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Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space
Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of ma...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9249733/ https://www.ncbi.nlm.nih.gov/pubmed/35778381 http://dx.doi.org/10.1038/s41467-022-31457-3 |
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author | Makowski, Emily K. Kinnunen, Patrick C. Huang, Jie Wu, Lina Smith, Matthew D. Wang, Tiexin Desai, Alec A. Streu, Craig N. Zhang, Yulei Zupancic, Jennifer M. Schardt, John S. Linderman, Jennifer J. Tessier, Peter M. |
author_facet | Makowski, Emily K. Kinnunen, Patrick C. Huang, Jie Wu, Lina Smith, Matthew D. Wang, Tiexin Desai, Alec A. Streu, Craig N. Zhang, Yulei Zupancic, Jennifer M. Schardt, John S. Linderman, Jennifer J. Tessier, Peter M. |
author_sort | Makowski, Emily K. |
collection | PubMed |
description | Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies. |
format | Online Article Text |
id | pubmed-9249733 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92497332022-07-03 Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space Makowski, Emily K. Kinnunen, Patrick C. Huang, Jie Wu, Lina Smith, Matthew D. Wang, Tiexin Desai, Alec A. Streu, Craig N. Zhang, Yulei Zupancic, Jennifer M. Schardt, John S. Linderman, Jennifer J. Tessier, Peter M. Nat Commun Article Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies. Nature Publishing Group UK 2022-07-01 /pmc/articles/PMC9249733/ /pubmed/35778381 http://dx.doi.org/10.1038/s41467-022-31457-3 Text en © The Author(s) 2022 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Makowski, Emily K. Kinnunen, Patrick C. Huang, Jie Wu, Lina Smith, Matthew D. Wang, Tiexin Desai, Alec A. Streu, Craig N. Zhang, Yulei Zupancic, Jennifer M. Schardt, John S. Linderman, Jennifer J. Tessier, Peter M. Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space |
title | Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space |
title_full | Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space |
title_fullStr | Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space |
title_full_unstemmed | Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space |
title_short | Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space |
title_sort | co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9249733/ https://www.ncbi.nlm.nih.gov/pubmed/35778381 http://dx.doi.org/10.1038/s41467-022-31457-3 |
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