Deep learning tools predict variants in disordered regions with lower sensitivity.

Journal: BMC genomics
PMID: 40221640

Abstract

BACKGROUND: The recent AI breakthrough of AlphaFold2 has revolutionized 3D protein structural modeling, proving crucial for protein design and variant effects prediction. However, intrinsically disordered regions-known for their lack of well-defined structure and lower sequence conservation-often yield low-confidence models. The latest Variant Effect Predictor (VEP), AlphaMissense, leverages AlphaFold2 models, achieving over 90% sensitivity and specificity in predicting variant effects. However, the effectiveness of tools for variants in disordered regions, which account for 30% of the human proteome, remains unclear.

Authors

  • Federica Luppino
    Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstrasse 108, 01307, Dresden, Germany.
  • Swantje Lenz
    Chair of Bioanalytics, Technische Universität Berlin, Berlin, Germany.
  • Chi Fung Willis Chow
    Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstrasse 108, 01307, Dresden, Germany.
  • Agnes Toth-Petroczy
    Max Planck Institute of Molecular Cell Biology and Genetics, 01307, Dresden, Germany. toth-petroczy@mpi-cbg.de.

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