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Protein engineering using variational free energy approximation

Author

Listed:
  • Evgenii Lobzaev

    (The University of Edinburgh
    The University of Edinburgh)

  • Michael A. Herrera

    (The University of Edinburgh)

  • Martyna Kasprzyk

    (The University of Edinburgh)

  • Giovanni Stracquadanio

    (The University of Edinburgh)

Abstract

Engineering proteins is a challenging task requiring the exploration of a vast design space. Traditionally, this is achieved using Directed Evolution (DE), which is a laborious process. Generative deep learning, instead, can learn biological features of functional proteins from sequence and structural datasets and return novel variants. However, most models do not generate thermodynamically stable proteins, thus leading to many non-functional variants. Here we propose a model called PRotein Engineering by Variational frEe eNergy approximaTion (PREVENT), which generates stable and functional variants by learning the sequence and thermodynamic landscape of a protein. We evaluate PREVENT by designing 40 variants of the conditionally essential E. coli phosphotransferase N-acetyl-L-glutamate kinase (EcNAGK). We find 85% of the variants to be functional, with 55% of them showing similar growth rate compared to the wildtype enzyme, despite harbouring up to 9 mutations. Our results support a new approach that can significantly accelerate protein engineering.

Suggested Citation

  • Evgenii Lobzaev & Michael A. Herrera & Martyna Kasprzyk & Giovanni Stracquadanio, 2024. "Protein engineering using variational free energy approximation," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54814-w
    DOI: 10.1038/s41467-024-54814-w
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    References listed on IDEAS

    as
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