Active learning-assisted directed evolution

Directed evolution (DE) is a powerful tool to optimize protein fitness for a specific application. However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior. Here, we present Active Learning-assisted Directed Evolution (ALDE), an iterative machine learning-assisted DE workflow that leverages uncertainty quantification to explore the search space of proteins more efficiently than current DE methods. We apply ALDE to an engineering landscape that is challenging for DE: optimization of five epistatic residues in the active site of an enzyme. In three rounds of wet-lab experimentation, we improve the yield of a desired product of a non-native cyclopropanation reaction from 12% to…

• NS
  National Science Foundation

  Award: W911NF-19
• AR
  Army Research Office

  Awards: W911NF-19-2-0026, W911NF, W911NF-19
• IF
  Institute for Collaborative Biotechnologies

  Award: W911NF-19-2-0026