Integration of protein stability and AlphaMissense scores improves bioinformatic impact prediction for p53 missense and in-frame amino acid deletion variants.

ABSTRACT: The clinical classification of germline missense variants and single-amino-acid deletions is challenging. The BayesDel and Align-GVGD bioinformatic prediction tools currently used for ClinGen TP53 variant curation expert panel (VCEP) classification do not directly capture changes in protein folding stability, measured using computed destabilization energies (ΔΔG scores). The AlphaMissense tool recently developed by Google DeepMind to predict pathogenicity for all human proteome missense variants is trained in part using AlphaFold2 architecture. Our study investigated whether protein folding stability and/or AlphaMissense scores could improve impact prediction for p53 missense and single-amino-acid deletion variants. ΔΔG scores were calculated for missense variants using FoldX and for singl

SUBMITTER: Rotenberg N 

PROVIDER: S-EPMC12120181 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature