Improved Multimer Prediction using Massive Sampling with AlphaFold in CASP15
Open accessPreprint — published as 10.1002/prot.26562 · Paper page
Abstract
AlphaFold has transformed structure prediction by enabling highly accurate predictions on par with experimentally determined structures. Still, for difficult cases, in particular, multimers, there is still room for improvement. Important for the success of AlphaFold is its ability to assess its own predictions. The basic idea for the Wallner group in CASP15 was to exploit the excellent ranking score in AlphaFold by massive sampling. To this end, we ran AlphaFold using six different settings, with and without templates, and with an increased number of recycles using both multimer v1 and v2 weights. In all cases, the dropout layers were enabled at inference to sample the uncertainty and increase the diversity of the generated models. A median of 4,810 models per target was generated and almost all (35/38) received a ranking_confidence > 0.7. Compared to other groups in CASP15, Wallner obtained the highest sum of Z-scores based on the DockQ score, 40.8 compared to 26.3 for the second highest, much higher than -0.2 achieved by the AlphaFold baseline method, NBIS-AF2-multimer. The improvement over the baseline is substantial with the mean DockQ increasing from 0.43 to 0.56, with several targets showing a DockQ score increase by +0.6 units. Remarkable, considering Wallner and NBIS-AF2-multimer were using identical input data. The reason for the success can be attributed to the diversified sampling using dropout with different settings and, in particular, the use of multimer v1, which seems to be much more susceptible to sampling compared to v2. The method is available here: http://wallnerlab.org/AFsample/.
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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
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471 · see citing works
Scoring function for automated assessment of protein structure template quality
Zhang, Y., & Skolnick, J. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20264 · Cited by 2,392 · see citing works
UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches
Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B., Wu, C. H., & the UniProt Consortium (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu739 · Cited by 1,928 · see citing works
Mariani, V., Biasini, M., Barbato, A., & Schwede, T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt473 · Cited by 1,138 · see citing works
Improved prediction of protein-protein interactions using AlphaFold2
Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,025 · see citing works
Clustering huge protein sequence sets in linear time
Steinegger, M., & Söding, J. (2018) · Nature Communications · doi:10.1038/s41467-018-04964-5 · Cited by 875 · see citing works
Bertoni, M., Kiefer, F., Biasini, M., Bordoli, L., & Schwede, T. (2017) · Scientific Reports · doi:10.1038/s41598-017-09654-8 · Cited by 791 · see citing works
Uniclust databases of clustered and deeply annotated protein sequences and alignments
Mirdita, M., von den Driesch, L., Galiez, C., Martin, M. J., Söding, J., & Steinegger, M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1081 · Cited by 710 · see citing works
Sampling alternative conformational states of transporters and receptors with AlphaFold2
del Alamo, D., Sala, D., Mchaourab, H. S., & Meiler, J. (2022) · eLife · doi:10.7554/elife.75751 · Cited by 511 · see citing works
US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes
Zhang, C., Shine, M., Pyle, A. M., & Zhang, Y. (2022) · Nature Methods · doi:10.1038/s41592-022-01585-1 · Cited by 511 · 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
Self-citationColabFold - 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
AlphaFold2 and the future of structural biology
Cramer, P. (2021) · Nature Structural & Molecular Biology · doi:10.1038/s41594-021-00650-1 · Cited by 325 · see citing works
State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold
Roney, J. P., & Ovchinnikov, S. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.238101 · Cited by 281 · see citing works
Mukherjee, S., & Zhang, Y. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp318 · Cited by 178 · see citing works
Improving peptide-protein docking with AlphaFold-Multimer using forced sampling
Johansson-Åkhe, I., & Wallner, B. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.959160 · Cited by 162 · see citing works
Self-citationDeep, A., Gu, Y., Gao, Y.-Q., Ego, K. M., Herzik, M. A., Zhou, H., & Corbett, K. D. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.09.008 · Cited by 106 · see citing works
Prediction of multiple conformational states by combining sequence clustering with AlphaFold2Preprint
Wayment-Steele, H. K., Ovchinnikov, S., Colwell, L. J., & Kern, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.17.512570 · Cited by 66 · see citing works
Clustering huge protein sequence sets in linear time
Steinegger, M., & Söding, J. (2018) · RePEc: Research Papers in Economics · Cited by 54 · see citing works
Haas, J., Gumienny, R., Barbato, A., Ackermann, F., Tauriello, G., Bertoni, M., Studer, G., Smolinski, A., & Schwede, T. F. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25815 · Cited by 45 · see citing works
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1 of the 23 references couldn’t be matched to a known paper.
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AFsample: improving multimer prediction with AlphaFold using massive sampling
Wallner, B. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad573 · Cited by 164 · see citing works
Self-citationYin, R., & Pierce, B. G. (2024) · Protein Science · doi:10.1002/pro.4865 · Cited by 145 · see citing works
Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER
Zheng, W. H., Wuyun, Q., Li, Y., Liu, Q., Zhou, X., Peng, C., Zhu, Y., Freddolino, L., & Zhang, Y. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02654-4 · Cited by 115 · see citing works
AFsample2 predicts multiple conformations and ensembles with AlphaFold2
Kalakoti, Y., & Wallner, B. (2025) · Communications Biology · doi:10.1038/s42003-025-07791-9 · Cited by 96 · see citing works
Self-citationAlphaFold two years on: Validation and impact
Kovalevskiy, O. V., Mateos-Garcia, J. C., & Tunyasuvunakool, K. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2315002121 · Cited by 81 · see citing works
MassiveFold: unveiling AlphaFold’s hidden potential with optimized and parallelized massive sampling
Raouraoua, N., Mirabello, C., Véry, T., Blanchet, C., Wallner, B., Lensink, M. F., & Brysbaert, G. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00714-4 · Cited by 51 · see citing works
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Self-citationAccurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking
Jeppesen, M., & André, I. (2023) · Nature Communications · doi:10.1038/s41467-023-43681-6 · Cited by 44 · see citing works
Prediction of protein assemblies by structure sampling followed by interface‐focused scoring
Olechnovič, K., Valančauskas, L., Dapkūnas, J., & Venclovas, Č. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26569 · Cited by 40 · see citing works
Nanobody engineering: computational modelling and design for biomedical and therapeutic applications
Salamouni, N. S. E., Cater, J. H., Spenkelink, L. M., & Yu, H. B. (2024) · FEBS Open Bio · doi:10.1002/2211-5463.13850 · Cited by 36 · see citing works
Feng, S., Chen, Z., Zhang, C., Xie, Y., Ovchinnikov, S., Gao, Y. Q., & Liu, S. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00873-z · Cited by 41 · see citing works
Pairing interacting protein sequences using masked language modeling
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
AFsample2: Predicting multiple conformations and ensembles with AlphaFold2Preprint
Kalakoti, Y., & Wallner, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.28.596195 · Cited by 26 · see citing works
Self-citationAlphaFold and what is next: bridging functional, systems and structural biology
Szczepski, K., & Jaremko, Ł. (2025) · Expert Review of Proteomics · doi:10.1080/14789450.2025.2456046 · Cited by 23 · see citing works
Assessment of Protein Complex Predictions in CASP16 : Are We Making Progress?
Zhang, J., Yuan, R., Kryshtafovych, A., Pei, J., Kretsch, R. C., Schaeffer, R. D., Zhou, J., Das, R., Grishin, N. V., & Cong, Q. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70068 · Cited by 24 · see citing works
Automated benchmarking of combined protein structure and ligand conformation prediction
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de Azevedo, W. F. (2024) · Current Medicinal Chemistry · doi:10.2174/0109298673313727240819070317 · Cited by 22 · see citing works
Multimeric protein interaction and complex prediction: Structure, dynamics and function
Lu, D., Yu, S., Huang, Y., & Gong, X. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.05.009 · Cited by 20 · see citing works
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Hou, M., Xia, Y., Wang, P., Lv, Z., Hou, D., Zhou, X., Zeng, J., & Zhang, G. (2025) · Nature Communications · doi:10.1038/s41467-025-65090-7 · Cited by 13 · see citing works
McGuffin, L. J., Alhaddad, S. N., Behzadi, B., Edmunds, N. S., Genc, A. G., & Adiyaman, R. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf336 · Cited by 11 · see citing works
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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
SeaMoon: From protein language models to continuous structural heterogeneity
Lombard, V., Timsit, D., Grudinin, S., & Laine, E. (2025) · Structure · doi:10.1016/j.str.2025.06.010 · Cited by 8 · see citing works
Insights into mechanisms of MALT1 allostery from NMR and AlphaFold dynamic analyses
Wallerstein, J., Han, X. Y., Levkovets, M., Lesovoy, D. M., Malmodin, D., Mirabello, C., Wallner, B., Sun, R., Sandalova, T., Agback, P., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06558-y · Cited by 8 · see citing works
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Swapna, G. V. T., Dube, N., Roth, M. J., & Montelione, G. T. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013590 · Cited by 5 · see citing works
Integrative modeling meets deep learning: Recent advances in modeling protein assemblies
Shor, B., & Schneidman‐Duhovny, D. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102841 · Cited by 6 · see citing works
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Duan, R., Xu, X., Qiu, L., Zhang, S., & Zou, X. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26805 · Cited by 4 · see citing works
Savaş, B., Yılmazbilek, İ., Özsan, A., & Karaca, E. (2026) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26820 · Cited by 4 · see citing works
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Christoffer, C. W., Kagaya, Y., Verburgt, J. C., Terashi, G., Shin, W., Jain, A., Sarkar, D., Aderinwale, T. W., Subramaniya, S. R. M. V., Wang, X., et al. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26818 · Cited by 3 · see citing works
Swapna, G. V. T., Dube, N., Roth, M. J., & Montelione, G. T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.15.603529 · Cited by 3 · see citing works
Benavides, T. L., & Montelione, G. T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.19.613999 · Cited by 2 · see citing works
Hu, C., Li, X., Zhang, M., Jing, C., Hai, M., Shen, J., Xu, Q., Dang, X., Shi, Y., Liu, E., et al. (2024) · Plants · doi:10.3390/plants13101324 · Cited by 2 · see citing works
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Wu, R., Zhou, J., Han, W., Junrui, W. U., & Mu, Z. (2026) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2026.105611 · Cited by 1 · see citing works
Dang, X., Shen, J., Li, J., Jing, C., Hu, C., Liu, E., Li, Z., & Jiang, J. (2026) · Plant Cell & Environment · doi:10.1111/pce.70543 · Cited by 1 · see citing works
Abdelfattah, N., Broncales, B., Albert, I., Kohan, J., & Yu, L. (2026) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2026.102612 · Cited by 1 · see citing works
Hoshi, K. (2025) · Journal of Structural Biology · doi:10.1016/j.jsb.2025.108252 · Cited by 1 · see citing works
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Li, H., Lin, P., Li, Y., & Huang, S. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26823
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Rodríguez‐Lumbreras, L. A., Monteagudo, V., & Fernández‐Recio, J. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4828-5_15
Lesovoy, D. M., Agback, T., Roshchin, K., Sandalova, T., Achour, A., Han, X. Y., Lomzov, A. A., Orekhov, V., & Agback, P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.17.694852
Li, Y.-L., Zhao, L., Zhao, X., Liu, Z., Jing, Q., Li, J., Ren, F.-F., Hong, D., & Rong, S. (2026) · Plants · doi:10.3390/plants15132063
- Authors
- Wallner, B.
- Affiliations
- Linköping University (SE)
- Corresponding author
- Wallner, Björn
- ORCID
- Wallner, Björn
- Venue
- Proteins Structure Function and Bioinformatics
- Publisher
- Wiley
- Published
- 2023-04-17
- Volume
- 91
- Issue
- 12
- Pages
- 1734-1746
- ISSN
- 0887-3585 (ISSN-L)
- OpenAlex
- W4385620526
- Cited by
- 53
- References
- 23
- Citations by year
- 2026: 5 · 2025: 24 · 2024: 17 · 2023: 7
- FWCI
- 4.72
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DiVA (Linkoping University) (submittedVersion, other-oa)
- Funders
- Swedish e-Science Research Centre
- National Science Council
- Carl Tryggers Stiftelse för Vetenskaplig Forskning (20:453)
- Linköpings Universitet
- Knut och Alice Wallenbergs Stiftelse
- Vetenskapsrådet (2020-03352)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- AlphaFold2, dropout layers, uncertainty sampling, CASP15, protein complex structure prediction
- MeSH terms
- Furylfuramide, Uncertainty
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