Antibody complementarity determining region design using high-capacity machine learning
Name
btz895.pdf
Description
Published version
Size
4.85 MB
Format
Adobe PDF
Checksum (MD5)
b5415503381e5124defed46380743018
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
Date Issued
November 2019
Journal
Bioinformatics
Publisher
Oxford University Press (OUP)
Citation
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)
Version
Final published version
Abstract
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.
MIT Department
Massachusetts 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
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/bioinformatics/btz895
