Designing Fidelity of CRISPR-Cas Endonucleases by Kinetic Insights

Finding high-fidelity CRISPR-Cas variants is critical for both the precision of in vitro DNA detection and the safety of in vivo gene-editing therapeutics. However, the large size of the Cas enzyme and the distinct selection criteria between its natural evolution and clinical practice lead to extensive experimental trials and limited success rate for de novo design, directed evolution, protein language model (PLM)-based filtering. Here, we present a PLM-assisted physics-driven approach that utilizes atomistic molecular dynamics simulations and automated path searching to efficiently obtain the complete kinetic insights, including the transition state structures, for the conformational changes of Cas before DNA cleavage. We show that these kinetic insights can pinpoint a few fidelity-diminishing protein residues during the early stage of target recognition, and have led to SpyCas9 and FnCas12a variants with ultra-high fidelity surpassing previously reported counterparts at minimal cost of a few wet-lab trials.