Researchers Use MULTI-evolve To Optimize Proteins

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.