Modeling protein–protein and protein–peptide complexes: CAPRI 6th edition
Open accessAbstract
We present the sixth report evaluating the performance of methods for predicting the atomic resolution structures of protein complexes offered as targets to the community-wide initiative on the Critical Assessment of Predicted Interactions (CAPRI). The evaluation is based on a total of 20,670 predicted models for 8 protein-peptide complexes, a novel category of targets in CAPRI, and 12 protein-protein targets in CAPRI prediction Rounds held during the years 2013-2016. For two of the protein-protein targets, the focus was on the prediction of side-chain conformation and positions of interfacial water molecules. Seven of the protein-protein targets were particularly challenging owing to their multicomponent nature, to conformational changes at the binding site, or to a combination of both. Encouragingly, the very large multiprotein complex with the nucleosome was correctly predicted, and correct models were submitted for the protein-peptide targets, but not for some of the challenging protein-protein targets. Models of acceptable quality or better were obtained for 14 of the 20 targets, including medium quality models for 13 targets and high quality models for 8 targets, indicating tangible progress of present-day computational methods in modeling protein complexes with increased accuracy. Our evaluation suggests that the progress stems from better integration of different modeling tools with docking procedures, as well as the use of more sophisticated evolutionary information to score models. Nonetheless, adequate modeling of conformational flexibility in interacting proteins remains an important area with a crucial need for improvement. Proteins 2017; 85:359-377. © 2016 Wiley Periodicals, Inc.
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Current docking methods still cannot rival the reliability of X-ray crystallography-derived structures.
“Current docking methods still cannot rival the reliability of X-ray crystallography-derived structures and their performance is regularly assessed by the Critical Assessment of Predicted Interactions (CAPRI) [179].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [179]
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“12,49”
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InfluentialComputational Approaches to Predict Protein–Protein Interactions in Crowded Cellular Environments
Grassmann, G., Miotto, M., Desantis, F., Rienzo, L. D., Tartaglia, G. G., Pastore, A., Ruocco, G., Monti, M. M., & Milanetti, E. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00550 · Cited by 106 · see citing works
Hashemi, Z. S., Zarei, M., Fath, M. K., Ganji, M., Farahani, M. S., Afsharnouri, F., Pourzardosht, N., Khalesi, B., Jahangiri, A., Rahbar, M. R., et al. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.669431 · Cited by 101 · see citing works
“Templatebased docking methods build a model of the complex using known structures, and it can be beneficial if the template is close to the investigated complex (Kundrotas et al., 2012; Lee et al., 2015; Lensink et al., 2017; Marcu et al., 2017; Pallara et al., 2017).”
MethodIntegrative Modelling of Biomolecular Complexes
Koukos, P. I., & Bonvin, A. M. J. J. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.11.009 · Cited by 89 · see citing works
Liu, C., Lv, N., Song, Y., Dong, L., Huang, M. Z., Shen, Q., Ren, G., Wu, R., Wang, B., Cao, Z., et al. (2022) · Food Hydrocolloids · doi:10.1016/j.foodhyd.2022.108226 · Cited by 90 · 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
“16 Docking methodologies are evaluated by community-wide blind assessment CAPRI, 17 and by benchmarking on pre-compiled protein–protein sets.”
MethodAn overview of in silico vaccine design against different pathogens and cancer
Kardani, K., Bolhassani, A., & Namvar, A. (2020) · Expert Review of Vaccines · doi:10.1080/14760584.2020.1794832 · Cited by 85 · see citing works
“Finally, the template-based docking method for building a model of the complex conducts known template structures as scaffolds [166–167–168].”
Methoddos Santos-Silva, C. A., Zupin, L., Oliveira-Lima, M., Vilela, L. M. B., Bezerra-Neto, J. P., Ferreira‐Neto, J. R. C., Ferreira, J. D. C., Silva, R. L. D. O., Pires, C. D. J., Aburjaile, F. F., et al. (2020) · Bioinformatics and Biology Insights · doi:10.1177/1177932220952739 · Cited by 84 · see citing works
In silico structure-based approaches to discover protein-protein interaction-targeting drugs
Shin, W., Christoffer, C. W., & Kihara, D. (2017) · Methods · doi:10.1016/j.ymeth.2017.08.006 · Cited by 84 · see citing works
Impact of AlphaFold on structure prediction of protein complexes: The CASP15‐CAPRI experiment
Lensink, M. F., Brysbaert, G., RAOURAOUA, N., Bates, P. A., Giulini, M., Honorato, R. V., van Noort, C. W., Teixeira, J. M. C., Bonvin, A. M. J. J., Kong, R., et al. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26609 · Cited by 79 · see citing works
“30,55 The same problem plagues deep learning-based structure prediction methods such as AF2 29 and AF2-M.”
Computational design of epitope-specific functional antibodies
Nimrod, G., Fischman, S., Austin, M., Herman, A., Keyes, F., Leiderman, O., Hargreaves, D., Strajbl, M., Breed, J., Klompus, S., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.10.081 · Cited by 77 · see citing works
Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes
Antunes, D. A., Abella, J. R., Devaurs, D., Rigo, M., & Kavraki, L. E. (2019) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026619666181224101744 · Cited by 71 · see citing works
“In addition, it has often been observed that combining several scoring functions can improve their accuracy (134).”
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
Rapid determination of quaternary protein structures in complex biological samples
Hauri, S., Khakzad, H., Happonen, L., Teleman, J., Malmström, J. A., & Malmström, L. (2019) · Nature Communications · doi:10.1038/s41467-018-07986-1 · Cited by 70 · see citing works
Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field
Tamar Schlick, A., Portillo‐Ledesma, S., Myers, C. G., Beljak, L., Chen, J. B., Dakhel, S., Darling, D., Ghosh, S., Hall, J. W., Jan, M., et al. (2021) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-091720-102019 · Cited by 68 · see citing works
“However, some tangible progress has been made using better integration of different modeling tools with docking procedures, as evident in the latest edition of the Critical Assessment of Predicted Interactions (CAPRI) (103).”
MethodCritical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes
Wodak, S. J., Vajda, S., Lensink, M. F., Kozakov, D., & Bates, P. A. (2023) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-102622-084607 · Cited by 71 · see citing works
Hierarchical Flexible Peptide Docking by Conformer Generation and Ensemble Docking of Peptides
Zhou, P., Li, B., Yan, Y., Jin, B., Wang, L., & Huang, S. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00142 · Cited by 69 · see citing works
Protein–Protein and Protein–Peptide Docking with ClusPro Server
Alekseenko, A. E., Ignatov, M., Jones, G. S., Sabitova, M., & Kozakov, D. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0708-4_9 · Cited by 68 · see citing works
Modelling peptide–protein complexes: docking, simulations and machine learning
Mondal, A., Chang, L., & Pérez, A. (2022) · QRB Discovery · doi:10.1017/qrd.2022.14 · Cited by 65 · see citing works
“Some of this success has been translated into the protein – protein docking field, as seen from the evolution of predictions in the CAPRI (Critical Assessment of PRediction of Inter-action) competition (Lensink et al. , 2017, 2020).”
BackgroundProtein Structural Bioinformatics: An Overview
Gromiha, M. M., Raju, N., & Selvaraj, S. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20278-1 · Cited by 61 · see citing works
Adaptive immune receptor repertoire analysis
Mhanna, V., Bashour, H., Quý, K. L., Barennes, P., Rawat, P., Greiff, V., & Mariotti‐Ferrandiz, E. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-023-00284-1 · Cited by 62 · see citing works
“Such approaches could be expanded to solid cancers, when AIRR-seq combined with antigen prediction identifies tumour targets that can specifically drive the engineered T cell or the CAR T cell to the tumour 440,441 .”
Computational design of antibodies
Fischman, S., & Ofran, Y. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2018.04.007 · Cited by 56 · see citing works
Tao, H., Zhang, Y., & Huang, S. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00058 · Cited by 54 · see citing works
Leveraging machine learning models for peptide–protein interaction prediction
Song, Y., Mi, X., & Shukla, D. (2024) · RSC Chemical Biology · doi:10.1039/d3cb00208j · Cited by 53 · see citing works
Molecular Modelling in Bioactive Peptide Discovery and Characterisation
Agoni, C., Fernández-Díaz, R., Timmons, P. B., Adelfio, A., Gómez, H., & Shields, D. C. (2025) · Biomolecules · doi:10.3390/biom15040524 · Cited by 59 · see citing works
“Hybrid approaches that combine mixed scoring functions with biological insights have been shown to yield the most accurate peptide–protein docking results [193,194,252].”
Assessment of ab initio models of protein complexes by molecular dynamics
Radom, F., Plückthun, A., & Paci, E. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006182 · Cited by 47 · see citing works
“Their performance is assessed periodically in the Critical Assessment of Predicted Interactions (CAPRI), where different research groups compete in blind docking of diverse complexes[10].”
BackgroundTonolo, F., Grinzato, A., Bindoli, A., & Rigobello, M. P. (2023) · Antioxidants · doi:10.3390/antiox12030665 · Cited by 50 · see citing works
Sampling Native-like Structures of RNA-Protein Complexes through Rosetta Folding and Docking
Kappel, K., & Das, R. (2018) · Structure · doi:10.1016/j.str.2018.10.001 · Cited by 50 · see citing works
Site‐Specific Studies of Nucleosome Interactions by Solid‐State NMR Spectroscopy
Xiang, S., le Paige, U. B., Horn, V., Houben, K., Baldus, M., & van Ingen, H. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201713158 · Cited by 49 · see citing works
Protein–peptide docking using CABS-dock and contact information
Błaszczyk, M., Ciemny, M. P., Koliński, A., Kurciński, M., & Kmiecik, S. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby080 · Cited by 45 · see citing works
“The contact information can be deduced from experimental data [28] (for example from NMR or mutagenesis experiments), structures of similar protein–peptide complexes (template-based modeling) or computational predictions of protein–peptide contacts that may include predictions of the binding site [29–32, 36], key inte…”
BackgroundMethod2D Zernike polynomial expansion: Finding the protein-protein binding regions
Milanetti, E., Miotto, M., Rienzo, L. D., Monti, M., Gosti, G., & Ruocco, G. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.11.051 · Cited by 42 · see citing works
“Ultimately, our method identifies the real region of interaction in about 60 % , representing a promising result that can be compared with the ability of docking methods to identify the experimental pose as the best one [33].”
BackgroundMethodLee, S., Kim, S., Lee, G. R., Kwon, S., Woo, H., Seok, C., & Park, H. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.057 · Cited by 40 · see citing works
“We took the CAPRI [30] metric to measure success, considering a prediction with ‘‘the best of top 5 ligand poses being at least at acceptable accuracy” (measured by a combined evaluation of contacts and orientations, details in Methods) as a success.”
MethodInfluentialPushing the accuracy limit of shape complementarity for protein-protein docking
Yan, Y., & Huang, S. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3270-y · Cited by 41 · see citing works
“Due to its fast global search, many FFT-based protein-protein docking algorithms have been developed in the past decade and achieved considerable successes in the community-wide CAPRI (Critical Assessment of Prediction of Interactions) experiments (http://capri.ebi.ac.uk/) [52–57].”
MethodDocking Paradigm in Drug Design
Sulimov, V. B., Kutov, D. C., Taschilova, A. S., Ilin, I. S., Tyrtyshnikov, E. E., & Sulimov, A. V. (2020) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026620666201207095626 · Cited by 43 · see citing works
Modeling Protein–Protein or Protein–DNA/RNA Complexes Using the HDOCK Webserver
Yan, Y., & Huang, S. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0708-4_12 · Cited by 40 · see citing works
On Calculating Free Energy Differences Using Ensembles of Transition Paths
Hall, R., Dixon, T. J., & Dickson, A. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00106 · Cited by 38 · see citing works
“In the field of biomolecular modeling, blind challenges— where a series of objectives are released by the organizers, and participants entries are directly judged by their agreement with experiment—have been useful catalysts for the development of predictive algorithms (Lensink et al., 2017; Synapse, 2018; Croll et al…”
MethodFrappier, V., Duran, M., & Keating, A. E. (2017) · Protein Science · doi:10.1002/pro.3320 · Cited by 37 · see citing works
“16 Many databases have compiled examples of protein–peptide complexes and extracted interesting structural trends.”
BackgroundMARTINI-Based Protein-DNA Coarse-Grained HADDOCKing
Honorato, R. V., Roel‐Touris, J., & Bonvin, A. M. J. J. (2019) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2019.00102 · Cited by 33 · see citing works
“Finally, as a proof of concept, we revisited CAPRI Target 95 (Lensink et al., 2017), a protein-nucleosome complex for which we failed to identify near native solutions in our original CAPRI submissions (although we did generate some).”
MethodIn Silico Methods in Antibody Design
Zhao, J., Nussinov, R., Wu, W., & Ma, B. (2018) · Antibodies · doi:10.3390/antib7030022 · Cited by 36 · see citing works
“Recent critical assessment of protein interactions(CAPRI) reported that six out of 20 test complexes cannot obtain acceptable models even with ~1000 candidate models per complex [123].”
BackgroundMethods 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 33 · see citing works
“The results were evaluated using criteria established by the Critical Assessment of PRedicted Interactions (CAPRI) community where models are classified into the four categories: Incorrect, Accept-able, Medium, or High quality [107].”
Protein–Protein Docking in Drug Design and Discovery
Kaczor, A. A., Bartuzi, D., Stępniewski, T. M., Matosiuk, D., & Selent, J. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7756-7_15 · Cited by 33 · see citing works
Molecular Modeling Applied to Nucleic Acid-Based Molecule Development
Krüger, A., Zimbres, F. M., Kronenberger, T., & Wrenger, C. (2018) · Biomolecules · doi:10.3390/biom8030083 · Cited by 31 · see citing works
“Additional studies were designed especially to test existing docking programs, including the community-wide Critical Assessment of Prediction of Interactions (CAPRI) initiative [73,74] and a wide variety of benchmark databases.”
BackgroundQuignot, 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 31 · see citing works
“Critical assessment of strategies for modeling protein interactions, as achieved by CAPRI (Critical Assessment of PRedicted Interactions) experiments (5,6), continuously rate template-based modeling as a reliable approach provided that a template can be unambiguously identified.”
MethodEnhanced 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 · see citing works
“Models were compared to their respective crystal structures and attributed a quality as previously described in the CAPRI blind challenges 30 .”
Li, Z., & Buck, M. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00195 · Cited by 29 · see citing works
“yielded steady improvements over the years; currently most “easy to moderately difficult” protein complex target structures can be predicted reliably to be among the top or top 5 (or 10) of calculated solutions [24], although often sequence/evolution-based as well as experimental information are used”
BackgroundMethodInterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network
Johansson-Åkhe, I., Mirabello, C., & Wallner, B. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.763102 · Cited by 28 · see citing works
“Table 1: CAPRI criteria for peptide-protein docked model quality (Lensink et al., 2017).”
BackgroundMethodInfluentialChawla, M., Kalra, U., Petta, A., Sharma, S., Shaikh, A. R., Cavallo, L., & Oliva, R. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf606 · Cited by 28 · see citing works
“…participations as scorers in the CAPRI (Critical Assessment of PRedicted Interactions) experiment (Vangone et al. 2013, Lensink et al. 2016, Lensink et al. 2017, Lensink et al. 2018, Lensink et al. 2019, Barradas-Bautista et al. 2020, Lensink et al. 2023), and combine it with our expertise…”
Venom-derived peptides inhibiting Kir channels: Past, present, and future
Doupnik, C. A. (2017) · Neuropharmacology · doi:10.1016/j.neuropharm.2017.07.011 · Cited by 28 · see citing works
“Additional refinements enable docking programs to achieve a high degree of predictive success in reproducing known protein-protein and protein- M AN US CR IP T AC CE PT ED 15 peptide complexes, and have become useful tools for predicting unknown complexes (Lensink et al., 2017).”
BackgroundClusPro LigTBM: Automated Template-based Small Molecule Docking
Alekseenko, A. E., Kotelnikov, S., Ignatov, M., Egbert, M. E., Kholodov, Y. A., Vajda, S., & Kozakov, D. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.12.011 · Cited by 27 · see citing works
“Recently, the template-based approach has been among the top-performing methods both in protein-protein and protein-ligand community-wide blind prediction challenges, such as CAPRI [2,3,34,35] and D3R Grand Challenges.”
MethodMarcu, 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
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 · see citing works
AL-Refaei, M. A., Makki, R. M., & Ali, H. M. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e03221 · Cited by 26 · see citing works
“This complex was selected to be the best docked complex for subsequent analysis (Figure 2) (Supplementary Video I) and was successfully refined using NAMD energy minimization (Karaca et al., 2010; Lensink et al., 2017; Phillips et al., 2005; Schneidman-Duhovny et al., 2005).”
MethodDisentangling the molecular determinants for Cenp‐F localization to nuclear pores and kinetochores
Berto, A., Yu, J., Morchoisne‐Bolhy, S., Bertipaglia, C., Vallee, R. B., Dumont, J., Ochsenbein, F., Guérois, R., & Doye, V. (2018) · EMBO Reports · doi:10.15252/embr.201744742 · Cited by 26 · see citing works
“We used the InterEvDock server [41], which is based on a pipeline that previously yielded the best predictions in the 6 community-wide CAPRI (Critical Assessment of PRediction of Interactions) evaluation meeting [42,43].”
Reproducibility in Research: Systems, Infrastructure, Culture
Crick, T., Hall, B. A., & Ishtiaq, S. S. (2017) · Journal of Open Research Software · doi:10.5334/jors.73 · Cited by 25 · see citing works
Corrada, D., Denison, M. S., & Bonati, L. (2017) · Molecular BioSystems · doi:10.1039/c7mb00005g · Cited by 24 · see citing works
PepVis: An integrated peptide virtual screening pipeline for ensemble and flexible docking protocols
Ansar, S., & Vetrivel, U. (2019) · Chemical Biology & Drug Design · doi:10.1111/cbdd.13607 · Cited by 24 · see citing works
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 24 · see citing works
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 22 · see citing works
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 · see citing works
NUScon: a community-driven platform for quantitative evaluation of nonuniform sampling in NMR
Pustovalova, Y. E., Delaglio, F., Craft, D. L., Arthanari, H., Bax, A., Billeter, M., Bostock, M. J., Dashti, H., Hansen, D. F., Hyberts, S. G., et al. (2021) · Magnetic Resonance · doi:10.5194/mr-2-843-2021 · Cited by 22 · see citing works
“Similarly, the challenges of predicting how molecules bind has been addressed by The Critical Assessment of PRedicted Inter95 actions (CAPRI, Janin et al., 2003; Lensink et al., 2017), resulting in numerous and powerful web-based docking platforms.”
BackgroundBerselli, A., Benfenati, F., Maragliano, L., & Alberini, G. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.10.038 · Cited by 22 · see citing works
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 · see citing works
“These scores are commonly used to rank interfaces, notably in blind prediction challenges [30,36].”
BackgroundAlford, R. F., Smolin, N., Young, H. S., Gray, J. J., & Robia, S. L. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.ra120.012948 · Cited by 22 · see citing works
“Methods for modeling protein–protein interactions are well-established for soluble proteins (34); however, progress formembrane proteins has been delayed because of the paucity of known structures (35) and difficulty accounting for the lipid bilayer.”
BackgroundModeling oblong proteins and water‐mediated interfaces with RosettaDock in CAPRI rounds 28–35
Marze, N. A., Jeliazkov, J. R., Burman, S. S. R., Boyken, S. E., DiMaio, F., & Gray, J. J. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25168 · Cited by 21 · see citing works
Kinetic Selection and Relaxation of the Intrinsically Disordered Region of a Protein upon Binding
Tran, D. P., & Kitao, A. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b01203 · Cited by 21 · see citing works
Modeling of Protein Tertiary and Quaternary Structures Based on Evolutionary Information
Studer, G., Tauriello, G., Bienert, S., Waterhouse, A. M., Bertoni, M., Bordoli, L., Schwede, T. F., & Lepore, R. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8736-8_17 · Cited by 20 · see citing works
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 · see citing works
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 · see citing works
“Irrespective of the specific challenge, one clear message always feeds through in the assessment of results, and deterministic modeling of flexibility in the docking process remains an unsolved problem.(33) One of the most exacerbating aspects, particularly for those developing docking servers, is our inability to est…”
BackgroundDocking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement
Badaczewska-Dawid, A. E., Kmiecik, S., & Koliński, M. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa109 · Cited by 19 · see citing works
“Application of the proposed protocol allowed for successful prediction of 6 out of 7 GPCR-peptide complexes providing structures with acceptable or medium accuracy (according to the criteria of CAPRI competition [46]).”
BackgroundMethodInfluentialElectrostatic features for nucleocapsid proteins of SARS-CoV and SARS-CoV-2
Guo, W., Xie, Y., Lopez-Hernandez, A. E., Sun, S., & Li, L. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021120 · Cited by 19 · see citing works
“Due to the relatively high cost of experiments and the rapid development of computational algorithms [12,13], computational methods are now widely used to study biology phenomena, including biomolecular structures [14,15], biomolecular interactions [16-18], pH dependence of protein-protein/DNA/RNA interactions [19,20]…”
BackgroundFrappier, V., Duran, M., & Keating, A. E. (2018) · Protein Science · doi:10.1002/pro.3431 · Cited by 19 · see citing works
Chantreau, M., Poux, C., Lensink, M. F., Brysbaert, G., Vekemans, X., & Castric, V. (2019) · eLife · doi:10.7554/elife.50253 · Cited by 19 · see citing works
Delaunay, M., & Ha‐Duong, T. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1855-4_11 · Cited by 17 · see citing works
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 · see citing works
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 · see citing works
“The Critical Assessment of PRedicted Interactions (CAPRI) classifies predicted protein complex conformations in four categories, namely incorrect, acceptable, medium and high quality, based on the extent to which they differ from the corresponding experimental structures (Lensink et al. , 2017).”
Shinobu, A., Takemura, K., Matubayasi, N., & Kitao, A. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5055799 · Cited by 17 · see citing works
Targeting SARS-CoV-2 Receptor Binding Domain with Stapled Peptides: An In Silico Study
de Campos, L. J., Palermo, N. Y., & Conda‐Sheridan, M. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c02398 · Cited by 16 · see citing works
Benchmarking predictions of allostery in liver pyruvate kinase in CAGI4
Xu, Q., Tang, Q., Katsonis, P., Lichtarge, O., Jones, D. T., Bovo, S., Babbi, G., Martelli, P. L., Casadio, R., Lee, G. R., et al. (2017) · Human Mutation · doi:10.1002/humu.23222 · Cited by 17 · see citing works
“, 2016) and CAPRI (Lensink et al., 2017), have provided independent assessment of computational prediction methods in structural biology.”
BackgroundMethod
- Authors
- Lensink, M. F., Velankar, S., & Wodak, S. J.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- European Bioinformatics Institute (GB)
- Vrije Universiteit Brussel (BE)
- Corresponding author
- Lensink, Marc F.
- Venue
- Proteins Structure Function and Bioinformatics
- Publisher
- Wiley
- Published
- 2016-12-02
- Volume
- 85
- Issue
- 3
- Pages
- 359-377
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- OpenAlex
- W2554109484
- Cited by
- 222
- References
- 43
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 10 · 2023: 17 · 2022: 21 · 2021: 24 · 2020: 38 · 2019: 41 · 2018: 34 · 2017: 17 · 2016: 2
- FWCI
- 16.29
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Science Foundation (0953839, 1458509, 2011-16, FP7/2007-2013, 1262189, 2009418, 1127027, CAREER, 1262621, 1319551, 1527292, 1546278)
- Cancer Research UK (FP7/2007-2013)
- Research Councils UK
- European Commission (604102, 2007–2013, 327899, FP7 2007-2013, 659025, H2020, FP7/2007)
- National Research Foundation
- Agence Nationale de la Recherche (ANR-11-MONU-0006)
- Bundesministerium für Bildung und Forschung (0315749)
- Ministerio de Economía y Competitividad (BIO2013‐48213‐R)
- National Research Foundation of Korea (2013R1A2A1A09012229, NRF-2013R1A2A1A09012229, 2008‐0061987)
- Regione Campania (LR5-AF2008)
- Klaus Tschira Stiftung
- National Institutes of Health (FP7/2007-2013, H2020, GM-078221, GM061867, GM109824, GM093147)
- Directorate for Biological Sciences
- Horizon 2020 Framework Programme (FP7/2007-2013, 604102)
- National Institute of General Medical Sciences (R01GM083960, R01GM097528, P41GM109824, R01GM074255, R01GM058187, R01GM078221, R01GM109980)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein-protein complex, protein-peptide complex, Capri, side-chain conformation, conformational flexibility, nucleosome, protein docking, model scoring, evolutionary information
- MeSH terms
- Algorithms, Binding Sites, Humans, Nucleosomes, Peptides, Protein Binding, Protein Conformation, Proteins, Research Design, Software, Water, Computational Biology, Benchmarking, Protein Interaction Mapping, Multiprotein Complexes, 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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