Antibody complementarity determining region design using high-capacity machine learning
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...
| Main Authors: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Oxford University Press (OUP)
2021
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| Online Access: | https://hdl.handle.net/1721.1/129332 |
| _version_ | 1811088481129070592 |
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| author | Liu, Ge Zeng, Haoyang Mueller, Jonas Weylin Carter, Brandon M. Wang, Ziheng Schilz, Jonas Horny, Geraldine Birnbaum, Michael E Ewert, Stefan Gifford, David K |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Liu, Ge Zeng, Haoyang Mueller, Jonas Weylin Carter, Brandon M. Wang, Ziheng Schilz, Jonas Horny, Geraldine Birnbaum, Michael E Ewert, Stefan Gifford, David K |
| author_sort | Liu, Ge |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:02:50Z |
| format | Article |
| id | mit-1721.1/129332 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:02:50Z |
| publishDate | 2021 |
| publisher | Oxford University Press (OUP) |
| record_format | dspace |
| spelling | mit-1721.1/1293322022-10-01T18:48:05Z Antibody complementarity determining region design using high-capacity machine learning Liu, Ge Zeng, Haoyang Mueller, Jonas Weylin Carter, Brandon M. Wang, Ziheng Schilz, Jonas Horny, Geraldine Birnbaum, Michael E Ewert, Stefan Gifford, David K 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 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. National Institutes of Health (Grant R01CA218094) 2021-01-07T22:12:52Z 2021-01-07T22:12:52Z 2019-11 2019-10 2020-12-15T17:22:50Z Article http://purl.org/eprint/type/JournalArticle 1367-4803 1460-2059 https://hdl.handle.net/1721.1/129332 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) en http://dx.doi.org/10.1093/bioinformatics/btz895 Bioinformatics Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Oxford University Press (OUP) Oxford University Press |
| spellingShingle | Liu, Ge Zeng, Haoyang Mueller, Jonas Weylin Carter, Brandon M. Wang, Ziheng Schilz, Jonas Horny, Geraldine Birnbaum, Michael E Ewert, Stefan Gifford, David K Antibody complementarity determining region design using high-capacity machine learning |
| title | Antibody complementarity determining region design using high-capacity machine learning |
| title_full | Antibody complementarity determining region design using high-capacity machine learning |
| title_fullStr | Antibody complementarity determining region design using high-capacity machine learning |
| title_full_unstemmed | Antibody complementarity determining region design using high-capacity machine learning |
| title_short | Antibody complementarity determining region design using high-capacity machine learning |
| title_sort | antibody complementarity determining region design using high capacity machine learning |
| url | https://hdl.handle.net/1721.1/129332 |
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