Antibody complementarity determining region design using high-capacity machine learning
Author(s)Liu, Ge; Zeng, Haoyang; Mueller, Jonas Weylin; Carter, Brandon M.; Wang, Ziheng; Schilz, Jonas; Horny, Geraldine; Birnbaum, Michael E; Ewert, Stefan; Gifford, David K; ... Show more Show less
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Motivation: The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties. Results: Here, we present Ens-Grad, a machine learning method that can design complementarity determining regions of human Immunoglobulin G antibodies with target affinities that are superior to candidates derived from phage display panning experiments. We also demonstrate that machine learning can improve target specificity by the modular composition of models from different experimental campaigns, enabling a new integrative approach to improving target specificity. Our results suggest a new path for the discovery of therapeutic molecules by demonstrating that predictive and differentiable models of antibody binding can be learned from high-throughput experimental data without the need for target structural data.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Department of Biological Engineering; Koch Institute for Integrative Cancer Research at MIT
Oxford University Press (OUP)
Liu, Ge et al. "Antibody complementarity determining region design using high-capacity machine learning." Bioinformatics 36, 7 (November 2019): 2126–2133 © 2019 The Author(s)
Final published version