Cited by 6

Advancing membrane-associated protein docking with improved sampling and scoring in Rosetta

Samnata, R., Harmalkar, A., Prathima, P., & Gray, J. J. (2024) · doi:10.1101/2024.07.09.602802 · Cited by 5

Protein–protein and protein–nucleic acid binding site prediction via interpretable hierarchical geometric deep learning

Zhang, S., Han, J., & Liu, J. (2024) · GigaScience · doi:10.1093/gigascience/giae080 · Cited by 5

Advancing Membrane-Associated Protein Docking with Improved Sampling and Scoring in Rosetta

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Transposable Elements Drive Regulatory and Functional Innovation of F-box Genes

Almeida, M. V., Li, Z., Rebelo‐Guiomar, P., Dallaire, A., Fiedler, L., Price, J. L., Sluka, J., Liu, X., Butter, F., Rödelsperger, C., et al. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf097 · Cited by 5

Beyond contacts: The important role of the support region in protein complex assembly

Miclot, T., & Timr, Š. (2026) · Protein Science · doi:10.1002/pro.70470 · Cited by 5

A computational framework for modeling functional protein‐protein interactions

Pal, A., Pal, D., & Mitra, P. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26156 · Cited by 5

Improving classification of correct and incorrect protein–protein docking models by augmenting the training set

Barradas‐Bautista, D., Almajed, A., Oliva, R., Kalnis, P., & Cavallo, L. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad012 · Cited by 5

Geometric epitope and paratope prediction

Pegoraro, M., Dominé, C., Rodolà, E., Veličković, P., & Deac, A. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae405 · Cited by 5

Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes

Zarbafian, S., Moghadasi, M., Roshandelpoor, A., Nan, F., Li, K., Vakli, P., Vajda, S., Kozakov, D., & Paschalidis, I. C. (2018) · Scientific Reports · doi:10.1038/s41598-018-23982-3 · Cited by 5

Side-chain Packing Using SE(3)-Transformer

Jindal, A., Kotelnikov, S., Padhorny, D., Kozakov, D., Zhu, Y., Chowdhury, R., & Vajda, S. (2021) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811250477_0005 · Cited by 5

Advanced computational tools for quantitative analysis of protein–nucleic acid interfaces

Mukherjee, S., & Nithin, C. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90264-9.00011-8 · Cited by 5

Prediction of the structural interface between fibroblast growth factor23 and Burosumab using alanine scanning and molecular docking

Kanhasut, K., Tharakaraman, K., Ruchirawat, M., Satayavivad, J., Fuangthong, M., & Sasisekharan, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-18580-3 · Cited by 5

Statistical database analysis of the role of loop dynamics for protein–protein complex formation and allostery

Gu, Y., Li, D., & Brüschweiler, R. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx070 · Cited by 5

Complex Stability is Encoded in Binding Patch Softness: a Monomer-Based Approach to Predict Inter-Subunit Affinity of Protein Dimers

Hadi‐Alijanvand, H. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00594 · Cited by 5

Prediction, Analysis, Visualization, and Storage of Protein–Protein Interactions Using Computational Approaches

Poluri, K. M., Gulati, K., & Sarkar, S. (2021) · Springer eBooks · doi:10.1007/978-981-16-1594-8_6 · Cited by 5

Computational design of protein complexes: influence of binding affinity

Ridha, F., Harini, K., Shanmugam, N. R. S., Nikam, R., & Gromiha, M. M. (2025) · Chemical Communications · doi:10.1039/d5cc04821d · Cited by 4

Multiscale chiral zeros in biomolecules

Moudgal, N., Ma, J., Emre, E. S. T., & Kotov, N. A. (2025) · Communications Chemistry · doi:10.1038/s42004-025-01808-4 · Cited by 4

Can we extract physics-like energies from generative protein diffusion Models?

Sarma, S., Truscott, H. H., Xu, D. Z., Reid, K. A., Chu, L., Chen, J. H. K., & Gray, J. J. (2026) · PRX Life · doi:10.1103/m7qr-gnjc · Cited by 5

The atypical thioredoxin ‘Alr2205’, a newly identified partner of the typical 2-Cys-Peroxiredoxin, safeguards the cyanobacterium Anabaena from oxidative stress

Banerjee, M., Waghamare, N., Kalwani, P., Hurali, D. T., Agarwal, R., & Ballal, A. D. (2023) · Biochemical Journal · doi:10.1042/bcj20220524 · Cited by 4

EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations

Biswas, G., Mukherjee, D., Dutta, N., Ghosh, P., & Basu, S. (2023) · Journal of Molecular Modeling · doi:10.1007/s00894-023-05626-0 · Cited by 4

Structural quality of unrefined models in protein docking

Anishchenko, I. V., Kundrotas, P. J., & Vakser, I. A. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25188 · Cited by 4

Partner-Specific Prediction of Protein-Dimer Stability from Unbound Structure of Monomer

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Trimer Protein-Protein Complex Interface Interacting Residue Pairs Prediction Using Deep Learning Approach

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