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

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

Tuesday May 7th, 4-5pm EST | Jeff Ruffolo, PhD and Stephen Nayfach, PhD

Abstract: Gene editing has the potential to solve fundamental challenges in agriculture, biotechnology, and human health. CRISPR-based gene editors derived from microbes, while powerful, often show significant functional tradeoffs when ported into non-native environments, such as human cells. Artificial intelligence (AI) enabled design provides a powerful alternative with potential to bypass evolutionary constraints and generate editors with optimal properties. Here, using large language models (LLMs) trained on biological diversity at scale, we demonstrate the first successful precision editing of the human genome with a programmable gene editor designed with AI. To achieve this goal, we curated a dataset of over one million CRISPR operons through systematic mining of 26 terabases of assembled genomes and meta-genomes. We demonstrate the capacity of our models by generating 4.8x the number of protein clusters across CRISPR-Cas families found in nature and tailoring single-guide RNA sequences for Cas9-like effector proteins. We experimentally tested 209 of our novel Cas9-like proteins in human cells with a hit-rate of 63%. Our most performant gene editor, denoted as OpenCRISPR-1, demonstrates comparable activity and markedly improved specificity relative to SpCas9, the prototypical gene editor, while being 400+ mutations away in sequence. Our results highlight the power of LLMs for generating novel proteins with complex functional activities beyond the scope of structure-based design methods.

Preprint: https://www.biorxiv.org/content/10.11...