Using AI-predicted protein structures as a reference to predict loss-of-function activity in tumor suppressor breast cancer genes.

ABSTRACT:

Background

The loss-of-function (LOF) classification of most missense variants in tumor suppressor breast cancer genes BRCA1, BRCA2, PALB2, and RAD51C remains unclassified and confounds clinical actionability. Classifying these variants is challenging due to their rarity, leading clinicians to rely on in silico predictive methods. Protein stability changes are associated with function, making stability predictors valuable. Stability predictions upon missense variant perturbations require high-resolution protein structures. However, the availability of these high-resolution structures is lacking. This study explores using generative AI to predict high-resolution protein structures, which can then be analyzed with in silico protein stability prediction met

SUBMITTER: Gnanaolivu R 

PROVIDER: S-EPMC11490748 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature