Machine learning-coupled combinatorial mutagenesis enables resource-efficient engineering of CRISPR-Cas9 genome editor activities

Abstract

Screening combinatorial mutants is too massive for wet-lab experiment alone. Here the authors present a machine learning-coupled combinatorial mutagenesis approach to vastly reduce experimental burden for engineering Cas9 genome editing enzymes.