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

Journal: Bioinformatics (Oxford, England)
PMID: 37084271

Abstract

MOTIVATION: Missense variants are a frequent class of variation within the coding genome, and some of them cause Mendelian diseases. Despite advances in computational prediction, classifying missense variants into pathogenic or benign remains a major challenge in the context of personalized medicine. Recently, the structure of the human proteome was derived with unprecedented accuracy using the artificial intelligence system AlphaFold2. This raises the question of whether AlphaFold2 wild-type structures can improve the accuracy of computational pathogenicity prediction for missense variants.

Authors

  • Axel Schmidt
    Institute of Human Genetics, University of Bonn, Medical Faculty & University Hospital Bonn, Bonn, Germany.
  • Sebastian Röner
    Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Karola Mai
    Institute of Human Genetics, Bonn School of Medicine, University Hospital of Bonn, University of Bonn, Bonn, Germany.
  • Hannah Klinkhammer
    Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
  • Martin Kircher
    Charité - Universitätsmedizin Berlin, 10117, Berlin, Germany. martin.kircher@bihealth.de.
  • Kerstin U Ludwig
    Institute of Human Genetics, Bonn School of Medicine, University Hospital of Bonn, University of Bonn, Bonn, Germany.

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