Anthropic says Claude found a new CRISPR-like enzyme system
About 950 autonomous Claude agents spent 21 hours searching 1.9 billion protein clusters, and human scientists at Anthropic's new wet lab then confirmed the finding in an unreviewed preprint.
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Anthropic said an autonomous search by roughly 950 instances of Claude, run over about 21 hours across 1.9 billion protein clusters in genomic databases, identified a previously uncharacterised system in bacteriophage DNA that the company said resembles the programmable architecture behind CRISPR gene editing. Anthropic named the system “array-associated reverse transcriptases,” or ART: a long array of repeating, non-coding DNA sequence sitting alongside a reverse-transcriptase enzyme and a neighbouring gene of unknown function.
The announcement doubled as the first public result from a new Anthropic life-sciences research group, based in the Bay Area, in which human scientists carry out physical wet-lab work — synthesising and testing candidate sequences — that follows from Claude’s computational search rather than replacing it. The underlying reverse transcriptase had turned up in earlier studies of a “jumbo” bacteriophage, but Anthropic said Claude was the first to notice the accessory array and gene that give the system its CRISPR-like structure.
Feng Zhang, a CRISPR pioneer at MIT and the Broad Institute who was not involved in the work, reviewed the finding and was quoted calling it “an exciting example of how AI agents can contribute to biological discovery,” while saying the repeat-array pattern “merits further investigation.” Anthropic was explicit that it does not yet know what the system does — whether it participates in phage defence, gene regulation or something else — and the finding was published as an unreviewed preprint, without independent replication at the time of the announcement.
The result is one of a small number of claimed cases in which an AI system’s own search, rather than a researcher’s hypothesis, pointed to something in biology that experts had not previously described. Its significance rests on that search having found a genuine, useful pattern in public data that specialists had overlooked — a claim that stands or falls on the peer review and replication still to come, not on the announcement itself.
In the commentary
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