Rapid protein evolution by few-shot learning with a protein language model.

ABSTRACT: Directed evolution of proteins is critical for applications in basic biological research, therapeutics, diagnostics, and sustainability. However, directed evolution methods are labor intensive, cannot efficiently optimize over multiple protein properties, and are often trapped by local maxima. In silico-directed evolution methods incorporating protein language models (PLMs) have the potential to accelerate this engineering process, but current approaches fail to generalize across diverse protein families. We introduce EVOLVEpro, a few-shot active learning framework to rapidly improve protein activity using a combination of PLMs and protein activity predictors, achieving improved activity with as few as four rounds of evolution. EVOLVEpro substantially enhances the efficiency and eff

SUBMITTER: Jiang K 

PROVIDER: S-EPMC11275896 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature