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; Peter M. Tessier

doi:10.1038/s41467-022-31457-3

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

Emily K. Makowski (Department of Pharmaceutical Sciences, University of Michigan) Patrick C. Kinnunen (Department of Chemical Engineering, University of Michigan) Jie Huang (Department of Pharmaceutical Sciences, University of Michigan) Lina Wu (Biointerfaces Institute, University of Michigan) Matthew D. Smith (Biointerfaces Institute, University of Michigan) Tiexin Wang (Biointerfaces Institute, University of Michigan) Alec A. Desai (Biointerfaces Institute, University of Michigan) Craig N. Streu (Department of Pharmaceutical Sciences, University of Michigan) Yulei Zhang (Biointerfaces Institute, University of Michigan) Jennifer M. Zupancic (Biointerfaces Institute, University of Michigan) John S. Schardt (Department of Pharmaceutical Sciences, University of Michigan) Jennifer J. Linderman (Department of Chemical Engineering, University of Michigan) Peter M. Tessier (Department of Pharmaceutical Sciences, University of Michigan)

Journal: Nature Communications • 2022-07-01 • Nature Portfolio • ISSN: 2041-1723

Abstract

Optimising antibody properties such as affinity can be detrimental to other key properties. Here the authors use machine learning to simplify the identification of antibodies with co-optimal levels of affinity and specificity for a clinical-stage antibody that displays high levels of on- and off-target binding.

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How to Cite this Article

                    Emily K. Makowski, Patrick C. Kinnunen, Jie Huang, et al. (2022). "Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space". Nature Communications. https://doi.org/10.1038/s41467-022-31457-3                

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DOI: 10.1038/s41467-022-31457-3 ↗
Primary Index: DOAJ