Accurate proteome-wide missense variant effect prediction with AlphaMissense
Open accessAbstract
The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.
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New VEPs, such as AlphaMissense, now utilising deep learning techniques offer significant improvements over earlier heuristic-based approaches.
“New VEPs continue rapidly to be developed, with many, like AlphaMissense, now utilising deep learning techniques that offer significant improvements over earlier heuristic-based – that is, involving computational ‘trial and error’ (27, 28) – approaches.”
From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [28]
The paper describes AlphaMissense as an AlphaFold-based model (p. 2) and reports improved performance over earlier predictors on clinical benchmarks (Improved pathogenicity classification, p. 3). It does not establish that the approaches it outperforms are heuristic-based, so that comparison is not fully supported. (LLM, conf=0.83)
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Cites
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- Authors
- Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., Schneider, R. G., Senior, A. W., Jumper, J., Hassabis, D., Kohli, P., & Avsec, Ž.
- Affiliations
- Google DeepMind (United Kingdom) (GB)
- Corresponding author
- Cheng, Jun
- Pan, Joshua
- Bycroft, Clare
- Žemgulytė, Akvilė
- Applebaum, Taylor
- Kohli, Pushmeet
- Avsec, Žiga
- ORCID
- Venue
- Science
- Published
- 2023-09-22
- Volume
- 381
- Issue
- 6664
- Pages
- eadg7492-eadg7492
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4386861232
- Cited by
- 2,163
- References
- 14
- Citations by year
- 2026: 845 · 2025: 764 · 2024: 496 · 2023: 56 · 2022: 1 · 2012: 1
- FWCI
- 346.20
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- AlphaMissense, AlphaFold, variant pathogenicity, evolutionary conservation, protein structure, single amino acid substitution, variant interpretation
- MeSH terms
- Machine Learning, Disease, Humans, Protein Conformation, Genome, Human, Sequence Alignment, Conserved Sequence, Amino Acid Substitution, Benchmarking, Mutation, Missense, Proteome, Databases, Genetic
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