Machine learning framework for cost effective deep mutational scanning through targeted substitution profiling.

Abstract excerpt

BACKGROUND: Deep mutational scanning (DMS) provides comprehensive maps of protein variant effects but remains experimentally intensive. Machine learning (ML) approaches have the potential to reduce experimental burden of DMS by predicting the functional impact of substitutions from limited data. RESULTS: We introduced a ML classifier trained on normalised DMS scores from SARS-CoV-2 main protease (Mpro) to...

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