Abstract

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.

Keywords

Missense mutationGeneticsBiologyPathogenicityComputational biologyPopulationGeneGenomeContext (archaeology)ProteomeMutationMedicine

MeSH Terms

HumansAmino Acid SubstitutionBenchmarkingConserved SequenceDatabasesGeneticDiseaseGenomeHumanMutationMissenseProtein ConformationProteomeSequence AlignmentMachine Learning

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Publication Info

Year
2023
Type
article
Volume
381
Issue
6664
Pages
eadg7492-eadg7492
Citations
1550
Access
Closed

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Cite This

Jun Cheng, Guido Novati, Joshua Pan et al. (2023). Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science , 381 (6664) , eadg7492-eadg7492. https://doi.org/10.1126/science.adg7492

Identifiers

DOI
10.1126/science.adg7492
PMID
37733863

Data Quality

Data completeness: 86%