Rapid directed evolution guided by protein language models and epistatic interactions

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…

• AI
  Arc Institute
• NS
  National Science Foundation
• BA
  Bill and Melinda Gates Foundation
• NS
  National Science Foundation Graduate Research Fellowship Program

  Award: 2019284848