Protein structure prediction by AlphaFold 2: are attention and symmetries all you need?
Open accessAbstract
The functions of most proteins result from their 3D structures, but determining their structures experimentally remains a challenge, despite steady advances in crystallography, NMR and single-particle cryoEM. Computationally predicting the structure of a protein from its primary sequence has long been a grand challenge in bioinformatics, intimately connected with understanding protein chemistry and dynamics. Recent advances in deep learning, combined with the availability of genomic data for inferring co-evolutionary patterns, provide a new approach to protein structure prediction that is complementary to longstanding physics-based approaches. The outstanding performance of AlphaFold2 in the recent Critical Assessment of protein Structure Prediction (CASP14) experiment demonstrates the remarkable power of deep learning in structure prediction. In this perspective, we focus on the key features of AlphaFold2, including its use of (i) attention mechanisms and Transformers to capture long-range dependencies, (ii) symmetry principles to facilitate reasoning over protein structures in three dimensions and (iii) end-to-end differentiability as a unifying framework for learning from protein data. The rules of protein folding are ultimately encoded in the physical principles that underpin it; to conclude, the implications of having a powerful computational model for structure prediction that does not explicitly rely on those principles are discussed.
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AlphaFold2 can predict three-dimensional protein structures accurately from sequence alone, which may solve the long-standing structure prediction problem.
“enables accurate prediction of three-dimensional protein structures from only the target amino acid sequence”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [4]
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“…with its deep learning algorithm that enables accurate prediction of three-dimensional (3D) protein structures from only the target amino acid sequence, potentially solving the half-a-century-old protein structure-prediction problem: how to predict 3D structures from only sequence information 1–4 .”
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Self-citation
- Authors
- Bouatta, N., Sorger, P., & AlQuraishi, M.
- Affiliations
- Harvard University (US)
- Columbia University (US)
- Corresponding author
- Bouatta, Nazim
- Sorger, Peter
- AlQuraishi, Mohammed
- Venue
- Acta Crystallographica Section D Structural Biology
- Published
- 2021-07-29
- Volume
- 77
- Issue
- 8
- Pages
- 982-991
- ISSN
- 2059-7983 (ISSN-L)
- PMC
- PMC8329862
- OpenAlex
- W3187563093
- Cited by
- 69
- References
- 42
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 14 · 2023: 12 · 2022: 16 · 2021: 6
- FWCI
- 3.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Acta Crystallographica Section D Structural Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Defense Advanced Research Projects Agency (HR0011-19-2-0022)
- National Cancer Institute (U54-CA225088)
- 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
- AlphaFold2, protein structure prediction, attention mechanism, transformer models, protein symmetry, long-range dependencies, protein folding, deep learning, CASP14
- MeSH terms
- Algorithms, Animals, Humans, Protein Conformation, Proteins, Computational Biology, Caspases, Databases, Protein
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