In Integrative biology : quantitative biosciences from nano to macro
Advancements in the interpretation of variants of unknown significance are critical for improving clinical outcomes. In a recent study, massive parallel assays were used to experimentally quantify the effects of missense substitutions in the RING domain of BRCA1 on E3 ubiquitin ligase activity as well as BARD1 RING domain binding. These attributes were subsequently used for training a predictive model of homology-directed DNA repair levels for these BRCA1 variants relative to wild type, which is critical for tumor suppression. Here, relative structural changes characterizing BRCA1 variants were quantified by using an efficient and cost-free computational mutagenesis technique, and we show that these features lead to improvements in model performance. This work underscores the potential for bench researchers to gain valuable insights from computational tools, prior to implementing costly and time-consuming experiments.
computational mutagenesis, homology-directed DNA repair, machine learning, prediction, structure–function relationships, variants