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Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning
The application of machine learning (ML) models to optimize antibody affinity to an antigen is gaining prominence. Unfortunately, the small and biased nature of the publicly available antibody-antigen interaction datasets makes it challenging to build an ML model that can accurately predict binding...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636138/ https://www.ncbi.nlm.nih.gov/pubmed/37945793 http://dx.doi.org/10.1038/s42004-023-01037-7 |
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author | Clark, Thomas Subramanian, Vidya Jayaraman, Akila Fitzpatrick, Emmett Gopal, Ranjani Pentakota, Niharika Rurak, Troy Anand, Shweta Viglione, Alexander Raman, Rahul Tharakaraman, Kannan Sasisekharan, Ram |
author_facet | Clark, Thomas Subramanian, Vidya Jayaraman, Akila Fitzpatrick, Emmett Gopal, Ranjani Pentakota, Niharika Rurak, Troy Anand, Shweta Viglione, Alexander Raman, Rahul Tharakaraman, Kannan Sasisekharan, Ram |
author_sort | Clark, Thomas |
collection | PubMed |
description | The application of machine learning (ML) models to optimize antibody affinity to an antigen is gaining prominence. Unfortunately, the small and biased nature of the publicly available antibody-antigen interaction datasets makes it challenging to build an ML model that can accurately predict binding affinity changes due to mutations (ΔΔG). Recognizing these inherent limitations, we reformulated the problem to ask whether an ML model capable of classifying deleterious vs non-deleterious mutations can guide antibody affinity maturation in a practical setting. To test this hypothesis, we developed a Random Forest classifier (Antibody Random Forest Classifier or AbRFC) with expert-guided features and integrated it into a computational-experimental workflow. AbRFC effectively predicted non-deleterious mutations on an in-house validation dataset that is free of biases seen in the publicly available training datasets. Furthermore, experimental screening of a limited number of predictions from the model (<10^2 designs) identified affinity-enhancing mutations in two unrelated SARS-CoV-2 antibodies, resulting in constructs with up to 1000-fold increased binding to the SARS-COV-2 RBD. Our findings indicate that accurate prediction and screening of non-deleterious mutations using machine learning offers a powerful approach to improving antibody affinity. |
format | Online Article Text |
id | pubmed-10636138 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106361382023-11-11 Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning Clark, Thomas Subramanian, Vidya Jayaraman, Akila Fitzpatrick, Emmett Gopal, Ranjani Pentakota, Niharika Rurak, Troy Anand, Shweta Viglione, Alexander Raman, Rahul Tharakaraman, Kannan Sasisekharan, Ram Commun Chem Article The application of machine learning (ML) models to optimize antibody affinity to an antigen is gaining prominence. Unfortunately, the small and biased nature of the publicly available antibody-antigen interaction datasets makes it challenging to build an ML model that can accurately predict binding affinity changes due to mutations (ΔΔG). Recognizing these inherent limitations, we reformulated the problem to ask whether an ML model capable of classifying deleterious vs non-deleterious mutations can guide antibody affinity maturation in a practical setting. To test this hypothesis, we developed a Random Forest classifier (Antibody Random Forest Classifier or AbRFC) with expert-guided features and integrated it into a computational-experimental workflow. AbRFC effectively predicted non-deleterious mutations on an in-house validation dataset that is free of biases seen in the publicly available training datasets. Furthermore, experimental screening of a limited number of predictions from the model (<10^2 designs) identified affinity-enhancing mutations in two unrelated SARS-CoV-2 antibodies, resulting in constructs with up to 1000-fold increased binding to the SARS-COV-2 RBD. Our findings indicate that accurate prediction and screening of non-deleterious mutations using machine learning offers a powerful approach to improving antibody affinity. Nature Publishing Group UK 2023-11-09 /pmc/articles/PMC10636138/ /pubmed/37945793 http://dx.doi.org/10.1038/s42004-023-01037-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Clark, Thomas Subramanian, Vidya Jayaraman, Akila Fitzpatrick, Emmett Gopal, Ranjani Pentakota, Niharika Rurak, Troy Anand, Shweta Viglione, Alexander Raman, Rahul Tharakaraman, Kannan Sasisekharan, Ram Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning |
title | Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning |
title_full | Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning |
title_fullStr | Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning |
title_full_unstemmed | Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning |
title_short | Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning |
title_sort | enhancing antibody affinity through experimental sampling of non-deleterious cdr mutations predicted by machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636138/ https://www.ncbi.nlm.nih.gov/pubmed/37945793 http://dx.doi.org/10.1038/s42004-023-01037-7 |
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