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The RESP AI model accelerates the identification of tight-binding antibodies

Author

Listed:
  • Jonathan Parkinson

    (University of California, San Diego)

  • Ryan Hard

    (University of California, San Diego)

  • Wei Wang

    (University of California, San Diego
    University of California, San Diego)

Abstract

High-affinity antibodies are often identified through directed evolution, which may require many iterations of mutagenesis and selection to find an optimal candidate. Deep learning techniques hold the potential to accelerate this process but the existing methods cannot provide the confidence interval or uncertainty needed to assess the reliability of the predictions. Here we present a pipeline called RESP for efficient identification of high affinity antibodies. We develop a learned representation trained on over 3 million human B-cell receptor sequences to encode antibody sequences. We then develop a variational Bayesian neural network to perform ordinal regression on a set of the directed evolution sequences binned by off-rate and quantify their likelihood to be tight binders against an antigen. Importantly, this model can assess sequences not present in the directed evolution library and thus greatly expand the search space to uncover the best sequences for experimental evaluation. We demonstrate the power of this pipeline by achieving a 17-fold improvement in the KD of the PD-L1 antibody Atezolizumab and this success illustrates the potential of RESP in facilitating general antibody development.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36028-8
    DOI: 10.1038/s41467-023-36028-8
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    1. 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.
    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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