Generative AI impact on protein stability prediction in breast cancer genes

The functional classification of a missense variant in cancer predisposition genes is often challenging due to how rare the variant is observed in the population. When available, clinicians utilize a combination of family history, in vitro functional assays and in silico methods to infer protein function. In silico methods, such as missense predictors (predict changes in protein function) and protein stability predictors (predict changes in free energy) have been used to help classify a missense variant in accordance with the American College of Medical Genetics and Genomics (ACMG) guideline. To measure protein stability, many in silico algorithms predict stability based on the change of free energy and most accurate protein stability predictors require a wild-type protein template. In this study, we examine the use of generative AI to predict high-resolution protein structures as templates analyzed with protein stability methods to evaluate loss of function (LOF) activity in cancer predisposition genes BRCA1, BRCA2, PALB2 and RAD51C upon the presence of missense variant. Utilizing multiplexed assay of variant effect measurements and variant classifications from ClinVar, we find that prediction of Gibbs free energy (ΔΔG) from AlphaFold2 (AF2) structures analyzed with FoldX predicts LOF better than experimental-derived wild type structures in the BRCT domain of BRCA1 and the DNA binding domain (DBD) of BRCA2 , but not in PALB2 and RAD51C . We also find that AF2 structures in the BRCT domain of BRCA1 and DBD-Dss1 domain of BRCA2 analyzed with FoldX measure homologous DNA recombination (HDR) activity significantly better than Rosetta and DDGun3D. Our study also revealed that there are other factors that contribute to predicting loss of function activity other than protein stability, with AlphaMissense ranking the best overall predictor of LOF activity in these tumor suppressor breast cancer genes.