Protein structure prediction using multiple deep neural networks in the 13th critical assessment of protein structure prediction (CASP13)
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We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13. Submissions were made by three free-modeling (FM) methods which combine the predictions of three neural networks. All three systems were guided by predictions of distances between pairs of residues produced by a neural network. Two systems assembled fragments produced by a generative neural network, one using scores from a network trained to regress GDT_TS. The third system shows that simple gradient descent on a properly constructed potential is able to perform on par with more expensive traditional search techniques and without requiring domain segmentation. In the CASP13 FM assessors' ranking by summed z-scores, this system scored highest with 68.3 vs 48.2 for the next closest group (an average GDT_TS of 61.4). The system produced high-accuracy structures (with GDT_TS scores of 70 or higher) for 11 out of 43 FM domains. Despite not explicitly using template information, the results in the template category were comparable to the best performing template-based methods.
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AlphaFold uses a deep neural network to generate and/or interpret the amino-acid proximity network as a central data structure.
“For instance, AlphaFold, a recent major achievement in protein folding prediction (Senior et al., 2019, 2020), uses a deep neural network to generate (initial AlphaFold version) and/or interpret (most recent implementation) the amino-acid proximity network as a central data structure.”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Senior et al., 2019
Amino-acid proximity networks are central to many efforts to model protein structure and to protein structure prediction.
“Such amino-acid proximity networks, whether experimentally generated or inferred, are central to many efforts to model protein structure. They are also central to protein structure prediction.”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Senior et al., 2019
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- Authors
- Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., Penedones, H., Petersen, S., Simonyan, K., Crossan, S., Kohli, P., Jones, D. T., Silver, D., Kavukcuoglu, K., & Hassabis, D.
- Affiliations
- Google DeepMind (United Kingdom) (GB)
- The Francis Crick Institute (GB)
- Corresponding author
- Senior, Andrew W.
- ORCID
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2019-11-11
- Volume
- 87
- Issue
- 12
- Pages
- 1141-1148
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC7079254
- OpenAlex
- W2980272550
- Cited by
- 347
- References
- 36
- Citations by year
- 2026: 16 · 2025: 32 · 2024: 29 · 2023: 68 · 2022: 45 · 2021: 99 · 2020: 50 · 2019: 7
- FWCI
- 16.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by-nc-nd)
- UCL Discovery (University College London) (submittedVersion)
- The Francis Crick Institute
- Funders
- DeepMind
- 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
- protein structure prediction, AlphaFold, CASP13, multiple neural networks, generative neural networks, fragment assembly, gradient descent, GDT_TS, template-based modeling
- MeSH terms
- Algorithms, Models, Molecular, Protein Conformation, Proteins, Neural Networks, Computer, Protein Folding, Computational Biology, Databases, Protein
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