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

The genome-editing Cas9 protein uses multiple amino-acid residues to bind the target DNA. Considering only the residues in proximity to the target DNA as potential sites to optimise Cas9's activity, the number of combinatorial variants to screen through is too massive for a wet-lab experiment. Here we generate and cross-validate ten in silico and experimental datasets of multi-domain combinatorial mutagenesis libraries for Cas9 engineering, and demonstrate that a machine learning-coupled engineering approach reduces the experimental screening burden by as high as 95% while enriching top-performing variants by ∼7.

High-fidelity KKH variant of Staphylococcus aureus Cas9 nucleases with improved base mismatch discrimination.

The Cas9 nuclease from Staphylococcus aureus (SaCas9) holds great potential for use in gene therapy, and variants with increased fidelity have been engineered. However, we find that existing variants have not reached the greatest accuracy to discriminate base mismatches and exhibited much reduced activity when their mutations were grafted onto the KKH mutant of SaCas9 for editing an expanded set of DNA targets. We performed structure-guided combinatorial mutagenesis to re-engineer KKH-SaCas9 with enhanced accuracy.

A Three-Way Combinatorial CRISPR Screen for Analyzing Interactions among Druggable Targets.

We present a CRISPR-based multi-gene knockout screening system and toolkits for extensible assembly of barcoded high-order combinatorial guide RNA libraries en masse. We apply this system for systematically identifying not only pairwise but also three-way synergistic therapeutic target combinations and successfully validate double- and triple-combination regimens for suppression of cancer cell growth and protection against Parkinson's disease-associated toxicity. This system overcomes the practical challenges of experimenting on a large number of high-order genetic and drug combinations and can be applied to uncover the rare synergistic interactions between druggable targets.