Custom CRISPR-Cas9 PAM variants via scalable engineering and machine learning.

ABSTRACT: Engineering and characterizing proteins can be time-consuming and cumbersome, motivating the development of generalist CRISPR-Cas enzymes^1-4 to enable diverse genome-editing applications. However, such enzymes have caveats such as an increased risk of off-target editing^3,5,6. Here, to enable scalable reprogramming of Cas9 enzymes, we combined high-throughput protein engineering with machine learning to derive bespoke editors that are more uniquely suited to specific targets. Through structure-function-informed saturation mutagenesis and bacterial selections, we obtained nearly 1,000 engineered SpCas9 enzymes and characterized their protospacer-adjacent motif (PAM)⁷ requirements to train a neural network that relates amino acid sequence to PAM specificity. B

SUBMITTER: Silverstein RA 

PROVIDER: S-EPMC12449813 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature