Monte Carlo Thompson sampling-guided design for antibody engineering
Antibodies are one of the predominant treatment modalities for various diseases. To improve the characteristics of a lead antibody, such as antigen-binding affinity and stability, we conducted comprehensive substitutions and exhaustively explored their sequence space. However, it is practically unfe...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | mAbs |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19420862.2023.2244214 |
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| _version_ | 1849387595896717312 |
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| author | Taro Kakuzaki Hikaru Koga Shuuki Takizawa Shoichi Metsugi Hirotake Shiraiwa Zenjiro Sampei Kenji Yoshida Hiroyuki Tsunoda Reiji Teramoto |
| author_facet | Taro Kakuzaki Hikaru Koga Shuuki Takizawa Shoichi Metsugi Hirotake Shiraiwa Zenjiro Sampei Kenji Yoshida Hiroyuki Tsunoda Reiji Teramoto |
| author_sort | Taro Kakuzaki |
| collection | DOAJ |
| description | Antibodies are one of the predominant treatment modalities for various diseases. To improve the characteristics of a lead antibody, such as antigen-binding affinity and stability, we conducted comprehensive substitutions and exhaustively explored their sequence space. However, it is practically unfeasible to evaluate all possible combinations of mutations owing to combinatorial explosion when multiple amino acid residues are incorporated. It was recently reported that a machine-learning guided protein engineering approach such as Thompson sampling (TS) has been used to efficiently explore sequence space in the framework of Bayesian optimization. For TS, over-exploration occurs when the initial data are biasedly distributed in the vicinity of the lead antibody. We handle a large-scale virtual library that includes numerous mutations. When the number of experiments is limited, this over-exploration causes a serious issue. Thus, we conducted Monte Carlo Thompson sampling (MTS) to balance the exploration-exploitation trade-off by defining the posterior distribution via the Monte Carlo method and compared its performance with TS in antibody engineering. Our results demonstrated that MTS largely outperforms TS in discovering desirable candidates at an earlier round when over-exploration occurs on TS. Thus, the MTS method is a powerful technique for efficiently discovering antibodies with desired characteristics when the number of rounds is limited. |
| format | Article |
| id | doaj-art-464d1ce6486045f391b77c27fa515809 |
| institution | Kabale University |
| issn | 1942-0862 1942-0870 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | mAbs |
| spelling | doaj-art-464d1ce6486045f391b77c27fa5158092025-08-20T03:51:35ZengTaylor & Francis GroupmAbs1942-08621942-08702023-12-0115110.1080/19420862.2023.2244214Monte Carlo Thompson sampling-guided design for antibody engineeringTaro Kakuzaki0Hikaru Koga1Shuuki Takizawa2Shoichi Metsugi3Hirotake Shiraiwa4Zenjiro Sampei5Kenji Yoshida6Hiroyuki Tsunoda7Reiji Teramoto8Research Division, Chugai Pharmaceutical Co., Ltd., Yokohama, JapanResearch Division, Chugai Pharmaceutical Co., Ltd., Yokohama, JapanResearch Division, Chugai Pharmaceutical Co., Ltd., Yokohama, JapanResearch Division, Chugai Pharmaceutical Co., Ltd., Yokohama, JapanResearch Division, Chugai Pharmaceutical Co., Ltd., Yokohama, JapanResearch Division, Chugai Pharmaceutical Co., Ltd., Yokohama, JapanResearch Division, Chugai Pharmaceutical Co., Ltd., Yokohama, JapanResearch Division, Chugai Pharmaceutical Co., Ltd., Yokohama, JapanResearch Division, Chugai Pharmaceutical Co., Ltd., Yokohama, JapanAntibodies are one of the predominant treatment modalities for various diseases. To improve the characteristics of a lead antibody, such as antigen-binding affinity and stability, we conducted comprehensive substitutions and exhaustively explored their sequence space. However, it is practically unfeasible to evaluate all possible combinations of mutations owing to combinatorial explosion when multiple amino acid residues are incorporated. It was recently reported that a machine-learning guided protein engineering approach such as Thompson sampling (TS) has been used to efficiently explore sequence space in the framework of Bayesian optimization. For TS, over-exploration occurs when the initial data are biasedly distributed in the vicinity of the lead antibody. We handle a large-scale virtual library that includes numerous mutations. When the number of experiments is limited, this over-exploration causes a serious issue. Thus, we conducted Monte Carlo Thompson sampling (MTS) to balance the exploration-exploitation trade-off by defining the posterior distribution via the Monte Carlo method and compared its performance with TS in antibody engineering. Our results demonstrated that MTS largely outperforms TS in discovering desirable candidates at an earlier round when over-exploration occurs on TS. Thus, the MTS method is a powerful technique for efficiently discovering antibodies with desired characteristics when the number of rounds is limited.https://www.tandfonline.com/doi/10.1080/19420862.2023.2244214Antibody engineeringBayesian optimizationmachine learningMonte Carlo methodprotein engineeringThompson sampling |
| spellingShingle | Taro Kakuzaki Hikaru Koga Shuuki Takizawa Shoichi Metsugi Hirotake Shiraiwa Zenjiro Sampei Kenji Yoshida Hiroyuki Tsunoda Reiji Teramoto Monte Carlo Thompson sampling-guided design for antibody engineering mAbs Antibody engineering Bayesian optimization machine learning Monte Carlo method protein engineering Thompson sampling |
| title | Monte Carlo Thompson sampling-guided design for antibody engineering |
| title_full | Monte Carlo Thompson sampling-guided design for antibody engineering |
| title_fullStr | Monte Carlo Thompson sampling-guided design for antibody engineering |
| title_full_unstemmed | Monte Carlo Thompson sampling-guided design for antibody engineering |
| title_short | Monte Carlo Thompson sampling-guided design for antibody engineering |
| title_sort | monte carlo thompson sampling guided design for antibody engineering |
| topic | Antibody engineering Bayesian optimization machine learning Monte Carlo method protein engineering Thompson sampling |
| url | https://www.tandfonline.com/doi/10.1080/19420862.2023.2244214 |
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