State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold
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Abstract
The problem of predicting a protein's 3D structure from its primary amino acid sequence is a longstanding challenge in structural biology. Recently, approaches like alphafold have achieved remarkable performance on this task by combining deep learning techniques with coevolutionary data from multiple sequence alignments of related protein sequences. The use of coevolutionary information is critical to these models' accuracy, and without it their predictive performance drops considerably. In living cells, however, the 3D structure of a protein is fully determined by its primary sequence and the biophysical laws that cause it to fold into a low-energy configuration. Thus, it should be possible to predict a protein's structure from only its primary sequence by learning an approximate biophysical energy function. We provide evidence that alphafold has learned such an energy function, and uses coevolution data to solve the global search problem of finding a low-energy conformation. We demonstrate that alphafold'slearned energy function can be used to rank the quality of candidate protein structures with state-of-the-art accuracy, without using any coevolution data. Finally, we explore several applications of this energy function, including the prediction of protein structures without multiple sequence alignments.
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Studies have reported that AF2 cannot predict how mutations affect protein structure and stability.
“studies have reported an inability of AF2 to predict the effects of mutations on protein structure and stability14,49,50”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [14]
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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
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286 · see citing works
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280 · 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
Robust deep learning based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · doi:10.1101/2022.06.03.494563 · Cited by 2,107 · see citing works
Robust deep learning–based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · Science · doi:10.1126/science.add2187 · Cited by 2,070 · see citing works
The Protein-Folding Problem, 50 Years On
Dill, K. A., & MacCallum, J. L. (2012) · Science · doi:10.1126/science.1219021 · Cited by 1,699 · see citing works
The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O’Meara, M. J., DiMaio, F. P., Park, H., Shapovalov, M. V., Renfrew, P. D., Mulligan, V. K., Kappel, K., et al. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00125 · Cited by 1,532 · see citing works
Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T., & Weigt, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111471108 · Cited by 1,434 · see citing works
Protein structure homology modeling using SWISS-MODEL workspace
Bordoli, L., Kiefer, F., Arnold, K., Benkert, P., Battey, J. N. D., & Schwede, T. F. (2008) · Nature Protocols · doi:10.1038/nprot.2008.197 · Cited by 1,218 · see citing works
THE KINETICS OF FORMATION OF NATIVE RIBONUCLEASE DURING OXIDATION OF THE REDUCED POLYPEPTIDE CHAIN
Anfinsen, C. B., Haber, E., Sela, M., & White, F. H. (1961) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.47.9.1309 · Cited by 1,182 · 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
LGA: a method for finding 3D similarities in protein structures
Zemla, A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg571 · Cited by 1,069 · see citing works
Jones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr638 · Cited by 760 · see citing works
Park, H., Bradley, P. H., Greisen, P. J., Liu, Y., Mulligan, V. K., Kim, D. E., Baker, D. A., & DiMaio, F. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00819 · Cited by 564 · see citing works
Zwanzig, R., Szabo, A., & Bagchi, B. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.1.20 · Cited by 527 · see citing works
Learning generative models for protein fold families
Balakrishnan, S., Kamisetty, H., Carbonell, J. G., Lee, S., & Langmead, C. J. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22934 · Cited by 316 · see citing works
Evolvability as a Function of Purifying Selection in TEM-1 β-Lactamase
Stiffler, M. A., Hekstra, D. R., & Ranganathan, R. (2015) · Cell · doi:10.1016/j.cell.2015.01.035 · Cited by 284 · see citing works
Improved protein structure refinement guided by deep learning based accuracy estimation
Hiranuma, N., Park, H., Baek, M., Anishchenko, I., Dauparas, J., & Baker, D. (2021) · Nature Communications · doi:10.1038/s41467-021-21511-x · Cited by 243 · see citing works
Evolving New Protein-Protein Interaction Specificity through Promiscuous Intermediates
Aakre, C. D., Herrou, J., Phung, T. N., Perchuk, B. S., Crosson, S., & Laub, M. T. (2015) · Cell · doi:10.1016/j.cell.2015.09.055 · Cited by 218 · see citing works
Protein sequence design by conformational landscape optimization
Norn, C., Wicky, B. I. M., Juergens, D., Liu, S., Kim, D., Tischer, D., Koepnick, B., Anishchenko, I., Players, F., Baker, D., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2017228118 · Cited by 144 · see citing works
Self-citationImproved AlphaFold modeling with implicit experimental information
Terwilliger, T. C., Poon, B. K., Afonine, P. V., Schlicksup, C. J., Croll, T. I., Millán, C., Richardson, J. S., Read, R. J., & Adams, P. D. (2022) · doi:10.1101/2022.01.07.475350 · Cited by 140 · see citing works
How to Train Your Energy-Based ModelsPreprint
Song, Y., & Kingma, D. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03288 · Cited by 132 · see citing works
Høie, M. H., Cagiada, M., Frederiksen, A. H. B., Stein, A., & Lindorff‐Larsen, K. (2022) · Cell Reports · doi:10.1016/j.celrep.2021.110207 · Cited by 121 · see citing works
Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro
Ghani, U., Desta, I., Jindal, A., Khan, O., Jones, G., Hashemi, N., Kotelnikov, S., Padhorny, D., Vajda, S., & Kozakov, D. (2021) · doi:10.1101/2021.09.07.459290 · Cited by 68 · see citing works
Assessment of protein model structure accuracy estimation in CASP14 : Old and new challenges
Kwon, S., Won, J., Kryshtafovych, A., & Seok, C. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26192 · Cited by 56 · see citing works
Computational Protein Design Quantifies Structural Constraints on Amino Acid Covariation
Ollikainen, N., & Kortemme, T. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003313 · Cited by 36 · see citing works
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Research works printed but not linked: 1
- [14]See Supplemental Material at http://link.aps.org/supplemental/10.1103/PhysRevLett.129.238101 for additional data and analyses, which includes Refs. [ 15– 17].
Cited by
Sorted by citations
Single-sequence protein structure prediction using language models from deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455 · see citing works
Single-sequence protein structure prediction using a language model and deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455 · see citing works
Ahdritz, G., Bouatta, N., Floristean, C., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02272-z · Cited by 439 · see citing works
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants
Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2021) · doi:10.1101/2021.10.23.465575 · Cited by 335 · see citing works
SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2
Stein, R. A., & Mchaourab, H. S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010483 · Cited by 258 · see citing works
High-throughput prediction of protein conformational distributions with subsampled AlphaFold2
Monteiro da Silva, G., Cui, J. Y., Dalgarno, D. C., Lisi, G. P., & Rubenstein, B. M. (2024) · Nature Communications · doi:10.1038/s41467-024-46715-9 · Cited by 196 · see citing works
AFsample: improving multimer prediction with AlphaFold using massive sampling
Wallner, B. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad573 · Cited by 164 · see citing works
Language models generalize beyond natural proteins
Verkuil, R., Kabeli, O., Du, Y., Wicky, B. I. M., Milles, L. F., Dauparas, J., Baker, D., Ovchinnikov, S., Sercu, T., & Rives, A. (2022) · doi:10.1101/2022.12.21.521521 · Cited by 181 · see citing works
Self-citationThe power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
Agarwal, V., & McShan, A. C. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01638-w · Cited by 155 · 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
Chang, L., Mondal, A., Singh, B., Martínez‐Noa, Y., & Perez, A. (2024) · WIREs Computational Molecular Science · doi:10.1002/wcms.1693 · Cited by 56 · see citing works
Improved Multimer Prediction using Massive Sampling with AlphaFold in CASP15
Wallner, B. (2023) · doi:10.22541/au.168170986.60158084/v1 · Cited by 53 · see citing works
Challenges in bridging the gap between protein structure prediction and functional interpretation
Varadi, M., Tsenkov, M., & Velankar, S. (2025) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26614 · Cited by 36 · see citing works
Unifying Constraints Linking Protein Folding and Native Dynamics Decoded from AlphaFold
Zhang, Z., Ren, W., Xie, L., Zheng, Y., Guan, X., Wang, J., Li, W., & Tang, Q.-Y. (2026) · Physical Review Letters · doi:10.1103/7j2j-f8f7 · Cited by 5 · see citing works
Learning antibody sequence constraints from allelic inclusion
Jagota, M., Hsu, C., Mazumder, T., Sung, K., DeWitt, W. S., Listgarten, J., Matsen, F. A., Ye, C. J., & Song, Y. S. (2024) · doi:10.1101/2024.10.22.619760 · Cited by 2 · see citing works
- Authors
- Roney, J. P., & Ovchinnikov, S.
- Affiliations
- Harvard University (US)
- Corresponding author
- Ovchinnikov, Sergey
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2022-11-28
- Volume
- 129
- Issue
- 23
- Pages
- 238101-238101
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- PMC
- PMC12178128
- OpenAlex
- W4310159409
- Cited by
- 281
- References
- 27
- Citations by year
- 2026: 45 · 2025: 70 · 2024: 79 · 2023: 62 · 2022: 25
- FWCI
- 19.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Physical Review Letters (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by)
- Funders
- National Institutes of Health (DP5OD026389)
- National Science Foundation (MCB2032259)
- Simons Foundation (735929LPI)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- AlphaFold, protein structure prediction, protein folding, multiple sequence alignment, protein 3D structure, deep learning
- MeSH terms
- Algorithms, Amino Acid Sequence, Models, Molecular, Protein Conformation, Proteins
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