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The RESP AI model accelerates the identification of tight-binding antibodies
High-affinity antibodies are often identified through directed evolution, which may require many iterations of mutagenesis and selection to find an optimal candidate. Deep learning techniques hold the potential to accelerate this process but the existing methods cannot provide the confidence interva...
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/PMC9884274/ https://www.ncbi.nlm.nih.gov/pubmed/36709319 http://dx.doi.org/10.1038/s41467-023-36028-8 |
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author | Parkinson, Jonathan Hard, Ryan Wang, Wei |
author_facet | Parkinson, Jonathan Hard, Ryan Wang, Wei |
author_sort | Parkinson, Jonathan |
collection | PubMed |
description | High-affinity antibodies are often identified through directed evolution, which may require many iterations of mutagenesis and selection to find an optimal candidate. Deep learning techniques hold the potential to accelerate this process but the existing methods cannot provide the confidence interval or uncertainty needed to assess the reliability of the predictions. Here we present a pipeline called RESP for efficient identification of high affinity antibodies. We develop a learned representation trained on over 3 million human B-cell receptor sequences to encode antibody sequences. We then develop a variational Bayesian neural network to perform ordinal regression on a set of the directed evolution sequences binned by off-rate and quantify their likelihood to be tight binders against an antigen. Importantly, this model can assess sequences not present in the directed evolution library and thus greatly expand the search space to uncover the best sequences for experimental evaluation. We demonstrate the power of this pipeline by achieving a 17-fold improvement in the K(D) of the PD-L1 antibody Atezolizumab and this success illustrates the potential of RESP in facilitating general antibody development. |
format | Online Article Text |
id | pubmed-9884274 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98842742023-01-30 The RESP AI model accelerates the identification of tight-binding antibodies Parkinson, Jonathan Hard, Ryan Wang, Wei Nat Commun Article High-affinity antibodies are often identified through directed evolution, which may require many iterations of mutagenesis and selection to find an optimal candidate. Deep learning techniques hold the potential to accelerate this process but the existing methods cannot provide the confidence interval or uncertainty needed to assess the reliability of the predictions. Here we present a pipeline called RESP for efficient identification of high affinity antibodies. We develop a learned representation trained on over 3 million human B-cell receptor sequences to encode antibody sequences. We then develop a variational Bayesian neural network to perform ordinal regression on a set of the directed evolution sequences binned by off-rate and quantify their likelihood to be tight binders against an antigen. Importantly, this model can assess sequences not present in the directed evolution library and thus greatly expand the search space to uncover the best sequences for experimental evaluation. We demonstrate the power of this pipeline by achieving a 17-fold improvement in the K(D) of the PD-L1 antibody Atezolizumab and this success illustrates the potential of RESP in facilitating general antibody development. Nature Publishing Group UK 2023-01-28 /pmc/articles/PMC9884274/ /pubmed/36709319 http://dx.doi.org/10.1038/s41467-023-36028-8 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 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 Parkinson, Jonathan Hard, Ryan Wang, Wei The RESP AI model accelerates the identification of tight-binding antibodies |
title | The RESP AI model accelerates the identification of tight-binding antibodies |
title_full | The RESP AI model accelerates the identification of tight-binding antibodies |
title_fullStr | The RESP AI model accelerates the identification of tight-binding antibodies |
title_full_unstemmed | The RESP AI model accelerates the identification of tight-binding antibodies |
title_short | The RESP AI model accelerates the identification of tight-binding antibodies |
title_sort | resp ai model accelerates the identification of tight-binding antibodies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884274/ https://www.ncbi.nlm.nih.gov/pubmed/36709319 http://dx.doi.org/10.1038/s41467-023-36028-8 |
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