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Active learning-assisted directed evolution

Author

Listed:
  • Jason Yang

    (California Institute of Technology)

  • Ravi G. Lal

    (California Institute of Technology)

  • James C. Bowden

    (California Institute of Technology
    University of California-Berkeley)

  • Raul Astudillo

    (California Institute of Technology)

  • Mikhail A. Hameedi

    (California Institute of Technology)

  • Sukhvinder Kaur

    (Elegen Corp)

  • Matthew Hill

    (Elegen Corp)

  • Yisong Yue

    (California Institute of Technology)

  • Frances H. Arnold

    (California Institute of Technology
    California Institute of Technology)

Abstract

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 93%. We also perform computational simulations on existing protein sequence-fitness datasets to support our argument that ALDE can be more effective than DE. Overall, ALDE is a practical and broadly applicable strategy to unlock improved protein engineering outcomes.

Suggested Citation

  • Jason Yang & Ravi G. Lal & James C. Bowden & Raul Astudillo & Mikhail A. Hameedi & Sukhvinder Kaur & Matthew Hill & Yisong Yue & Frances H. Arnold, 2025. "Active learning-assisted directed evolution," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-55987-8
    DOI: 10.1038/s41467-025-55987-8
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