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

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. Linderman and Peter M. Tessier ()
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Emily K. Makowski: University of Michigan
Patrick C. Kinnunen: University of Michigan
Jie Huang: University of Michigan
Lina Wu: University of Michigan
Matthew D. Smith: University of Michigan
Tiexin Wang: University of Michigan
Alec A. Desai: University of Michigan
Craig N. Streu: University of Michigan
Yulei Zhang: University of Michigan
Jennifer M. Zupancic: University of Michigan
John S. Schardt: University of Michigan
Jennifer J. Linderman: University of Michigan
Peter M. Tessier: University of Michigan

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-31457-3

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