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In Biophysical chemistry

A significant impediment to the improvement of clinical outcomes in treating breast and ovarian cancers rests with the lack of available interpretations for BRCA1 variants of unknown significance. Two research groups recently implemented large-scale functional assays for quantifying effects of single missense mutations on homology-directed DNA repair activity of BRCA1 variants, which is critical for tumor suppression and strongly correlates with cancer risk, and their results are significantly concordant with each other as well as with known pathogenic and benign variant clinical data. In this work, we implemented an established computational mutagenesis procedure to characterize structural impacts of single residue replacements to the BRCA1 RING domain. The computational data showed similarly strong concordance with known clinical data as well as with experimental data from both functional assays. Predictions made by models trained on our computational data offer a complementary and orthogonal approach for classifying all remaining unexplored BRCA1 RING domain variants.

Masso Majid, Bansal Anirudh, Bansal Arnav, Henderson Andrea


Computational mutagenesis, Homology-directed DNA repair, Machine learning, Prediction, Structure-function relationships, Variants