Researchml guided designprotein engineeringuc berkeley
Researchers Use MULTI-evolve To Optimize Proteins
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UC Berkeley and Arc Institute researchers led by Patrick Hsu recently introduce MULTI-evolve, a machine-learning-guided experimental pipeline that predicts effects of multiple amino-acid mutations and directs lab validation. The method trains on single and pairwise mutation data then extrapolates to combinations of five-plus mutations, producing improved antibodies, a CRISPR protein with higher precision, and other enhanced enzymes.
Scoring Rationale
Strong methodological advance with validated lab results, limited by lack of peer-reviewed publication or wide replication.
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