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Simultaneous enhancement of multiple functional properties using evolution-informed protein design

Author

Listed:
  • Benjamin Fram

    (Harvard Medical School
    Dana-Farber Cancer Institute)

  • Yang Su

    (Harvard Medical School)

  • Ian Truebridge

    (Institute for Protein Innovation
    Harvard Medical School
    AI Proteins)

  • Adam J. Riesselman

    (Harvard Medical School
    Harvard Medical School)

  • John B. Ingraham

    (Harvard Medical School)

  • Alessandro Passera

    (Dana-Farber Cancer Institute
    Vienna BioCenter (VBC))

  • Eve Napier

    (Trinity College Dublin)

  • Nicole N. Thadani

    (Harvard Medical School
    Apriori Bio)

  • Samuel Lim

    (Harvard Medical School)

  • Kristen Roberts

    (Selux Diagnostics Inc.)

  • Gurleen Kaur

    (Selux Diagnostics Inc.)

  • Michael A. Stiffler

    (Dana-Farber Cancer Institute
    Dyno Therapeutics)

  • Debora S. Marks

    (Harvard Medical School
    Broad Institute of MIT and Harvard)

  • Christopher D. Bahl

    (Institute for Protein Innovation
    Harvard Medical School
    AI Proteins)

  • Amir R. Khan

    (Trinity College Dublin
    Boston Children’s Hospital)

  • Chris Sander

    (Harvard Medical School
    Dana-Farber Cancer Institute
    Broad Institute of MIT and Harvard)

  • Nicholas P. Gauthier

    (Harvard Medical School
    Dana-Farber Cancer Institute
    Broad Institute of MIT and Harvard)

Abstract

A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 β-lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications.

Suggested Citation

  • Benjamin Fram & Yang Su & Ian Truebridge & Adam J. Riesselman & John B. Ingraham & Alessandro Passera & Eve Napier & Nicole N. Thadani & Samuel Lim & Kristen Roberts & Gurleen Kaur & Michael A. Stiffl, 2024. "Simultaneous enhancement of multiple functional properties using evolution-informed protein design," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49119-x
    DOI: 10.1038/s41467-024-49119-x
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