Rapid directed evolution guided by protein language models and epistatic interactions
Summary
Protein engineering is limited by the inefficient search through a high-dimensional sequence space to find combinations of synergistic mutations. Traditional approaches use stepwise mutation stacking, whereas machine learning methods require extensive datasets or multiple experimental rounds and are bottlenecked by costly, length-limited gene synthesis. We present MULTI-evolve (where MULTI stands for model-guided, universal, targeted installation of multimutants), a rapid evolution framewo
Content
# Rapid directed evolution guided by protein language models and epistatic interactions
*Published: 2026 May 7*
Protein engineering is limited by the inefficient search through a
high-dimensional sequence space to find combinations of synergistic mutations.
Traditional approaches use stepwise mutation stacking, whereas machine learning
methods require extensive datasets or multiple experimental rounds and are
bottlenecked by costly, length-limited gene synthesis. We present MULTI-evolve
(where MULTI stands for model-guided, universal, targeted installation of
multimutants), a rapid evolution framework that systematically engineers
multimutants. Our approach combines protein language models or existing
functional data with epistatic modeling to predict synergistic combinations.
Proposed multimutants are built through MULTI-assembly, a mutagenesis method
enabling high-efficiency assembly across multikilobase sequences. Applying
MULTI-evolve to three proteins achieved up to 10-fold improvements with a single
round of machine learning-guided directed evolution. MULTI-evolve provides a
streamlined approach for end-to-end, multimutant engineering for a broad range
of protein types and functions.
DOI: 10.1126/science.aea1820