Peter Cameron

Engineering of CRISPR-Cas PAM recognition using deep learning of vast evolutionary data

This paper introduces Protein2PAM, a deep learning model that predicts CRISPR-Cas PAM specificity from protein sequences, enabling the design of Cas variants with tailored PAM recognition.

Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences

Language models trained on over one million mined CRISPR operons generated Cas9-like effectors and matching guide RNAs, and an AI-designed editor is reported to edit the human genome precisely.

NmeCas9 is an intrinsically high-fidelity genome editing platform

The paper demonstrates that NmeCas9, a Cas9 ortholog, exhibits high-fidelity genome editing in human cells with minimal off-target effects and multiple PAM compatibility.