Machine learning methods for protein-protein binding affinity prediction in protein design
Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immuno...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Bioinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2022.1065703/full |
| _version_ | 1811196810921771008 |
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| author | Zhongliang Guo Rui Yamaguchi Rui Yamaguchi |
| author_facet | Zhongliang Guo Rui Yamaguchi Rui Yamaguchi |
| author_sort | Zhongliang Guo |
| collection | DOAJ |
| description | Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design. |
| first_indexed | 2024-04-12T01:05:19Z |
| format | Article |
| id | doaj.art-2bb00ea4a9c840a2b96bfca0a10f9613 |
| institution | Directory Open Access Journal |
| issn | 2673-7647 |
| language | English |
| last_indexed | 2024-04-12T01:05:19Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj.art-2bb00ea4a9c840a2b96bfca0a10f96132022-12-22T03:54:18ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472022-12-01210.3389/fbinf.2022.10657031065703Machine learning methods for protein-protein binding affinity prediction in protein designZhongliang Guo0Rui Yamaguchi1Rui Yamaguchi2Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Aichi, JapanDivision of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Aichi, JapanDivision of Cancer Informatics, Nagoya University Graduate School of Medicine, Nagoya, Aichi, JapanProtein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design.https://www.frontiersin.org/articles/10.3389/fbinf.2022.1065703/fullmachine learningdeep neural networkprotein-protein interactionbinding affinityprotein design |
| spellingShingle | Zhongliang Guo Rui Yamaguchi Rui Yamaguchi Machine learning methods for protein-protein binding affinity prediction in protein design Frontiers in Bioinformatics machine learning deep neural network protein-protein interaction binding affinity protein design |
| title | Machine learning methods for protein-protein binding affinity prediction in protein design |
| title_full | Machine learning methods for protein-protein binding affinity prediction in protein design |
| title_fullStr | Machine learning methods for protein-protein binding affinity prediction in protein design |
| title_full_unstemmed | Machine learning methods for protein-protein binding affinity prediction in protein design |
| title_short | Machine learning methods for protein-protein binding affinity prediction in protein design |
| title_sort | machine learning methods for protein protein binding affinity prediction in protein design |
| topic | machine learning deep neural network protein-protein interaction binding affinity protein design |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2022.1065703/full |
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