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This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/" /> <meta name="DC.AccessRights" content="restricted" /> <meta name="DC.Description" content="The versatile binding properties of antibodies have made them an extremely important class of biotherapeutics. However, therapeutic antibody development is a complex, expensive and time-consuming task, with the final antibody needing to not only have strong and specific binding, but also be minimally impacted by any developability issues. The success of transformer-based language models in protein sequence space and the availability of vast amounts of antibody sequences, has led to the development of many antibody-specific language models to help guide antibody discovery and design. Antibody diversity primarily arises from V(D)J recombination, mutations within the CDRs, and/or from a small number of mutations away from the germline outside the CDRs. Consequently, a significant portion of the variable domain of all natural antibody sequences remains germline. This affects the pre-training of antibody-specific language models, where this facet of the sequence data introduces a prevailing bias towards germline residues. This poses a challenge, as mutations away from the germline are often vital for generating specific and potent binding to a target, meaning that language models need be able to suggest key mutations away from germline. In this study, we explore the implications of the germline bias, examining its impact on both general-protein and antibody-specific language models. We develop and train a series of new antibody-specific language models optimised for predicting non-germline residues. We then compare our final model, AbLang-2, with current models and show how it suggests a diverse set of valid mutations with high cumulative probability. AbLang-2 is trained on both unpaired and paired data, and is freely available (<https://github.com/oxpig/AbLang2.git>). ### Competing Interest Statement Author IM is employed by GlaxoSmithKline plc. All authors declare no other competing interests." /> <meta name="DC.Contributor" content="Tobias H. Olsen" /> <meta name="DC.Contributor" content="Iain H. Moal" /> <meta name="DC.Contributor" content="Charlotte M. Deane" /> <meta name="article:published_time" content="2024-02-07" /> <meta name="article:section" content="New Results" /> <meta name="citation_title" content="Addressing the antibody germline bias and its effect on language models for improved antibody design" /> <meta name="citation_abstract" lang="en" content="<h3>Abstract</h3> <p>The versatile binding properties of antibodies have made them an extremely important class of biotherapeutics. However, therapeutic antibody development is a complex, expensive and time-consuming task, with the final antibody needing to not only have strong and specific binding, but also be minimally impacted by any developability issues. The success of transformer-based language models in protein sequence space and the availability of vast amounts of antibody sequences, has led to the development of many antibody-specific language models to help guide antibody discovery and design. Antibody diversity primarily arises from V(D)J recombination, mutations within the CDRs, and/or from a small number of mutations away from the germline outside the CDRs. Consequently, a significant portion of the variable domain of all natural antibody sequences remains germline. This affects the pre-training of antibody-specific language models, where this facet of the sequence data introduces a prevailing bias towards germline residues. This poses a challenge, as mutations away from the germline are often vital for generating specific and potent binding to a target, meaning that language models need be able to suggest key mutations away from germline.</p><p>In this study, we explore the implications of the germline bias, examining its impact on both general-protein and antibody-specific language models. We develop and train a series of new antibody-specific language models optimised for predicting non-germline residues. We then compare our final model, AbLang-2, with current models and show how it suggests a diverse set of valid mutations with high cumulative probability. AbLang-2 is trained on both unpaired and paired data, and is freely available (https://github.com/oxpig/AbLang2.git).</p>" /> <meta name="citation_journal_title" content="bioRxiv" /> <meta name="citation_publisher" content="Cold Spring Harbor Laboratory" /> <meta name="citation_publication_date" content="2024/01/01" /> <meta name="citation_mjid" content="biorxiv;2024.02.02.578678v1" /> <meta name="citation_id" content="2024.02.02.578678v1" /> <meta name="citation_public_url" content="https://www.biorxiv.org/content/10.1101/2024.02.02.578678v1" /> <meta name="citation_abstract_html_url" content="https://www.biorxiv.org/content/10.1101/2024.02.02.578678v1.abstract" /> <meta name="citation_full_html_url" content="https://www.biorxiv.org/content/10.1101/2024.02.02.578678v1.full" /> <meta name="citation_pdf_url" content="https://www.biorxiv.org/content/biorxiv/early/2024/02/07/2024.02.02.578678.full.pdf" /> <meta name="citation_doi" content="10.1101/2024.02.02.578678" /> <meta name="citation_num_pages" content="14" /> <meta name="citation_article_type" content="Article" /> <meta name="citation_section" content="New Results" /> <meta name="citation_firstpage" content="2024.02.02.578678" /> <meta name="citation_author" content="Tobias H. 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CoRR, abs/2109.0, 2021." /> <meta name="twitter:title" content="Addressing the antibody germline bias and its effect on language models for improved antibody design" /> <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="The versatile binding properties of antibodies have made them an extremely important class of biotherapeutics. However, therapeutic antibody development is a complex, expensive and time-consuming task, with the final antibody needing to not only have strong and specific binding, but also be minimally impacted by any developability issues. The success of transformer-based language models in protein sequence space and the availability of vast amounts of antibody sequences, has led to the development of many antibody-specific language models to help guide antibody discovery and design. Antibody diversity primarily arises from V(D)J recombination, mutations within the CDRs, and/or from a small number of mutations away from the germline outside the CDRs. Consequently, a significant portion of the variable domain of all natural antibody sequences remains germline. This affects the pre-training of antibody-specific language models, where this facet of the sequence data introduces a prevailing bias towards germline residues. This poses a challenge, as mutations away from the germline are often vital for generating specific and potent binding to a target, meaning that language models need be able to suggest key mutations away from germline. In this study, we explore the implications of the germline bias, examining its impact on both general-protein and antibody-specific language models. We develop and train a series of new antibody-specific language models optimised for predicting non-germline residues. We then compare our final model, AbLang-2, with current models and show how it suggests a diverse set of valid mutations with high cumulative probability. AbLang-2 is trained on both unpaired and paired data, and is freely available (<https://github.com/oxpig/AbLang2.git>). ### Competing Interest Statement Author IM is employed by GlaxoSmithKline plc. All authors declare no other competing interests." /> <meta name="og-title" property="og:title" content="Addressing the antibody germline bias and its effect on language models for improved antibody design" /> <meta name="og-url" property="og:url" content="https://www.biorxiv.org/content/10.1101/2024.02.02.578678v1" /> <meta name="og-site-name" property="og:site_name" content="bioRxiv" /> <meta name="og-description" property="og:description" content="The versatile binding properties of antibodies have made them an extremely important class of biotherapeutics. However, therapeutic antibody development is a complex, expensive and time-consuming task, with the final antibody needing to not only have strong and specific binding, but also be minimally impacted by any developability issues. The success of transformer-based language models in protein sequence space and the availability of vast amounts of antibody sequences, has led to the development of many antibody-specific language models to help guide antibody discovery and design. Antibody diversity primarily arises from V(D)J recombination, mutations within the CDRs, and/or from a small number of mutations away from the germline outside the CDRs. Consequently, a significant portion of the variable domain of all natural antibody sequences remains germline. This affects the pre-training of antibody-specific language models, where this facet of the sequence data introduces a prevailing bias towards germline residues. This poses a challenge, as mutations away from the germline are often vital for generating specific and potent binding to a target, meaning that language models need be able to suggest key mutations away from germline. In this study, we explore the implications of the germline bias, examining its impact on both general-protein and antibody-specific language models. We develop and train a series of new antibody-specific language models optimised for predicting non-germline residues. We then compare our final model, AbLang-2, with current models and show how it suggests a diverse set of valid mutations with high cumulative probability. AbLang-2 is trained on both unpaired and paired data, and is freely available (<https://github.com/oxpig/AbLang2.git>). ### Competing Interest Statement Author IM is employed by GlaxoSmithKline plc. All authors declare no other competing interests." /> <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="2024-02-07" /> <link rel="alternate" type="application/vnd.ms-powerpoint" title="Powerpoint" href="/content/10.1101/2024.02.02.578678v1.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/10.1101/2024.02.02.578678v1" /> <link rel="shortlink" href="https://www.biorxiv.org/node/3641427" /> <title>Addressing the antibody germline bias and its effect on language models for improved antibody design | bioRxiv</title> <link type="text/css" rel="stylesheet" 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<section id="section-content" class="section section-content"> <div id="zone-content" class="zone zone-content clearfix container-30"> <div class="grid-28 suffix-1 prefix-1 region region-content" id="region-content"> <div class="region-inner region-content-inner"> <a id="main-content"></a> <div class="block block-system block-main block-system-main odd block-without-title" id="block-system-main"> <div class="block-inner clearfix"> <div class="content clearfix"> <div class="panel-display panels-960-layout jcore-2col-layout" > <div class="panel-row-wrapper clearfix"> <div class="main-content-wrapper grid-17 suffix-1 alpha"> <div class="panel-panel panel-region-content"> <div class="inside"><div class="panel-pane pane-highwire-article-citation" > <div class="pane-content"> <div class="highwire-article-citation highwire-citation-type-highwire-article node3641427" data-node-nid="3641427" id="node-3641427--21454912402" data-pisa="biorxiv;2024.02.02.578678v1" data-pisa-master="biorxiv;2024.02.02.578678" data-apath="/biorxiv/early/2024/02/07/2024.02.02.578678.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">Addressing the antibody germline bias and its effect on language models for improved antibody design</h1> <div class="highwire-cite-authors" ><span class="highwire-citation-authors"><span class="highwire-citation-author first hw-author-orcid-logo-wrapper" data-delta="0"><a href="http://orcid.org/0000-0002-6348-4650" 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">Tobias H.</span> <span class="nlm-surname">Olsen</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Iain H.</span> <span class="nlm-surname">Moal</span></span>, <span class="highwire-citation-author hw-author-orcid-logo-wrapper" data-delta="2"><a href="http://orcid.org/0000-0003-1388-2252" 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">Charlotte M.</span> <span class="nlm-surname">Deane</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/10.1101/2024.02.02.578678 </span></div> </div> <div id="hw-article-author-popups-node-3641427--21454912402" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Tobias H. Olsen </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'>GSK Medicines Research Centre, GSK</span>, Stevenage SG1 2NY, <span class='nlm-country'>United Kingdom</span></div><div class='author-affiliation'><span class='nlm-sup'>2</span><span class='nlm-institution'>Department of Statistics, University of Oxford</span>, Oxford OX1 3LB, <span class='nlm-country'>United Kingdom</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=Tobias%2BH.%2BOlsen%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=Olsen%20TH&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%3ATobias%2BH.%2BOlsen%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 last"><a href="http://orcid.org/0000-0002-6348-4650" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Tobias H. Olsen</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Iain H. Moal </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'>GSK Medicines Research Centre, GSK</span>, Stevenage SG1 2NY, <span class='nlm-country'>United Kingdom</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=Iain%2BH.%2BMoal%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=Moal%20IH&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%3AIain%2BH.%2BMoal%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">Charlotte M. Deane </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'>Department of Statistics, University of Oxford</span>, Oxford OX1 3LB, <span class='nlm-country'>United Kingdom</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=Charlotte%2BM.%2BDeane%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=Deane%20CM&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%3ACharlotte%2BM.%2BDeane%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/0000-0003-1388-2252" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Charlotte M. Deane</a></li><li class="author-corresp-email-link last"><span>For correspondence: <a href="mailto:deane@stats.ox.ac.uk" class="" data-icon-position="" data-hide-link-title="0">deane@stats.ox.ac.uk</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/10.1101/2024.02.02.578678v1" class="panels-ajax-tab-tab" data-panel-name="biorxiv_tab_art" data-target-id="highwire_article_tabs" data-entity-context="node:3641427" data-trigger="" data-url-enabled="1">Abstract</a><a href="/panels_ajax_tab/biorxiv_tab_art/node:3641427/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/2024.02.02.578678v1.full-text" class="panels-ajax-tab-tab" data-panel-name="article_tab_full_text" data-target-id="highwire_article_tabs" data-entity-context="node:3641427" data-trigger="full-text" data-url-enabled="1">Full Text</a><a href="/panels_ajax_tab/article_tab_full_text/node:3641427/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/2024.02.02.578678v1.article-info" class="panels-ajax-tab-tab" data-panel-name="biorxiv_tab_info" data-target-id="highwire_article_tabs" data-entity-context="node:3641427" data-trigger="article-info" data-url-enabled="1">Info/History</a><a href="/panels_ajax_tab/biorxiv_tab_info/node:3641427/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/2024.02.02.578678v1.article-metrics" class="panels-ajax-tab-tab" data-panel-name="article_tab_metrics" data-target-id="highwire_article_tabs" data-entity-context="node:3641427" data-trigger="article-metrics" data-url-enabled="1">Metrics</a><a href="/panels_ajax_tab/article_tab_metrics/node:3641427/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li class="last"><a href="/content/10.1101/2024.02.02.578678v1.full.pdf+html" class="panels-ajax-tab-tab" data-panel-name="biorxiv_tab_pdf" data-target-id="highwire_article_tabs" data-entity-context="node:3641427" data-trigger="full.pdf+html" data-url-enabled="1"><i class="icon-file-alt"></i> Preview PDF</a><a href="/panels_ajax_tab/biorxiv_tab_pdf/node:3641427/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li></ul></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-highwire-panel-tabs-container" > <div class="pane-content"> <div data-panels-ajax-tab-preloaded="biorxiv_tab_art" id="panels-ajax-tab-container-highwire_article_tabs" class="panels-ajax-tab-container"><div class="panels-ajax-tab-loading" style ="display:none"><img class="loading" src="https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif" alt="Loading" title="Loading" /></div><div class="panels-ajax-tab-wrap-biorxiv_tab_art"><div class="panel-display panel-1col clearfix" > <div class="panel-panel panel-col"> <div><div class="panel-pane pane-highwire-markup" > <div class="pane-content"> <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" data-highwire-cite-ref-tooltip-instance="highwire_reflinks_tooltip" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div class="article abstract-view "><span class="highwire-journal-article-marker-start"></span><div class="section abstract" id="abstract-1"><h2 class="">A<span class="sc">bstract</span></h2><p id="p-2">The versatile binding properties of antibodies have made them an extremely important class of biotherapeutics. However, therapeutic antibody development is a complex, expensive and time-consuming task, with the final antibody needing to not only have strong and specific binding, but also be minimally impacted by any developability issues. The success of transformer-based language models in protein sequence space and the availability of vast amounts of antibody sequences, has led to the development of many antibody-specific language models to help guide antibody discovery and design. Antibody diversity primarily arises from V(D)J recombination, mutations within the CDRs, and/or from a small number of mutations away from the germline outside the CDRs. Consequently, a significant portion of the variable domain of all natural antibody sequences remains germline. This affects the pre-training of antibody-specific language models, where this facet of the sequence data introduces a prevailing bias towards germline residues. This poses a challenge, as mutations away from the germline are often vital for generating specific and potent binding to a target, meaning that language models need be able to suggest key mutations away from germline.</p><p id="p-3">In this study, we explore the implications of the germline bias, examining its impact on both general-protein and antibody-specific language models. We develop and train a series of new antibody-specific language models optimised for predicting non-germline residues. We then compare our final model, AbLang-2, with current models and show how it suggests a diverse set of valid mutations with high cumulative probability. AbLang-2 is trained on both unpaired and paired data, and is freely available (<a href="https://github.com/oxpig/AbLang2.git">https://github.com/oxpig/AbLang2.git</a>).</p></div><h3>Competing Interest Statement</h3><p id="p-4">Author IM is employed by GlaxoSmithKline plc. 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