Improved protein structure prediction using potentials from deep learning
Open accessAbstract
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence 1. This problem is of fundamental importance as the structure of a protein largely determines its function 2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures 3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force 4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction 5 (CASP13)-a blind assessment of the state of the field-AlphaFold created high-accuracy structures (with template modelling (TM) scores 6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined 7.
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Existing protein structure prediction methods fall far short of atomic or experimental accuracy, especially when no homologous structure is available, limiting their utility for many biological applications.
“Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available.”
From Highly accurate protein structure prediction with AlphaFold (2021) · cites this paper as [10]
Advances in artificial intelligence (AI) are driving a revolution across science, enabling exciting new results across chemistry, biology, and medicine.
“Advances in artificial intelligence (AI) are driving a revolution across science, enabling exciting new results across chemistry,80–82 biology,83,84 and medicine.85–88”
From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [83]
AlphaFold has achieved remarkable precision in modelling intramolecular relations, enabling prediction of a protein's three-dimensional structure solely from its DNA sequence.
“Surpassing the capabilities of any human-conceived model, the neural-network-based algorithm Alpha-Fold has achieved remarkable precision in modelling intramolecular relations, enabling it to tackle one of molecular biology's greatest challenges: predicting a protein's three-dimensional structure solely on the basis of its DNA sequence 65 .”
From Machine culture (2023) · cites this paper as [65]
In recent years, there has been major progress in utilizing residue co-evolution information for predicting structures of individual proteins, based on rapid growth of experimental data and new ideas, combined with deep learning.
“In recent years, there has been major progress in utilizing the residue co-evolution information for predicting structures of individual proteins, based on the rapid growth of experimental data on proteins and new ideas on how to use it, combined with spectacular advances in computer science (deep learning) [30–33].”
From Challenges in protein docking (2020) · cites this paper as [30]
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., 2020
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., 2020
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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
- Nature
- Published
- 2020-01-15
- Volume
- 577
- Issue
- 7792
- Pages
- 706-710
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2999044305
- Cited by
- 3,378
- References
- 57
- Citations by year
- 2026: 277 · 2025: 444 · 2024: 454 · 2023: 509 · 2022: 621 · 2021: 823 · 2020: 241 · 2019: 6
- FWCI
- 177.94
- Type
- journal article
- Language
- en
- Open access
- green
- discovery.ucl.ac.uk
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- 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, potential of mean force, neural network potentials, contact prediction, gradient descent, CASP13
- MeSH terms
- Deep Learning, Amino Acid Sequence, Models, Molecular, Protein Conformation, Proteins, Software, Protein Folding, Caspases, Datasets as Topic
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