[2310.19513] Inverse folding for antibody sequence design using deep learning

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Building on previous work, we propose a fine-tuned inverse folding model that is specifically optimised for antibody structures and outperforms generic protein models on sequence recovery and structure robustness when applied on antibodies, with notable improvement on the hypervariable CDR-H3 loop. We study the canonical conformations of complementarity-determining regions and find improved encoding of these loops into known clusters. Finally, we consider the applications of our model to drug discovery and binder design and evaluate the quality of proposed sequences using physics-based methods."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="Inverse folding for antibody sequence design using deep learning"/> <meta name="twitter:description" content="We consider the problem of antibody sequence design given 3D structural information. Building on previous work, we propose a fine-tuned inverse folding model that is specifically optimised for..."/> <meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/> <meta name="twitter:image:alt" content="arXiv logo"/> <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script> <script src="/static/browse/0.3.4/js/toggle-labs.js?20241022" type="text/javascript"></script> <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="Inverse folding for antibody sequence design using deep learning" /><meta name="citation_author" content="Dreyer, Fr茅d茅ric A." /><meta name="citation_author" content="Cutting, Daniel" /><meta name="citation_author" content="Schneider, Constantin" /><meta name="citation_author" content="Kenlay, Henry" /><meta name="citation_author" content="Deane, Charlotte M." /><meta name="citation_date" content="2023/10/30" /><meta name="citation_online_date" content="2023/10/30" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2310.19513" /><meta name="citation_arxiv_id" content="2310.19513" /><meta name="citation_abstract" content="We consider the problem of antibody sequence design given 3D structural information. Building on previous work, we propose a fine-tuned inverse folding model that is specifically optimised for antibody structures and outperforms generic protein models on sequence recovery and structure robustness when applied on antibodies, with notable improvement on the hypervariable CDR-H3 loop. We study the canonical conformations of complementarity-determining regions and find improved encoding of these loops into known clusters. Finally, we consider the applications of our model to drug discovery and binder design and evaluate the quality of proposed sequences using physics-based methods." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a>  <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a> </div> </div> <div id="header" class="is-hidden-mobile"> <a aria-hidden="true" tabindex="-1" 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href="https://arxiv.org/search/q-bio?searchtype=author&query=Dreyer,+F+A" rel="nofollow">Fr茅d茅ric A. Dreyer</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Cutting,+D" rel="nofollow">Daniel Cutting</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Schneider,+C" rel="nofollow">Constantin Schneider</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Kenlay,+H" rel="nofollow">Henry Kenlay</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Deane,+C+M" rel="nofollow">Charlotte M. Deane</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled Inverse folding for antibody sequence design using deep learning, by Fr\'ed\'eric A. Dreyer and 4 other authors</div> <a class="mobile-submission-download" href="/pdf/2310.19513">View PDF</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>We consider the problem of antibody sequence design given 3D structural information. Building on previous work, we propose a fine-tuned inverse folding model that is specifically optimised for antibody structures and outperforms generic protein models on sequence recovery and structure robustness when applied on antibodies, with notable improvement on the hypervariable CDR-H3 loop. We study the canonical conformations of complementarity-determining regions and find improved encoding of these loops into known clusters. Finally, we consider the applications of our model to drug discovery and binder design and evaluate the quality of proposed sequences using physics-based methods. </blockquote>  <div class="metatable"> <table summary="Additional metadata"> <tr> <td class="tablecell label">Comments:</td> <td class="tablecell comments mathjax">2023 ICML Workshop on Computational Biology, model weights available at <a href="https://zenodo.org/record/8164693" rel="external noopener nofollow" class="link-external link-https">this https URL</a></td> </tr> <tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Biomolecules (q-bio.BM)</span>; Artificial Intelligence (cs.AI)</td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2310.19513">arXiv:2310.19513</a> [q-bio.BM]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2310.19513v1">arXiv:2310.19513v1</a> [q-bio.BM]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> <a href="https://doi.org/10.48550/arXiv.2310.19513" id="arxiv-doi-link">https://doi.org/10.48550/arXiv.2310.19513</a><div class="button-and-tooltip"> <button class="more-info" aria-describedby="more-info-desc-1"> <svg height="15" role="presentation" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 110c23.196 0 42 18.804 42 42s-18.804 42-42 42-42-18.804-42-42 18.804-42 42-42zm56 254c0 6.627-5.373 12-12 12h-88c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h12v-64h-12c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h64c6.627 0 12 5.373 12 12v100h12c6.627 0 12 5.373 12 12v24z" class=""></path></svg> <span class="visually-hidden">Focus to learn more</span> </button>  <div role="tooltip" id="more-info-desc-1"> <span class="left-corner"></span> arXiv-issued DOI via DataCite</div> </div> </td> </tr></table> </div> </div> </div> <div class="submission-history"> <h2>Submission history</h2> From: Fr茅d茅ric Dreyer [<a href="/show-email/401cc2ad/2310.19513" rel="nofollow">view email</a>] <br/> <strong>[v1]</strong> Mon, 30 Oct 2023 13:12:41 UTC (1,708 KB)<br/> </div> </div>  <div class="extra-services"> <div class="full-text"> <a name="other"></a> <span class="descriptor">Full-text links:</span> <h2>Access Paper:</h2> <ul> <div id="download-button-info" hidden> View a PDF of the paper titled Inverse folding for antibody sequence design using deep learning, by Fr\'ed\'eric A. 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[2310.19513] Inverse folding for antibody sequence design using deep learning