Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro
Open accessAbstract
It has been demonstrated earlier that the neural network based program AlphaFold2 can be used to dock proteins given the two sequences separated by a gap as the input. The protocol presented here combines AlphaFold2 with the physics based docking program ClusPro. The monomers of the model generated by AlphaFold2 are separated, re-docked using ClusPro, and the resulting 10 models are refined by AlphaFold2. Finally, the five original AlphaFold2 models are added to the 10 AlphaFold2 refined ClusPro models, and the 15 models are ranked by their predicted aligned error (PAE) values obtained by AlphaFold2. The protocol is applied to two benchmark sets of complexes, the first based on the established protein-protein docking benchmark, and the second consisting of only structures released after May 2018, the cut-off date for training AlphaFold2. It is shown that the quality of the initial AlphaFold2 models improves with each additional step of the protocol. In particular, adding the AlphaFold2 refined ClusPro models to the AlphaFold2 models increases the success rate by 23% in the top 5 predictions, whereas considering the 10 models obtained by the combined protocol increases the success rate to close to 40%. The improvement is similar for the second benchmark that includes only complexes distinct from the proteins used for training the neural network.
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Studies using AF2-generated monomer models with docking methods are based on the idea that AF2 generates high-quality monomeric models that could improve the chance of native-like poses in docking.
“studies have been carried out whereby models of single proteins generated with AF2 are used with docking methods. They are based on the idea that AF2 generates high-quality monomeric models that could improve the chance of native-like poses in docking22.”
From AF2Complex predicts direct physical interactions in multimeric proteins with deep learning (2022) · cites this paper as [22]
In docking studies using AF2 monomers, the benchmark set includes protein structures used to train AF2 deep learning models, and use of holo monomers compromises rigor because AF2 likely provides an observed holo-structure for docking.
“One issue with these studies, as some authors pointed out, is that the benchmark set tested includes protein structures used to train the AF2 deep learning models. Although the AF2 models were not trained on protein complex structures, the use of the holo monomers in training compromises rigor because AF2 likely provides an “observed” holo-structure for docking.”
From AF2Complex predicts direct physical interactions in multimeric proteins with deep learning (2022) · cites this paper as [22]
Another complex, docking-centric strategy increases the mean DockQ score from 0.25 to 0.47 on CP17.
“Another complex, docking-centric strategy increases the mean of DockQ score from 0.25 to 0.4722.”
From AF2Complex predicts direct physical interactions in multimeric proteins with deep learning (2022) · cites this paper as [22]
The docking-centric strategy of building a complex using ColabFold AF2, splitting monomers, and using ClusPro yields an acceptable or better top-ranked model for fewer than half of the CP17 targets.
“This strategy yields an acceptable or better top-ranked model for fewer than half of the targets.”
From AF2Complex predicts direct physical interactions in multimeric proteins with deep learning (2022) · cites this paper as [22]
The docking-centric approach has a mean DockQ score of 0.25 on CP17.
“a dramatic improvement from 0.25 of the docking-centric approach.”
From AF2Complex predicts direct physical interactions in multimeric proteins with deep learning (2022) · cites this paper as [22]
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
ColabFold - Making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391 · see citing works
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,542 · see citing works
The ClusPro web server for protein–protein docking
Kozakov, D., Hall, D. R., Xia, B., Porter, K. A., Padhorny, D., Yueh, C., Beglov, D., & Vajda, S. (2017) · Nature Protocols · doi:10.1038/nprot.2016.169 · Cited by 3,297 · see citing works
Self-citationHarnessing protein folding neural networks for peptide–protein docking
Tsaban, T., Varga, J. K., Avraham, O., Ben-Aharon, Z., Khramushin, A., & Schueler-Furman, O. (2022) · Nature Communications · doi:10.1038/s41467-021-27838-9 · Cited by 1,205 · see citing works
Harnessing protein folding neural networks for peptide-protein docking
(2021) · doi:10.1101/2021.08.01.454656 · Cited by 1,203 · see citing works
PIPER: An FFT‐based protein docking program with pairwise potentials
Kozakov, D., Brenke, R., Comeau, S. R., & Vajda, S. (2006) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21117 · Cited by 865 · see citing works
Self-citationPerformance and Its Limits in Rigid Body Protein-Protein Docking
Desta, I., Porter, K. A., Xia, B., Kozakov, D., & Vajda, S. (2020) · Structure · doi:10.1016/j.str.2020.06.006 · Cited by 697 · see citing works
Self-citationMMseqs2 desktop and local web server app for fast, interactive sequence searches
Mirdita, M., Steinegger, M., & Söding, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty1057 · Cited by 618 · see citing works
DockQ: A Quality Measure for Protein-Protein Docking Models
Basu, S., & Wallner, B. (2016) · PLOS ONE · doi:10.1371/journal.pone.0161879 · Cited by 460 · see citing works
ColabFold - Making protein folding accessible to allPreprint
Ovchinnikov, S., Steinegger, M., Mirdita, M., konsti, Moriwaki, Y., Kim, R. S., Wang, P., Kosinski, J., Tadej Satler, P., Cha, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.5281/zenodo.5123296 · Cited by 425 · see citing works
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., et al. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.07.016 · Cited by 424 · see citing works
High‐accuracy protein structure prediction in <scp>CASP14</scp>
Pereira, J., Simpkin, A. J., Hartmann, M. D., Rigden, D. J., Keegan, R. M., & Lupas, A. N. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26171 · Cited by 314 · see citing works
High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock
Alam, N., Goldstein, O., Xia, B., Porter, K. A., Kozakov, D., & Schueler‐Furman, O. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005905 · Cited by 170 · see citing works
Self-citationCan AlphaFold2 predict protein-peptide complex structures accurately?
Ko, J., & Lee, J. (2021) · doi:10.1101/2021.07.27.453972 · Cited by 83 · see citing works
The breakthrough in protein structure prediction
Lupas, A. N., Pereira, J., Alva, V., Merino, F., Coles, M. P., & Hartmann, M. D. (2021) · Biochemical Journal · doi:10.1042/bcj20200963 · Cited by 64 · see citing works
Assessment of the <scp>CASP14</scp> assembly predictions
Ozden, B., Kryshtafovych, A., & Karaca, E. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26199 · Cited by 41 · see citing works
ClusPro in rounds 38 to 45 of CAPRI: Toward combining template‐based methods with free docking
Padhorny, D., Porter, K. A., Ignatov, M., Alekseenko, A. E., Beglov, D., Kotelnikov, S., Ashizawa, R., Desta, I., Alam, N., Sun, Z., et al. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25887 · Cited by 13 · see citing works
Self-citationAssessing the binding properties of <scp>CASP14</scp> targets and models
Egbert, M., Ghani, U., Ashizawa, R., Kotelnikov, S., Nguyen, T., Desta, I., Hashemi, N., Padhorny, D., Kozakov, D., & Vajda, S. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26209 · Cited by 10 · see citing works
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Protein complex prediction with AlphaFold-Multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808 · see citing works
Harnessing protein folding neural networks for peptide–protein docking
Tsaban, T., Varga, J. K., Avraham, O., Ben-Aharon, Z., Khramushin, A., & Schueler-Furman, O. (2022) · Nature Communications · doi:10.1038/s41467-021-27838-9 · Cited by 1,205 · see citing works
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Honorato, R. V., Trellet, M. E., Jiménez‐García, B., Schaarschmidt, J., Giulini, M., Reys, V., Koukos, P. I., Rodrigues, J., Karaca, E., van Zundert, G. C. P., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-01011-0 · Cited by 653 · see citing works
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Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2022) · Protein Science · doi:10.1002/pro.4379 · Cited by 335 · see citing works
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Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2021) · doi:10.1101/2021.10.23.465575 · Cited by 335 · see citing works
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Roney, J. P., & Ovchinnikov, S. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.238101 · Cited by 281 · see citing works
Tubiana, J., Schneidman-Duhovny, D., & Wolfson, H. J. (2022) · Nature Methods · doi:10.1038/s41592-022-01490-7 · Cited by 274 · see citing works
AF2Complex predicts direct physical interactions in multimeric proteins with deep learning
Gao, M., Nakajima An, D., Parks, J. M., & Skolnick, J. (2022) · Nature Communications · doi:10.1038/s41467-022-29394-2 · Cited by 252 · see citing works
AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges
Schauperl, M., & Denny, R. A. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00026 · Cited by 122 · see citing works
From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2
Bret, H., Gao, J., Zea, D. J., Andreani, J., & Guerois, R. (2024) · Nature Communications · doi:10.1038/s41467-023-44288-7 · Cited by 103 · 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
Self-citationIndependent SE(3)-Equivariant Models for End-to-End Rigid Protein DockingPreprint
Ganea, O.-E., Huang, X., Bunne, C., Bian, Y., Barzilay, R., Jaakkola, T., & Krause, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.07786 · Cited by 75 · see citing works
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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
Self-citationExploring protein-protein interactions at the proteome level
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Chu, L., Ruffolo, J. A., Harmalkar, A., & Gray, J. J. (2024) · Protein Science · doi:10.1002/pro.4862 · Cited by 41 · see citing works
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Lupo, U., Sgarbossa, D., & Bitbol, A.-F. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311887121 · Cited by 31 · see citing works
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Durairaj, J., de Ridder, D., & van Dijk, A. D. J. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.12.039 · Cited by 31 · see citing works
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Gaudreault, F., Corbeil, C. R., & Sulea, T. I. (2023) · Scientific Reports · doi:10.1038/s41598-023-42090-5 · Cited by 32 · see citing works
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Hashemi, N., Hao, B., Ignatov, M., Paschalidis, I. C., Vakili, P., Vajda, S., & Kozakov, D. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1207380 · Cited by 25 · see citing works
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Lehrer, S. P., & Rheinstein, P. H. (2022) · Journal of Proteins and Proteomics · doi:10.1007/s42485-022-00088-z · Cited by 12 · see citing works
Lin, P., Li, H., & Huang, S. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102789 · Cited by 13 · see citing works
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Martin, J. (2022) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583522000051 · Cited by 10 · see citing works
Li, M., Qing, R., Tao, F., Xu, P., & Zhang, S. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.12.008 · Cited by 9 · see citing works
Li, H., Nithin, C., Kmiecik, S., & Huang, S. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104382 · Cited by 9 · see citing works
AlphaCutter: Efficient removal of non‐globular regions from predicted protein structures
Tam, C., & Iwasaki, W. (2023) · PROTEOMICS · doi:10.1002/pmic.202300176 · Cited by 8 · see citing works
Wang, Y., Wang, Z., Sadeh, G., Zancato, L., Achille, A., Karypis, G., & Rangwala, H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.29.620988 · Cited by 9 · see citing works
Gismene, C., González, J. E. H., Santisteban, A. R. N., Nascimento, A. F. Z., Cunha, L. D. S., de Moraes, F. R., Oliveira, C. L. P., Oliveira, C. C., Provazzi, P. J. S., Pascutti, P. G., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23179857 · Cited by 8 · see citing works
Li, M., Qing, R., Tao, F., Xu, P., & Zhang, S. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242216266 · Cited by 7 · see citing works
Apprehensions and emerging solutions in ML-based protein structure prediction
Dahlström, K. M., & Salminen, T. A. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102819 · Cited by 8 · see citing works
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Bekar-Cesaretli, A. A., Khan, O., Nguyen, T., Kozakov, D., Joseph‐McCarthy, D., & Vajda, S. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01761 · Cited by 5 · see citing works
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Kotelnikov, S., Ashizawa, R., Popov, K. I., Khan, O., Ignatov, M., Li, S. X., Hassan, M., Coutsias, E. A., Poda, G. I., Padhorny, D., et al. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26587 · Cited by 6 · see citing works
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Dapkūnas, J., & Venclovas, Č. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103079 · Cited by 4 · see citing works
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Xu, Z., Davila, A., Wiamowski, J., Teraguchi, S., & Standley, D. M. (2022) · doi:10.1101/2022.05.21.492907 · Cited by 4 · see citing works
Refinement of AlphaFold-Multimer structures with single sequence inputPreprint
Oda, T. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.27.521991 · Cited by 4 · see citing works
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Martin, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.25.554771 · Cited by 4 · see citing works
Yamamori, Y., & Tomii, K. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23041977 · Cited by 3 · see citing works
Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models
Cohen, T., Halfon, M., Carter, L. G., Sharkey, B., Jain, T. S., Sivasubramanian, A., & Schneidman‐Duhovny, D. (2022) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2022.11.003 · Cited by 3 · see citing works
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Agarwal, S., Chandpa, H. H., Naskar, S., Meena, C. L., Panda, A. K., & Meena, J. (2024) · Vaccine · doi:10.1016/j.vaccine.2024.04.082 · Cited by 2 · see citing works
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Gao, M., An, D. N., Parks, J. M., & Skolnick, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.09.467949 · Cited by 2 · see citing works
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DeRoo, J., Terry, J. S., Zhao, N., Stasevich, T. J., Snow, C. D., & Geiss, B. J. (2024) · eLife · doi:10.7554/elife.98369.1 · Cited by 2 · see citing works
Liao, K.-J., & Sun, Y.-J. (2025) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26844 · Cited by 1 · see citing works
Adil, B., Shahar, S., Amran, S. I., Omar, M. S. S., & Naser, M. (2023) · International Journal of Pharmaceutical Quality Assurance · doi:10.25258/ijpqa.14.1.34
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Li, H., Li, Y., & Huang, S. (2025) · Wiley eBooks · doi:10.1002/9783527845828.ch7
Chong, N. H. H., Tan, E. Z. T., Tan, C. H. E., Ilangovan, H., Lim, Y. J., Srivastava, A., & Tan, Y. S. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c01990
Behbahani, Y. M. (2022) · theses.fr (ABES) · doi:10.70675/bfdded7czb638z455azaedaz4ae9f8d0a13d
Structural prediction of protein assemblies with deep learning
Bret, H. (2023) · theses.fr (ABES) · doi:10.70675/b3849091zce01z4ab0z91efz2b71d9cc754d
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Abualrous, E. T., Miller, W., & Cannon, D. A. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5041-7_4
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DeRoo, J., Terry, J. S., Zhao, N., Stasevich, T. J., Snow, C. D., & Geiss, B. J. (2026) · eLife · doi:10.7554/elife.98369.3
- Authors
- Ghani, U., Desta, I., Jindal, A., Khan, O., Jones, G., Hashemi, N., Kotelnikov, S., Padhorny, D., Vajda, S., & Kozakov, D.
- Affiliations
- Boston University (US)
- Stony Brook University (US)
- Corresponding author
- Padhorny, Dzmitry
- Kozakov, Dima
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2021-09-07
- OpenAlex
- W3197480237
- Cited by
- 68
- References
- 19
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 13 · 2023: 16 · 2022: 23 · 2021: 3
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Funders
- National Science Foundation (R35 GM118078, 1645512, RM1135136, 1759277)
- National Institute of General Medical Sciences (R21GM127952, RM1135136, R35GM118078)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein-protein docking, AlphaFold2, ClusPro, protein structure prediction
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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