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Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries

Author

Listed:
  • Lin Li

    (Massachusetts Institute of Technology Lincoln Laboratory)

  • Esther Gupta

    (Massachusetts Institute of Technology Lincoln Laboratory)

  • John Spaeth

    (Massachusetts Institute of Technology Lincoln Laboratory)

  • Leslie Shing

    (Massachusetts Institute of Technology Lincoln Laboratory)

  • Rafael Jaimes

    (Massachusetts Institute of Technology Lincoln Laboratory)

  • Emily Engelhart

    (A-Alpha Bio, Inc.)

  • Randolph Lopez

    (A-Alpha Bio, Inc.)

  • Rajmonda S. Caceres

    (Massachusetts Institute of Technology Lincoln Laboratory)

  • Tristan Bepler

    (Massachusetts Institute of Technology
    New York Structural Biology Center)

  • Matthew E. Walsh

    (Massachusetts Institute of Technology Lincoln Laboratory
    Johns Hopkins Bloomberg School of Public Health)

Abstract

Therapeutic antibodies are an important and rapidly growing drug modality. However, the design and discovery of early-stage antibody therapeutics remain a time and cost-intensive endeavor. Here we present an end-to-end Bayesian, language model-based method for designing large and diverse libraries of high-affinity single-chain variable fragments (scFvs) that are then empirically measured. In a head-to-head comparison with a directed evolution approach, we show that the best scFv generated from our method represents a 28.7-fold improvement in binding over the best scFv from the directed evolution. Additionally, 99% of designed scFvs in our most successful library are improvements over the initial candidate scFv. By comparing a library’s predicted success to actual measurements, we demonstrate our method’s ability to explore tradeoffs between library success and diversity. Results of our work highlight the significant impact machine learning models can have on scFv development. We expect our method to be broadly applicable and provide value to other protein engineering tasks.

Suggested Citation

  • Lin Li & Esther Gupta & John Spaeth & Leslie Shing & Rafael Jaimes & Emily Engelhart & Randolph Lopez & Rajmonda S. Caceres & Tristan Bepler & Matthew E. Walsh, 2023. "Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39022-2
    DOI: 10.1038/s41467-023-39022-2
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    References listed on IDEAS

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    1. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
    2. Jung-Eun Shin & Adam J. Riesselman & Aaron W. Kollasch & Conor McMahon & Elana Simon & Chris Sander & Aashish Manglik & Andrew C. Kruse & Debora S. Marks, 2021. "Protein design and variant prediction using autoregressive generative models," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
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    1. Tianyu Wu & Min Zhou & Jingcheng Zou & Qi Chen & Feng Qian & Jürgen Kurths & Runhui Liu & Yang Tang, 2024. "AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria," Nature Communications, Nature, vol. 15(1), pages 1-22, December.

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