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Accurate proteome-wide missense variant effect prediction with AlphaMissense.
Cheng, Jun; Novati, Guido; Pan, Joshua; Bycroft, Clare; Zemgulyte, Akvile; Applebaum, Taylor; Pritzel, Alexander; Wong, Lai Hong; Zielinski, Michal; Sargeant, Tobias; Schneider, Rosalia G; Senior, Andrew W; Jumper, John; Hassabis, Demis; Kohli, Pushmeet; Avsec, Ziga.
Affiliation
  • Cheng J; Google DeepMind, London, UK.
  • Novati G; Google DeepMind, London, UK.
  • Pan J; Google DeepMind, London, UK.
  • Bycroft C; Google DeepMind, London, UK.
  • Zemgulyte A; Google DeepMind, London, UK.
  • Applebaum T; Google DeepMind, London, UK.
  • Pritzel A; Google DeepMind, London, UK.
  • Wong LH; Google DeepMind, London, UK.
  • Zielinski M; Google DeepMind, London, UK.
  • Sargeant T; Google DeepMind, London, UK.
  • Schneider RG; Google DeepMind, London, UK.
  • Senior AW; Google DeepMind, London, UK.
  • Jumper J; Google DeepMind, London, UK.
  • Hassabis D; Google DeepMind, London, UK.
  • Kohli P; Google DeepMind, London, UK.
  • Avsec Z; Google DeepMind, London, UK.
Science ; 381(6664): eadg7492, 2023 09 22.
Article in En | MEDLINE | ID: mdl-37733863
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Disease / Sequence Alignment / Amino Acid Substitution / Mutation, Missense / Proteome Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Science Year: 2023 Document type: Article Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Disease / Sequence Alignment / Amino Acid Substitution / Mutation, Missense / Proteome Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Science Year: 2023 Document type: Article Country of publication: United States