Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes

The Protein Data Bank

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

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 5,018 · see citing works

MolProbity: all-atom contacts and structure validation for proteins and nucleic acids

Davis, I., Leaver‐Fay, A., Chen, V. B., Block, J. N., Kapral, G. J., Wang, X., Murray, L. W., Arendall, W. B., Snoeyink, J. S., Richardson, J. S., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm216 · Cited by 3,915 · 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

The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes

van Zundert, G. C. P., Rodrigues, J. P. G. L. M., Trellet, M., Schmitz, C., Kastritis, P. L., Karaca, E., Melquiond, A. S. J., van Dijk, M., de Vries, S. J., & Bonvin, A. M. J. J. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.09.014 · Cited by 2,785 · see citing works

mCSM: Predicting the effects of mutations in proteins using graph-based signatures

Pires, D. E. V., Ascher, D. B., & Blundell, T. L. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt691 · Cited by 1,066 · see citing works

Contacts-based prediction of binding affinity in protein–protein complexes

Vangone, A., & Bonvin, A. (2015) · eLife · doi:10.7554/elife.07454 · Cited by 563 · see citing works

On 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 519 · see citing works

Protein−Protein Interactions: Interface Structure, Binding Thermodynamics, and Mutational Analysis

Stites, W. E. (1997) · Chemical Reviews · doi:10.1021/cr960387h · Cited by 491 · see citing works

BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations

Dehouck, Y., Kwasigroch, J. M., Rooman, M., & Gilis, D. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt450 · Cited by 338 · see citing works

A 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

SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models

Moal, I. H., & Fernández-Recio, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts489 · Cited by 281 · see citing works

Identification 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

A simple definition of structural regions in proteins and its use in analyzing interface evolution

Levy, E. D. (2010) · Journal of Molecular Biology · doi:10.1016/j.jmb.2010.09.028 · Cited by 254 · see citing works

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/pr9009854 · Cited by 246 · see citing works

Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: A CASP‐CAPRI experiment

Lensink, M. F., Velankar, S. S., Kryshtafovych, A., Huang, S., Schneidman‐Duhovny, D., Šali, A., Segura, J., Fernández‐Fuentes, N., Viswanath, S., Elber, R., et al. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25007 · Cited by 153 · see citing works

Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles

Brender, J. R., & Zhang, Y. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004494 · Cited by 140 · see citing works

Predicting the Impact of Missense Mutations on Protein–Protein Binding Affinity

Li, M., Petukh, M., Alexov, E., & Panchenko, A. R. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct401022c · Cited by 111 · see citing works

Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

Berliner, N., Teyra, J., Çolak, R., Lopez, S. G., & Kim, P. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107353 · Cited by 79 · see citing works

A multiscale approach to predicting affinity changes in protein-protein interfaces: Multiscale Method for Predicting ΔΔG

Dourado, D. F. A. R., & Flores, S. C. (2014) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.24634 · Cited by 56 · see citing works

Intermolecular Contact Potentials for Protein–Protein Interactions Extracted from Binding Free Energy Changes upon Mutation

Moal, I. H., & Fernández‐Recio, J. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400295z · Cited by 35 · see citing works

SKEMPI 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation

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

Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein–Protein Binding Affinity upon Mutation

Barlow, K. A., Conchúir, S. Ó., Thompson, S., Suresh, P., Lucas, J. E., Heinonen, M., & Kortemme, T. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.7b11367 · Cited by 298 · see citing works

Protein–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

SAAMBE-3D: Predicting Effect of Mutations on Protein–Protein Interactions

Pahari, S., Li, G., Murthy, A. K., Liang, S., Fragoza, R., Yu, H., & Alexov, E. G. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21072563 · Cited by 109 · see citing works

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Geng, C., Xue, L. C., Roel‐Touris, J., & Bonvin, A. M. J. J. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1410 · Cited by 101 · see citing works

iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations

Geng, C., Vangone, A., Folkers, G. E., Xue, L. C., & Bonvin, A. M. J. J. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25630 · Cited by 82 · see citing works

ATLAS: A database linking binding affinities with structures for wild-type and mutant TCR-pMHC complexes

Borrman, T. M., Cimons, J. M., Cosiano, M., Purcaro, M. J., Pierce, B. G., Baker, B. M., & Weng, Z. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25260 · Cited by 78 · see citing works

Recent advances in user-friendly computational tools to engineer protein function

Sequeiros-Borja, C. E., Surpeta, B., & Brezovský, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa150 · Cited by 79 · see citing works

Persistent spectral based ensemble learning (PerSpect-EL) for protein–protein binding affinity prediction

Wee, J., & Xia, K. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac024 · Cited by 45 · 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

Application of Assisted Design of Antibody and Protein Therapeutics (ADAPT) improves efficacy of a Clostridium difficile toxin A single-domain antibody

Sulea, T. I., Hussack, G., Ryan, S., Tanha, J., & Purisima, E. O. (2018) · Scientific Reports · doi:10.1038/s41598-018-20599-4 · Cited by 38 · see citing works

Free energy perturbation calculations of mutation effects on SARS-CoV-2 RBD::ACE2 binding affinity

Sergeeva, A. P., Katsamba, P. S., Liao, J., Sampson, J. M., Bahna, F., Mannepalli, S., Morano, N. C., Shapiro, L., Friesner, R. A., & Honig, B. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168187 · Cited by 27 · see citing works

FlexPepDock lessons from CAPRI peptide–protein rounds and suggested new criteria for assessment of model quality and utility

Marcu, O., Dodson, E. J., Alam, N., Sperber, M., Kozakov, D., Lensink, M. F., & Schueler‐Furman, O. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25230 · Cited by 27 · see citing works

Protein–Protein Interaction Interfaces and their Functional Implications

Schreiber, G. (2020) · Royal Society of Chemistry eBooks · doi:10.1039/9781788016544-00001 · Cited by 26 · see citing works

Hom-Complex-Based Machine Learning (HCML) for the Prediction of Protein–Protein Binding Affinity Changes upon Mutation

Liu, X., Feng, H., Wu, J., & Xia, K. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00580 · Cited by 18 · see citing works

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation

Rana, M. M., & Nguyen, D. D. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c02679 · Cited by 11 · see citing works

Persistent Tor-algebra for protein–protein interaction analysis

Liu, X., Feng, H., Lü, Z., & Xia, K. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad046 · Cited by 10 · see citing works

Biomolecular Topology: Modelling and Analysis

Liu, J., Xia, K., Wu, J., Yau, S. S. ‐., & Wei, G. (2022) · Acta Mathematica Sinica English Series · doi:10.1007/s10114-022-2326-5 · Cited by 9 · see citing works

Decoding the effects of mutation on protein interactions using machine learning

Wang, X., Li, A., Zhao, Y., & Peng, Y. (2025) · Biophysics Reviews · doi:10.1063/5.0249920 · Cited by 6 · see citing works

An Electrostatically-steered Conformational Selection Mechanism Promotes SARS-CoV-2 Spike Protein Variation

Sorokina, M., Belapure, J., Tüting, C., Paschke, R., Papasotiriou, I., Rodrigues, J., & Kastritis, P. L. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167637 · Cited by 4 · see citing works

Categorization of hotspots into three types – weak, moderate and strong to distinguish protein–protein versus protein–peptide interactions

Kumar, A. K., & Rathore, R. S. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2252077 · Cited by 3 · see citing works

Dominant negative variants and cotranslational assembly of macromolecular complexes

Veitia, R. A. (2023) · BioEssays · doi:10.1002/bies.202300105 · Cited by 3 · see citing works

Excuse me, there is a mutant in my bioactivity soup!A comprehensive analysis of the genetic variability landscape of bioactivity databases and its effect on activity modellingPreprint

González, M. G., Béquignon, O. J. M., Manners, E. J., Gaulton, A., Mutowo, P., Dawson, E., Zdrazil, B., Leach, A. R., IJzerman, A. P., Heitman, L. H., et al. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-kxlgm · Cited by 3 · see citing works

Leveraging Neural Networks to Correct FoldX Free Energy Estimates

Barnes, J. E., Chi, L. A., Pandey, S. K., Ytreberg, F. M., & Patel, J. S. (2026) · ACS Omega · doi:10.1021/acsomega.6c01409 · Cited by 2 · see citing works

Predicting interacting hotspots for nanobodies' binding using triplets of residues

Hamdani, R., Cianferoni, D., Reche, R., Delgado, J., & Serrano, L. G. (2025) · Protein Science · doi:10.1002/pro.70220 · Cited by 1 · see citing works

Arayüz Mutasyonlarının Protein Etkileşimlerine Tesirini Tahmin Eden Algoritmalarla HADDOCK’un Performansının Karşılaştırılması

Koşaca, M., Şamiloğlu, E., & Karaca, E. (2021) · International Journal of Advances in Engineering and Pure Sciences · doi:10.7240/jeps.920075

Three types of hotspots- weak, moderate and strong in protein-protein and protein-peptide interactionsPreprint

Kumar, A. K., & Rathore, R. S. (2022) · doi:10.22541/au.167101052.24746048/v1

Geometric and topological AI for molecular sciences

Wee, J. (2023) · doi:10.32657/10356/165903

PDA-MutPred: Reliable prediction of binding affinity change upon mutation in protein-DNA complexes

Harini, K., Yasuo, N., Sekijima, M., & Gromiha, M. M. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.153205 · Cited by 1 · see citing works

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Geng, C., Xue, L. C., Roel‐Touris, J., & Bonvin, A. M. J. J. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1410 · Cited by 101 · see citing works

PCA-MutPred: Prediction of Binding Free Energy Change Upon Missense Mutation in Protein-carbohydrate Complexes

Shanmugam, N. R. S., Veluraja, K., & Gromiha, M. M. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167526 · Cited by 9 · see citing works

Accurate Estimation of Ligand Binding Affinity Changes upon Protein Mutation

Aldeghi, M., Gapsys, V., & de Groot, B. L. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00717 · Cited by 111 · see citing works

Software for Predicting Binding Free Energy of Protein–Protein Complexes and Their Mutants

Jarończyk, M. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3985-6_9