Generative Design of High-affinity T-cell Receptors by Progressive Learning with Structural Confidence

Generative Design of High-affinity T-cell Receptors by Progressive Learning with Structural Confidence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Generative Design of High-affinity T-cell Receptors by Progressive Learning with Structural Confidence Fuli Feng, Xinyuan Zhu, Qingshuo Jin, Jiadong Lu, Jun Wu, Lixia Zhu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8992119/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The engineering of T-cell receptors (TCRs) for precision immunotherapy is constrained by the difficulty of modeling the conformational dynamics of hypervariable regions, a challenge exacerbated by the scarcity of experimental structures. In this paper, we constructed ProTSC-TCR, a specialized model for the sequence-structure co-design of TCRs conditioned on specific peptide-major histocompatibility complexes (pMHCs), through a progressive learning framework that synthesizes the sequence-structure distribution of TCR-pMHCs by knowledge transfer from antibody-antigen complexes and structure prediction tools. ProTSC-TCR establishes a new state-of-the-art in TCR design, demonstrating robustness across experimental and computationally predicted TCR-pMHC templates, enabling the construction of a million-scale design database (ProTSC-TCR-DB) covering 957 pMHC targets. In experimental validation targeting SARS-CoV-2 Spike-Y453F and KRAS-G12V antigens, the model achieved a 55.0% success rate across 20 variants, delivering a 10-fold affinity improvement (37.8 μM to 3.5 μM) over wild-type receptors. These results underscore the broad utility of ProTSC-TCR and its learning framework. Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Immunology/Immunotherapy Biological sciences/Computational biology and bioinformatics/Protein design Biological sciences/Computational biology and bioinformatics/Computational models Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTable1.xlsx Supplementary Table 1 SupplementaryTable2.xlsx Supplementary Table 2 ProTSCTCRsuppinfo.pdf Supplementary Information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8992119","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":605073015,"identity":"174677de-d373-445d-a6c1-31d5e460d459","order_by":0,"name":"Fuli Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBAC9gYgUVFgA+WyEaGF5wCQOGOQBlVNgpbDpGhh7z384oDB+cT585sfMHwoO8zAP7uBgBaec2kWBwxuJ244xmbAOOPcYQaJOwfwa7GXyDEz/gDSwsbDwMzbdpjBQCKBgC3yb8wMDhicS5zfBtTylygtEjzGDw4YHEhsOAbUwkiUFp4cM4YDBsnGG46lGRzsOZfOI3GDkBb2M8YfDlTYyc5vPvzwwY8yazn+GQS0AAGbBIx1AGQGQfVAwPyBGFWjYBSMglEwggEA6KdC0G6Rvf8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5828-9842","institution":"University of Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Fuli","middleName":"","lastName":"Feng","suffix":""},{"id":605073016,"identity":"cc0e7665-334c-465e-aefb-05f2ee0c94f4","order_by":1,"name":"Xinyuan Zhu","email":"","orcid":"","institution":"University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Xinyuan","middleName":"","lastName":"Zhu","suffix":""},{"id":605073017,"identity":"fb380729-c6aa-4b47-8d43-0cb3c02d7712","order_by":2,"name":"Qingshuo Jin","email":"","orcid":"","institution":"University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Qingshuo","middleName":"","lastName":"Jin","suffix":""},{"id":605073018,"identity":"69f742d7-c6b7-404d-8168-2a2b814c440d","order_by":3,"name":"Jiadong Lu","email":"","orcid":"","institution":"University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Jiadong","middleName":"","lastName":"Lu","suffix":""},{"id":605073019,"identity":"aad95538-31aa-4dfb-921b-55f4fac604b3","order_by":4,"name":"Jun Wu","email":"","orcid":"","institution":"University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wu","suffix":""},{"id":605073020,"identity":"ee709259-49dd-49fc-bddb-e9ba71da3235","order_by":5,"name":"Lixia Zhu","email":"","orcid":"","institution":"University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Lixia","middleName":"","lastName":"Zhu","suffix":""},{"id":605073021,"identity":"87e63730-eb60-49ed-b9ef-13c63eb36c54","order_by":6,"name":"Yuhua Shang","email":"","orcid":"","institution":"Anhui Genebiol Biotech. 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