What it is
The team mined 26 terabases of assembled genomes and metagenomes to curate a dataset of more than 1 million CRISPR operons, then trained protein language models on it to generate novel gene editors. The models produced 4.8 times as many protein clusters as are found across natural CRISPR-Cas families, and several generated editors matched or beat the standard SpCas9 on activity and specificity while sitting about 400 mutations away in sequence. One design, released as OpenCRISPR-1, performs precision editing of the human genome and is also compatible with base editing.
Why it matters
CRISPR editors are normally discovered in microbes and often work poorly when moved into human cells. Generating them with a model trained on biological diversity shows functional editing enzymes can be invented rather than found, about 400 mutations from any natural protein, bypassing evolutionary constraints. Comparable or better activity and specificity than SpCas9 suggests AI design can target the exact properties a therapy needs.
Underlined numbers link to their source. Every metric and quoted figure is listed under Sources and data below.
Filed underCRISPR, protein design, gene editing, generative biology, genomics
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