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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.
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Citations
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
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
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
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
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
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
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
Self-citation
Flagged first, then by citations
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Items 301–400 of 421
Generalizable Protein Interface Prediction with End-to-End Learning.Preprint
Townshend, R. J. L., Bedi, R., & Dror, R. O. (2018) · arXiv (Cornell University) · Cited by 4
ProteinFlow: a Python Library to Pre-Process Protein Structure Data for Deep Learning ApplicationsPreprint
Kozlova, E., Valentin, A., Khadhraoui, A., & Gutierrez, D. N.-Z. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.25.559346 · Cited by 4
BindingGYM: A Large-Scale Mutational Dataset Toward Deciphering Protein-Protein InteractionsPreprint
Lu, W., Zhang, J., Gu, M., & Zheng, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.03.626712 · Cited by 4
Diffusion generation of peptides crossdocking three finger-loops of snake-venomsPreprint
Coll, J. M. (2025) · ChemRxiv · doi:10.26434/chemrxiv-2025-lmr10 · Cited by 4
Impact of Structural Relaxation on Protein–Protein Docking in Large Macromolecular Complexes
Rosa, R. S. D. L., Bulla, A. C. S., Bernardi, R. C., & da Silva, M. L. (2025) · Applied Biosciences · doi:10.3390/applbiosci4040048 · Cited by 3
Kumar, A. K., & Rathore, R. S. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2252077 · Cited by 3
Docking Foundations: From Rigid to Flexible Docking
Kuder, K. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3985-6_1 · Cited by 3
EuDockScore: Euclidean graph neural networks for scoring protein–protein interfaces
McFee, M., Kim, J., & Kim, P. M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae636 · Cited by 3
Barhoon, M., & Mahdiuni, H. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01029 · Cited by 3
Revisiting Protein–Protein Docking: A Systematic Evaluation Framework
Jiang, L., Zhang, K., Zhu, K., Wang, Y., Kang, Y., & Hou, T. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01399 · Cited by 3
Classification of protein–protein association rates based on biophysical informatics
Dhusia, K., & Wu, Y. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04323-0 · Cited by 3
ProSPs: Protein Sites Prediction Based on Sequence Fragments
Quadrini, M., Cavallin, M., Daberdaku, S., & Ferrari, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-95467-3_41 · Cited by 3
In-silico analysis of potent Mosquirix vaccine adjuvant leads
Onyango, O. H., Mwenda, C. M., Gitau, G. W., Muoma, J. V. O., & OKOTH, P. K. (2023) · Journal of Genetic Engineering and Biotechnology · doi:10.1186/s43141-023-00590-x · Cited by 3
Intra-Inter Graph Representation Learning for Protein-Protein Binding Sites Prediction
Zhao, W., Xu, G., Wang, L., Cui, Z., Zhang, T., & Yang, J. J. (2024) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2024.3416341 · Cited by 3
Gowthaman, R., Park, M., Yin, R. M., Guest, J. D., & Pierce, B. G. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26801 · Cited by 3
Self-citationBertoni, M., & Aloy, P. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.09.011 · Cited by 3
Using restraints in EROS‐DOCK improves model quality in pairwise and multicomponent protein docking
Echartea, M. E. R., Ritchie, D. W., & de Beauchêne, I. C. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25959 · Cited by 3
Aoyama, K., Watanabe, H., Ohue, M., & Akiyama, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-48842-0_2 · Cited by 3
Kurciński, M., Kmiecik, S., Zalewski, M., & Koliński, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.22.432196 · Cited by 3
Improved protein docking by predicted interface residuesPreprint
Pozzati, G., Kundrotas, P. J., & Elofsson, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.22541/au.163254390.01798129/v1 · Cited by 4
Geometric Epitope and Paratope PredictionPreprint
Pegoraro, M., Dominé, C., Rodolà, E., Veličković, P., & Deac, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.29.546973 · Cited by 3
BoltzGen cyclic-minipeptides targeting Acinetobacter baumannii lipoprotein-transportersPreprint
Coll, J. M. (2026) · ChemRxiv · doi:10.26434/chemrxiv.10001541/v1 · Cited by 3
Information-driven modelling of antibody–antigen complexes
Ambrosetti, F., Jiménez-García, B., Roel-Touris, J., & Bonvin, A. M. J. J. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3362436 · Cited by 2
Self-citationBarman, P., Sharma, S., Joshi, S., Maan, M., Sharma, S., & Saini, A. (2025) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2025.126531 · Cited by 2
Zaverdas, H., Stojceski, F., Romero‐Zaliz, R., Androutsos, L., Makrygiannis, P., Pallante, L., Martos, V., Grasso, G., Deriu, M. A., Theofilatos, K., et al. (2025) · npj Science of Food · doi:10.1038/s41538-025-00478-9 · Cited by 2
The effect of hydration and dynamics on the mass density of single proteins
McAllister, C., Rudden, L. S. P., Bromley, E. H. C., & Degiacomi, M. T. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0276752 · Cited by 2
Wang, S., Ma, T., Dong, K., Han, P., Li, X., Ma, J., Li, M., & Song, T. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf470 · Cited by 2
Enhancing antibody-antigen interaction prediction with atomic flexibility
Joubbi, S., Micheli, A., Milazzo, P., Ciano, G., Gagné, S. M., Lió, P., Medini, D., & Maccari, G. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013576 · Cited by 2
Understanding the Roles of Secondary Shell Hotspots in Protein–Protein Complexes
Parvathy, J., Yazhini, A., Srinivasan, N., & Sowdhamini, R. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70074 · Cited by 2
Ghosh, S., & Mitra, P. (2026) · IEEE Access · doi:10.1109/access.2026.3658221 · Cited by 2
Molecular Imaging of Protein–Protein Interactions and Protein Folding
Massoud, T. F., & Paulmurugan, R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-816386-3.00071-5 · Cited by 2
A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models
Chen, X., Liu, J., Park, N., & Cheng, J. (2024) · Biomolecules · doi:10.3390/biom14050574 · Cited by 2
Biswas, G., Mukherjee, D., & Basu, S. (2024) · Journal of Computational Biology · doi:10.1089/cmb.2024.0554 · Cited by 2
Grassmann, G., Rienzo, L. D., Ruocco, G., Miotto, M., & Milanetti, E. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02286 · Cited by 2
Refinement of pairwise potentials via logistic regression to score protein‐protein interactions
Tanemura, K. A., Pei, J., & Merz, K. M. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25973 · Cited by 2
A Novel Protein Interface Prediction Framework via Hybrid Attention Mechanism
Wu, H., Luo, S., Zhao, W., Jiang, X., & He, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05981-0_29 · Cited by 2
In silico models of the macromolecular NaV1.5-KIR2.1 complex
Stary‐Weinzinger, A. (2024) · Frontiers in Physiology · doi:10.3389/fphys.2024.1362964 · Cited by 2
Dock2D: Synthetic Data for the Molecular Recognition Problem
Bhadra-Lobo, S., Derevyanko, G., & Lamoureux, G. (2024) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2024.3407477 · Cited by 3
Deep Learning Methods for Binding Site Prediction in Protein Structures
Geraseva, E. (2024) · Biochemistry (Moscow) Supplement Series B Biomedical Chemistry · doi:10.1134/s1990750823600498 · Cited by 2
Implication of Terminal Residues at Protein-Protein and Protein-DNA Interfaces
Martin, O. M. F., Éthève, L., Launay, G., & Martin, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162143 · Cited by 2
Constraining Redundancy to Improve Protein Docking
Krippahl, L., & Barahona, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44953-1_45 · Cited by 2
Modeling Protein Conformational Transition Pathways Using Collective Motions and the LASSO Method
Hayes, T. W., & Moal, I. H. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b01110 · Cited by 2
Self-citationBelkin, N. L. (2003) · PubMed · Cited by 2
A Geometric Complementarity-Based Tool for Protein–Protein Docking
Sunny, S., & Jayaraj, P. B. (2021) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s273741652250003x · Cited by 2
Giulini, M., Honorato, R. V., Rivera, J. L., & Bonvin, A. M. J. J. (2024) · Communications Biology · doi:10.1038/s42003-023-05718-w · Cited by 2
Self-citationDatabase-Driven Identification of Structurally Similar Protein-Protein Interfaces
Graef, J., Ehrt, C., Reim, T., & Rarey, M. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01462 · Cited by 2
Experimental methods to study the thermodynamics of protein–protein interactions
Sasidharan, S., Nag, N., Tripathi, T., & Saudagar, P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90264-9.00007-6 · Cited by 2
Wang, L. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/294074 · Cited by 2
Targeting fish betanodavirus with BoltzGen cyclic-minipeptidesPreprint
Coll, J. M. (2026) · ChemRxiv · doi:10.26434/chemrxiv.10001866/v1 · Cited by 2
Lü, L., Li, H., Su, T., & Ming, D. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25179172 · Cited by 1
A GFlowNet-Based World Model for Structural Protein–Protein Interaction Prediction on DB5
Oyelade, O. N., & Wang, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-4381-6_11 · Cited by 1
IgPose: a generative data-augmented pipeline for robust immunoglobulin–antigen binding prediction
Bui, T.-C., Chung, I., Lee, W., Ko, J., & Lee, J. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag076 · Cited by 1
DynaBench: Dynamic data for the docking benchmark
Barlas, A. B., Laurent, B., Karaca, E., Prévost, C., & Sacquin‐Mora, S. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169650 · Cited by 1
Fundamentals of molecular docking: Concepts, terminology, docking types and scoring functions
Ankit, Kumar, S., Kaushik, D., Thakur, A., & Sharma, S. C. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-49253-2.00019-2 · Cited by 1
Versini, R., Reys, V., Kravchenko, A., Honorato, R. V., & Bonvin, A. M. J. J. (2026) · Protein Science · doi:10.1002/pro.70793 · Cited by 1
Self-citationMachine Learning Approaches for Predicting Protein Complex Similarity
Farhoodi, R., Akbal-Delibas, B., & Haspel, N. (2016) · Journal of Computational Biology · doi:10.1089/cmb.2016.0137 · Cited by 1
Yang, Y. X., & Gong, X. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59575-7_36 · Cited by 1
BLRM: A Basic Linear Ranking Model for Protein Interface Prediction
Shariat, B., Neumann, D., & Ben‐Hur, A. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621174 · Cited by 1
Mezei, M. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1855-4_2 · Cited by 1
Principles of computational drug designing and drug repurposing—An algorithmic approach
Bagchi, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15280-1.00011-x · Cited by 1
Challenges of Protein-Protein Docking of the Membrane Proteins
Kiani, Y. S., & Jabeen, I. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3985-6_12 · Cited by 1
Assessment of scoring functions for computational models of protein-protein interfaces
Sumner, J., Brandt, N. L., Meng, G., Finlay, D. E., Grigas, A. T., Córdoba, A., Shattuck, M. D., & O’Hern, C. S. (2026) · Physical review. E · doi:10.1103/krhp-fxv3 · Cited by 1
Towards protein-protein docking with significant structural changes using CABS-dockPreprint
Ciemny, M. P., Kurciński, M., Koliński, A., & Kmiecik, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.09266 · Cited by 1
ResDock: Protein-Protein Docking Using Shape Complementarity of Surface ResiduesPreprint
Sunny, S., & Jayaraj, P. B. (2021) · doi:10.36227/techrxiv.13619462.v1 · Cited by 1
Gupta, R. (2021) · Research Publications (Maastricht University) · doi:10.26481/dis.20210304rg · Cited by 1
ResDock: Protein-Protein Docking Using Shape Complementarity of Surface ResiduesPreprint
Sunny, S., & Jayaraj, P. B. (2021) · doi:10.36227/techrxiv.13619462 · Cited by 1
Predicting small-molecule inhibition of protein complexesPreprint
Yaseen, A., Roy, S., Akhter, N., Ben‐Hur, A., & Minhas, F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.23.609286 · Cited by 1
Development of protein-protein docking methodology and benchmarking environment
Anishchenko, I. V. (2016) · KU ScholarWorks (University of Kansas)
Ranking Protein-Protein Binding Using Evolutionary Information and Machine Learning
Farhoodi, R., Akbal-Delibas, B., & Haspel, N. (2017) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3107411.3107497
Optimization and machine learning methods for Computational Protein Docking
Zarbafian, S. (2018) · OpenBU (Boston University)
CDB—a database for protein heterodimeric complexes
Aker, M., Ohanona, S., Fisher, S., Katsman, E., Dvorkin, S., Kopelowitz, E., Goldstein, M., Barnett‐Itzhaki, Z., & Amitay, M. (2018) · Protein Engineering Design and Selection · doi:10.1093/protein/gzy030
Rosell, M., & Fernández‐Recio, J. (2018) · UPCommons institutional repository (Universitat Politècnica de Catalunya)
Application of Deep Architecture in Bioinformatics
Sen, S., Das, R., Dasgupta, S., & Maulik, U. (2019) · Studies in big data · doi:10.1007/978-3-030-33966-1_9 · Cited by 1
Local Algorithms for Minimizing the Force Field for 3D Representation of Macromolecules
Yakovlev, P., Anikin, A. S., Bolshakova, O., Gasnikov, A. V., Gornov, A. Y., Ermak, T., Makarenko, D., Morozov, V. P., & Neterebskii, B. (2019) · Computational Mathematics and Mathematical Physics · doi:10.1134/s0965542519120030
Campos, L. B., Ferreira, J. R. B. D. C., & dos Santos, W. P. (2022) · Research Society and Development · doi:10.33448/rsd-v11i8.31222
Does Inter-Protein Contact Prediction Benefit from Multi-Modal Data and Auxiliary Tasks?Preprint
Talukder, A., Yin, R., Sun, Y., Shen, Y., & You, Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.29.518454
Nagaraju, M., & Liu, H. (2022) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798322011858
Discrimination of Protein-Protein and Protein-Peptide Interactions using Machine Learning MethodsPreprint
Kumar, A. K., & Rathore, R. S. (2024) · Research Square · doi:10.21203/rs.3.rs-4388519/v1
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
Wong, K., Subramanian, I., Stevens, E., & Chakraborty, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.11.642737
The potential of AI techniques in biomolecular simulations
Frasnetti, E., & Colombo, G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00033-9
Xu, L., Yang, Q., Dong, W., Li, X., Wang, K., Dong, S., Luo, G., Zhang, X., Yang, T., Gao, X., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.28.667136
RFdiffusion-peptides targeting Listeria prfA dimer-interfacesPreprint
Coll, J. M. (2025) · ChemRxiv · doi:10.26434/chemrxiv-2025-p27lh
Area-Based Selection of Binding Interfaces for Structural Prediction of Protein–Protein ComplexesPreprint
Yang, Y. X., & Zhu, B. T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.07.680849
Ricciardelli, T., Barradas‐Bautista, D., Cao, Z. Z., Chawla, M., Cavallo, L., & Oliva, R. (2025) · Protein Science · doi:10.1002/pro.70314
Udomwong, P., Pamonsupornwichit, T., Kodchakorn, K., & Tayapiwatana, C. (2025) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1684042
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Additional file 2 of Integrated structure-based protein interface prediction
Walder, M., Edelstein, E., Carroll, M., Lazarev, S., Fajardo, J. E., Fiser, A., & Viswanathan, R. (2022) · Figshare · doi:10.6084/m9.figshare.20374579
Dhusia, K., & Wu, Y. (2021) · Figshare · doi:10.6084/m9.figshare.15184665
Additional file 1 of Integrated structure-based protein interface prediction
Walder, M., Edelstein, E., Carroll, M., Lazarev, S., Fajardo, J. E., Fiser, A., & Viswanathan, R. (2022) · Figshare · doi:10.6084/m9.figshare.20374576
Ghosh, S., & Mitra, P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.28.702421
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Zhou, Q., Chomicz, D., Melvin, D. G., Griffiths, M., Yahiya, S., Reece, S. T., Pannérer, M.-M. L., & Krawczyk, K. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.25.713450
Géométrie des interactions protéiques
Dequeker, C. (2018) · doi:10.70675/e2579bb6zb2d7z4a4czb4b4zb35b76a5e3c1
Development and assessment of protein loop modeling methods : Application to CDR loops in antibodies
Barozet, A. (2019) · theses.fr (ABES) · doi:10.70675/cee89238z8630z463bzbc86z2d01ca39b316
Echartea, M. E. R. (2019) · theses.fr (ABES) · doi:10.70675/9f63a212z2148z4952za179z025cba3f7eb1
Exploration and structural modelling of protein interactions using evolutionary information
Quignot, C. (2020) · theses.fr (ABES) · doi:10.70675/c93aba27zbff8z4080za06cze186c92097dc
TINA: a geometry-aware post-hoc scoring framework for protein-protein interaction
Jiang, L., Li, Y.-R., Su, Q., Cheng, S., Shen, L.-J., Zhang, K., Zhu, K., Zhang, H., Shen, C., Kang, Y., et al. (2026) · Acta Pharmacologica Sinica · doi:10.1038/s41401-026-01937-z
A yeast-based assay to screen protein-protein interaction modulators
Sudevan, A. (2021) · theses.fr (ABES) · doi:10.70675/9ab04dcaz498fz43ebz8762z012f3f86154c
Metadata
- 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
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › 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 ›
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