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Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space

Author

Listed:
  • Emily K. Makowski

    (University of Michigan
    University of Michigan)

  • Patrick C. Kinnunen

    (University of Michigan)

  • Jie Huang

    (University of Michigan
    University of Michigan)

  • Lina Wu

    (University of Michigan
    University of Michigan)

  • Matthew D. Smith

    (University of Michigan
    University of Michigan)

  • Tiexin Wang

    (University of Michigan
    University of Michigan)

  • Alec A. Desai

    (University of Michigan
    University of Michigan)

  • Craig N. Streu

    (University of Michigan
    University of Michigan
    Albion College)

  • Yulei Zhang

    (University of Michigan
    University of Michigan)

  • Jennifer M. Zupancic

    (University of Michigan
    University of Michigan)

  • John S. Schardt

    (University of Michigan
    University of Michigan
    University of Michigan)

  • Jennifer J. Linderman

    (University of Michigan
    University of Michigan)

  • Peter M. Tessier

    (University of Michigan
    University of Michigan
    University of Michigan
    University of Michigan)

Abstract

Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.

Suggested Citation

  • Emily K. Makowski & Patrick C. Kinnunen & Jie Huang & Lina Wu & Matthew D. Smith & Tiexin Wang & Alec A. Desai & Craig N. Streu & Yulei Zhang & Jennifer M. Zupancic & John S. Schardt & Jennifer J. Lin, 2022. "Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31457-3
    DOI: 10.1038/s41467-022-31457-3
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    References listed on IDEAS

    as
    1. Regina S Salvat & Andrew S Parker & Yoonjoo Choi & Chris Bailey-Kellogg & Karl E Griswold, 2015. "Mapping the Pareto Optimal Design Space for a Functionally Deimmunized Biotherapeutic Candidate," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-15, January.
    2. Richard N. McLaughlin Jr & Frank J. Poelwijk & Arjun Raman & Walraj S. Gosal & Rama Ranganathan, 2012. "The spatial architecture of protein function and adaptation," Nature, Nature, vol. 491(7422), pages 138-142, November.
    3. 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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    Cited by:

    1. Jonathan Parkinson & Ryan Hard & Wei Wang, 2023. "The RESP AI model accelerates the identification of tight-binding antibodies," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

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