Khan, Asif;
Cowen-Rivers, Alexander I;
Grosnit, Antoine;
Deik, Derrick-Goh-Xin;
Robert, Philippe A;
Greiff, Victor;
Smorodina, Eva;
... Bou-Ammar, Haitham; + view all
(2023)
Toward real-world automated antibody design with combinatorial Bayesian optimization.
Cell Reports Methods
, 3
(1)
, Article 100374. 10.1016/j.crmeth.2022.100374.
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Abstract
Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.
| Type: | Article |
|---|---|
| Title: | Toward real-world automated antibody design with combinatorial Bayesian optimization |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.crmeth.2022.100374 |
| Publisher version: | https://doi.org/10.1016/j.crmeth.2022.100374 |
| Language: | English |
| Additional information: | Copyright © 2022 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keywords: | Computational antibody design; structural biology; protein engineering; Bayesian optimization; combinatorial Bayesian optimization; Gaussian processes; machine learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10174190 |
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