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Predicting the pathogenicity of missense variants using features derived from AlphaFold2.

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Predicting missense variant pathogenicity is crucial for personalized medicine. Integrating AlphaFold2 structures with existing tools enhances prediction accuracy for these genetic variations.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Structural Biology

Background:

  • Missense variants are common genetic variations that can cause Mendelian diseases.
  • Accurate classification of missense variants is essential for personalized medicine but remains challenging.
  • Recent advances in artificial intelligence, such as AlphaFold2, have provided highly accurate human proteome structures.

Purpose of the Study:

  • To investigate whether AlphaFold2-predicted wild-type structures can improve the accuracy of computational pathogenicity prediction for missense variants.
  • To develop a novel pathogenicity prediction score leveraging AlphaFold2 structures.

Main Methods:

  • Engineered features from AlphaFold2 structures for each amino acid.
  • Trained a random forest model using common (proxy-benign) and singleton (proxy-pathogenic) missense variants from gnomAD v3.1.
  • Developed AlphScore, an AlphaFold2-based pathogenicity prediction score incorporating features like solvent accessibility and physicochemical environment.

Main Results:

  • AlphScore alone showed lower performance compared to existing scores (CADD, REVEL).
  • Combining AlphScore with existing scores significantly improved pathogenicity prediction accuracy.
  • Performance enhancement was validated using deep mutational scan data and ClinVar missense variants.

Conclusions:

  • Integration of AlphaFold2-predicted protein structures can enhance the accuracy of missense variant pathogenicity prediction.
  • AlphScore, when combined with established methods, offers a valuable tool for genetic variant interpretation.
  • Public availability of AlphScore and associated data facilitates further research and clinical application.