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<!DOCTYPE html> <html lang="en" dir="ltr" xmlns="http://www.w3.org/1999/xhtml" xmlns:mml="http://www.w3.org/1998/Math/MathML"> <head prefix="og: http://ogp.me/ns# article: http://ogp.me/ns/article# book: http://ogp.me/ns/book#" >  <link rel="dns-prefetch" href="//d33xdlntwy0kbs.cloudfront.net" /> <link rel="dns-prefetch" href="//www.google.com" /> <link rel="dns-prefetch" href="//scholar.google.com" /> <link rel="dns-prefetch" href="//www.googletagmanager.com" /> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="shortcut icon" href="https://www.biorxiv.org/sites/default/files/images/favicon.ico" type="image/vnd.microsoft.icon" /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <link rel="alternate" type="application/pdf" title="Full Text (PDF)" href="/content/10.1101/2023.09.25.559190v1.full.pdf" /> <link rel="alternate" type="text/plain" title="Full Text (Plain)" href="/content/10.1101/2023.09.25.559190v1.full.txt" /> <meta name="article_thumbnail" content="https://www.biorxiv.org/content/biorxiv/early/2023/09/26/2023.09.25.559190/embed/graphic-7.gif" /> <meta name="type" content="article" /> <meta name="category" content="article" /> <meta name="HW.identifier" content="/biorxiv/early/2023/09/26/2023.09.25.559190.atom" /> <meta name="HW.pisa" content="biorxiv;2023.09.25.559190v1" /> <meta name="DC.Format" content="text/html" /> <meta name="DC.Language" content="en" /> <meta name="DC.Title" content="Generative Diffusion Models for Antibody Design, Docking, and Optimization" /> <meta name="DC.Identifier" content="10.1101/2023.09.25.559190" /> <meta name="DC.Date" content="2023-09-26" /> <meta name="DC.Publisher" content="Cold Spring Harbor Laboratory" /> <meta name="DC.Rights" content="© 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/" /> <meta name="DC.AccessRights" content="restricted" /> <meta name="DC.Description" content="In recent years, optimizing antibody binding affinity for biomedical applications has become increasingly important. However, traditional wet-experiment-based approaches are time-consuming and inefficient. To address this issue, we propose a diffusion model-based antibody optimization pipeline to improve binding affinity. Our approach involves two key models: AbDesign for designing antibody sequences and structures, and AbDock, a paratope-epitope docking model, used for screening designed CDRs. On an independent test set, our AbDesign demonstrates the exceptional performance of an RMSD of 2.56Å in structure design and an amino acid recovery of 36.47% in sequence design. In a paratope-epitope docking test set, our AbDock achieves a state-of-the-art performance of DockQ 0.44, irms 2.71Å, fnat 0.40, and Lrms 6.29Å. The effectiveness of the optimization pipeline is further experimentally validated by optimizing a flaviviruse antibody 1G5.3, resulting in a broad-spectrum antibody that demonstrates improved binding to 6 out of the nine tested flaviviruses. This research offers a general-purpose methodology to enhance antibody functionality without training on data from specific antigens. ### Competing Interest Statement The authors have declared no competing interest." /> <meta name="DC.Contributor" content="Zhangzhi Peng" /> <meta name="DC.Contributor" content="Chenchen Han" /> <meta name="DC.Contributor" content="Xiaohan Wang" /> <meta name="DC.Contributor" content="Dapeng Li" /> <meta name="DC.Contributor" content="Fajie Yuan" /> <meta name="article:published_time" content="2023-09-26" /> <meta name="article:section" content="New Results" /> <meta name="citation_title" content="Generative Diffusion Models for Antibody Design, Docking, and Optimization" /> <meta name="citation_abstract" lang="en" content="<h3>Abstract</h3> <p>In recent years, optimizing antibody binding affinity for biomedical applications has become increasingly important. However, traditional wet-experiment-based approaches are time-consuming and inefficient. To address this issue, we propose a diffusion model-based antibody optimization pipeline to improve binding affinity. Our approach involves two key models: AbDesign for designing antibody sequences and structures, and AbDock, a paratope-epitope docking model, used for screening designed CDRs. On an independent test set, our AbDesign demonstrates the exceptional performance of an RMSD of 2.56Å in structure design and an amino acid recovery of 36.47% in sequence design. In a paratope-epitope docking test set, our AbDock achieves a state-of-the-art performance of DockQ 0.44, irms 2.71Å, fnat 0.40, and Lrms 6.29Å. The effectiveness of the optimization pipeline is further experimentally validated by optimizing a flaviviruse antibody 1G5.3, resulting in a broad-spectrum antibody that demonstrates improved binding to 6 out of the nine tested flaviviruses. This research offers a general-purpose methodology to enhance antibody functionality without training on data from specific antigens.</p>" /> <meta name="citation_journal_title" content="bioRxiv" /> <meta name="citation_publisher" content="Cold Spring Harbor Laboratory" /> <meta name="citation_publication_date" content="2023/01/01" /> <meta name="citation_mjid" content="biorxiv;2023.09.25.559190v1" /> <meta name="citation_id" content="2023.09.25.559190v1" /> <meta name="citation_public_url" content="https://www.biorxiv.org/content/10.1101/2023.09.25.559190v1" /> <meta name="citation_abstract_html_url" content="https://www.biorxiv.org/content/10.1101/2023.09.25.559190v1.abstract" /> <meta name="citation_full_html_url" content="https://www.biorxiv.org/content/10.1101/2023.09.25.559190v1.full" /> <meta name="citation_pdf_url" content="https://www.biorxiv.org/content/biorxiv/early/2023/09/26/2023.09.25.559190.full.pdf" /> <meta name="citation_doi" content="10.1101/2023.09.25.559190" /> <meta name="citation_num_pages" content="23" /> <meta name="citation_article_type" content="Article" /> <meta name="citation_section" content="New Results" /> <meta name="citation_firstpage" content="2023.09.25.559190" /> <meta name="citation_author" content="Zhangzhi Peng" /> <meta name="citation_author_institution" content="Electrical Engineering & Computer Science, University of Missouri" /> <meta name="citation_author" content="Chenchen Han" /> <meta name="citation_author_institution" content="School of Life Engineering, Westlake University" /> <meta name="citation_author" content="Xiaohan Wang" /> <meta name="citation_author_institution" content="Center for Infectious Disease Research, Westlake University" /> <meta name="citation_author_institution" content="School of Life Sciences, Westlake University" /> <meta name="citation_author" content="Dapeng Li" /> <meta name="citation_author_institution" content="Center for Infectious Disease Research, Westlake University" /> <meta name="citation_author_institution" content="School of Life Sciences, Westlake University" /> <meta name="citation_author_email" content="lidapeng@westlake.edu.cn" /> <meta name="citation_author_email" content="yuanfajie@westlake.edu.cn" /> <meta name="citation_author" content="Fajie Yuan" /> <meta name="citation_author_institution" content="School of Life Engineering, Westlake University" /> <meta name="citation_author_email" content="lidapeng@westlake.edu.cn" /> <meta name="citation_author_email" content="yuanfajie@westlake.edu.cn" /> <meta name="citation_author_orcid" content="http://orcid.org/0000-0001-8452-9929" /> <meta name="citation_reference" content="Gram, H., Marconi, L.A., Barbas, C.F., Collet, T.A., Lerner, R.A., Kang, A.S.: In vitro selection and affinity maturation of antibodies from a naive combinatorial immunoglobulin library. 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Proteins 78, 1950–1958 (2010)" /> <meta name="twitter:title" content="Generative Diffusion Models for Antibody Design, Docking, and Optimization" /> <meta name="twitter:site" content="@biorxivpreprint" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:image" content="https://www.biorxiv.org/sites/default/files/images/biorxiv_logo_homepage7-5-small.png" /> <meta name="twitter:description" content="In recent years, optimizing antibody binding affinity for biomedical applications has become increasingly important. However, traditional wet-experiment-based approaches are time-consuming and inefficient. To address this issue, we propose a diffusion model-based antibody optimization pipeline to improve binding affinity. Our approach involves two key models: AbDesign for designing antibody sequences and structures, and AbDock, a paratope-epitope docking model, used for screening designed CDRs. On an independent test set, our AbDesign demonstrates the exceptional performance of an RMSD of 2.56Å in structure design and an amino acid recovery of 36.47% in sequence design. In a paratope-epitope docking test set, our AbDock achieves a state-of-the-art performance of DockQ 0.44, irms 2.71Å, fnat 0.40, and Lrms 6.29Å. The effectiveness of the optimization pipeline is further experimentally validated by optimizing a flaviviruse antibody 1G5.3, resulting in a broad-spectrum antibody that demonstrates improved binding to 6 out of the nine tested flaviviruses. This research offers a general-purpose methodology to enhance antibody functionality without training on data from specific antigens. ### Competing Interest Statement The authors have declared no competing interest." /> <meta name="og-title" property="og:title" content="Generative Diffusion Models for Antibody Design, Docking, and Optimization" /> <meta name="og-url" property="og:url" content="https://www.biorxiv.org/content/10.1101/2023.09.25.559190v1" /> <meta name="og-site-name" property="og:site_name" content="bioRxiv" /> <meta name="og-description" property="og:description" content="In recent years, optimizing antibody binding affinity for biomedical applications has become increasingly important. However, traditional wet-experiment-based approaches are time-consuming and inefficient. To address this issue, we propose a diffusion model-based antibody optimization pipeline to improve binding affinity. Our approach involves two key models: AbDesign for designing antibody sequences and structures, and AbDock, a paratope-epitope docking model, used for screening designed CDRs. On an independent test set, our AbDesign demonstrates the exceptional performance of an RMSD of 2.56Å in structure design and an amino acid recovery of 36.47% in sequence design. In a paratope-epitope docking test set, our AbDock achieves a state-of-the-art performance of DockQ 0.44, irms 2.71Å, fnat 0.40, and Lrms 6.29Å. The effectiveness of the optimization pipeline is further experimentally validated by optimizing a flaviviruse antibody 1G5.3, resulting in a broad-spectrum antibody that demonstrates improved binding to 6 out of the nine tested flaviviruses. This research offers a general-purpose methodology to enhance antibody functionality without training on data from specific antigens. ### Competing Interest Statement The authors have declared no competing interest." /> <meta name="og-type" property="og:type" content="article" /> <meta name="og-image" property="og:image" content="https://www.biorxiv.org/sites/default/files/images/biorxiv_logo_homepage7-5-small.png" /> <meta name="citation_date" content="2023-09-26" /> <link rel="alternate" type="application/vnd.ms-powerpoint" title="Powerpoint" href="/content/10.1101/2023.09.25.559190v1.ppt" /> <meta name="description" content="bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution" /> <meta name="generator" content="Drupal 7 (http://drupal.org)" /> <link rel="canonical" href="https://www.biorxiv.org/content/10.1101/2023.09.25.559190v1" /> <link rel="shortlink" href="https://www.biorxiv.org/node/3387487" /> 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class="highwire-article-citation highwire-citation-type-highwire-article node3387487" data-node-nid="3387487" id="node-3387487--21494880747" data-pisa="biorxiv;2023.09.25.559190v1" data-pisa-master="biorxiv;2023.09.25.559190" data-apath="/biorxiv/early/2023/09/26/2023.09.25.559190.atom" data-hw-author-tooltip-instance="highwire_author_tooltip"><div class="highwire-cite highwire-cite-highwire-article highwire-citation-biorxiv-article-top clearfix has-author-tooltip" > <span class="biorxiv-article-type"> New Results </span> <h1 class="highwire-cite-title" id="page-title">Generative Diffusion Models for Antibody Design, Docking, and Optimization</h1> <div class="highwire-cite-authors" ><span class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">Zhangzhi</span> <span class="nlm-surname">Peng</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Chenchen</span> <span class="nlm-surname">Han</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Xiaohan</span> <span class="nlm-surname">Wang</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Dapeng</span> <span class="nlm-surname">Li</span></span>, <span class="highwire-citation-author hw-author-orcid-logo-wrapper" data-delta="4"><a href="http://orcid.org/0000-0001-8452-9929" target="_blank" class="hw-author-orcid-logo link-icon-only link-icon"><span class="hw-icon-orcid hw-icon-color-orcid"></span> <span class="title element-invisible">View ORCID Profile</span></a><span class="nlm-given-names">Fajie</span> <span class="nlm-surname">Yuan</span></span></span></div> <div class="highwire-cite-metadata" ><span class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">doi:</span> https://doi.org/10.1101/2023.09.25.559190 </span></div> </div> <div id="hw-article-author-popups-node-3387487--21494880747" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Zhangzhi Peng </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Electrical Engineering & Computer Science, University of Missouri</span>, Columbia, 65211, MO, <span class='nlm-country'>USA</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Zhangzhi%2BPeng%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Peng%20Z&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AZhangzhi%2BPeng%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Chenchen Han </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>2</span><span class='nlm-institution'>School of Life Engineering, Westlake University</span>, Hangzhou, 310024, Zhejiang, <span class='nlm-country'>China</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Chenchen%2BHan%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Han%20C&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AChenchen%2BHan%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">Xiaohan Wang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>3</span><span class='nlm-institution'>Center for Infectious Disease Research, Westlake University</span>, Hangzhou, 310024, Zhejiang, <span class='nlm-country'>China</span></div><div class='author-affiliation'><span class='nlm-sup'>4</span><span class='nlm-institution'>School of Life Sciences, Westlake University</span>, Hangzhou, 310024, Zhejiang, <span class='nlm-country'>China</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Xiaohan%2BWang%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Wang%20X&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AXiaohan%2BWang%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">Dapeng Li </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>3</span><span class='nlm-institution'>Center for Infectious Disease Research, Westlake University</span>, Hangzhou, 310024, Zhejiang, <span class='nlm-country'>China</span></div><div class='author-affiliation'><span class='nlm-sup'>4</span><span class='nlm-institution'>School of Life Sciences, Westlake University</span>, Hangzhou, 310024, Zhejiang, <span class='nlm-country'>China</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Dapeng%2BLi%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Li%20D&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3ADapeng%2BLi%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-corresp-email-link last"><span>For correspondence: <a href="mailto:lidapeng@westlake.edu.cn" class="" data-icon-position="" data-hide-link-title="0">lidapeng@westlake.edu.cn</a> <a href="mailto:yuanfajie@westlake.edu.cn" class="" data-icon-position="" data-hide-link-title="0">yuanfajie@westlake.edu.cn</a></span></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">Fajie Yuan </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>2</span><span class='nlm-institution'>School of Life Engineering, Westlake University</span>, Hangzhou, 310024, Zhejiang, <span class='nlm-country'>China</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Fajie%2BYuan%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Yuan%20F&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AFajie%2BYuan%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link"><a href="http://orcid.org/0000-0001-8452-9929" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Fajie Yuan</a></li><li class="author-corresp-email-link last"><span>For correspondence: <a href="mailto:lidapeng@westlake.edu.cn" class="" data-icon-position="" data-hide-link-title="0">lidapeng@westlake.edu.cn</a> <a href="mailto:yuanfajie@westlake.edu.cn" class="" data-icon-position="" data-hide-link-title="0">yuanfajie@westlake.edu.cn</a></span></li></ul></div></div></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-highwire-panel-tabs pane-panels-ajax-tab-tabs" > <div class="pane-content"> <div class="item-list"><ul class="tabs inline panels-ajax-tab"><li class="first"><a href="/content/10.1101/2023.09.25.559190v1" class="panels-ajax-tab-tab" data-panel-name="biorxiv_tab_art" data-target-id="highwire_article_tabs" data-entity-context="node:3387487" data-trigger="" data-url-enabled="1">Abstract</a><a href="/panels_ajax_tab/biorxiv_tab_art/node:3387487/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/2023.09.25.559190v1.full-text" class="panels-ajax-tab-tab" data-panel-name="article_tab_full_text" data-target-id="highwire_article_tabs" 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However, traditional wet-experiment-based approaches are time-consuming and inefficient. To address this issue, we propose a diffusion model-based antibody optimization pipeline to improve binding affinity. Our approach involves two key models: AbDesign for designing antibody sequences and structures, and AbDock, a paratope-epitope docking model, used for screening designed CDRs. On an independent test set, our AbDesign demonstrates the exceptional performance of an RMSD of 2.56Å in structure design and an amino acid recovery of 36.47% in sequence design. In a paratope-epitope docking test set, our AbDock achieves a state-of-the-art performance of DockQ 0.44, irms 2.71Å, fnat 0.40, and Lrms 6.29Å. The effectiveness of the optimization pipeline is further experimentally validated by optimizing a flaviviruse antibody 1G5.3, resulting in a broad-spectrum antibody that demonstrates improved binding to 6 out of the nine tested flaviviruses. This research offers a general-purpose methodology to enhance antibody functionality without training on data from specific antigens.</p></div><h3>Competing Interest Statement</h3><p id="p-5">The authors have declared no competing interest.</p><div class="section fn-group" id="fn-group-1"><h2>Footnotes</h2><ul><li class="fn-others" id="fn-1"><p id="p-1"><a class="rev-xref" href="#xref-fn-1-1">↵</a><span class="fn-label">†</span> Key experiments were conducted at Westlake Univeristy.</p></li></ul></div><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-biorxiv-copyright" > <div class="pane-content"> <div class="field field-name-field-highwire-copyright field-type-text field-label-inline clearfix"><div class="field-label">Copyright </div><div class="field-items"><div class="field-item even">The copyright holder for this preprint is the author/funder, who 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