DeepNeoAGNet: Enhancing Cancer Immunotherapy with General Heterogeneous Sequence Learning for Precise Neoantigen Prediction and Immunogenicity Assessment

DeepNeoAGNet: Enhancing Cancer Immunotherapy with General Heterogeneous Sequence Learning for Precise Neoantigen Prediction and Immunogenicity Assessment | 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 Research Article DeepNeoAGNet: Enhancing Cancer Immunotherapy with General Heterogeneous Sequence Learning for Precise Neoantigen Prediction and Immunogenicity Assessment Xian-Xian LIU, Jie YANG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7699332/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Multi-Peptide Personalized drugs have emerged as a prominent focus in pharmaceutical research owing to their high specificity, low toxicity, and excellent biocompatibility. They are increasingly being applied across various fields, including peptide-drug conjugates (PDCs), infectious disease vaccines, and anti-aging interventions. Immunotherapy, leveraging the immune system to combat cancer, increasingly relies on identifying neoantigens—tumor-specific proteins arising from mutations. These neoantigens, pivotal for personalized cancer vaccines, present computational challenges in their identification and binding affinity prediction to T-cell receptors. This study introduces DeepNeoAGNet, a novel sequence deep learning framework designed for gene sequence prediction. DeepNeoAGNet utilizes bidirectional long-short term memory (Bi-LSTM) neural networks to extract important features from peptide sequences by incorporating both global and local bioengineer information. Our approach incorporates new analysis techniques, including comprehensive amino acid feature engineering such as global amino acid composition strategy, dipeptide composition, local amino acid composition, and local dipeptide composition. Inter-residue sequence relationship analysis is employed to capture intricate dependencies within the sequences. In both the training and testing phases, our approach achieves higher R-square values, indicating a correlation enhancement of 46.466%, and lower RMSE, achieving an accuracy improvement of 11.147%, compared to HLAIImaster and TripHLApan models. These advancements indicate the possibility of facilitating precise identification of HLA-peptide binding, offering researchers and clinicians with a more effective and general tool for neoantigen prediction. By leveraging both global and local features within a BiLSTM architecture, DeepNeoAGNet offers interpretable prediction. Furthermore, it serves as a valuable domain-specific knowledge resource for large language models (LLMs). Immunotherapy immunogenomics neoantigen identification HLA-peptide affinity prediction Hetero-bidirectional LSTM neural networks T-cell receptor (TCR) interaction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7699332","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540340225,"identity":"050a866d-350e-428c-bd46-e424ae28ae25","order_by":0,"name":"Xian-Xian 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