Network-Integrated Reverse Vaccinology Using Biomni-Prioritized Features and Graph Neural Networks in Flavobacterium

Network-Integrated Reverse Vaccinology Using Biomni-Prioritized Features and Graph Neural Networks in Flavobacterium | 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 Network-Integrated Reverse Vaccinology Using Biomni-Prioritized Features and Graph Neural Networks in Flavobacterium Muhammad Kazim, Faria Farzana, Harun Pirim, Larry Hanson, Matt Griffin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8107499/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 Rational vaccine design against emerging and understudied pathogens is hindered by incomplete protein–protein interaction (PPI) maps and limited integration of computational prioritization with network-based inference. Here, we present a unified framework that couples Biomni, a biomedical AI agent providing vaccine-priority and virulence-aware annotations, with graph neural network (GNN) based link prediction on STRING-derived PPIs to systematically identify candidate vaccine targets in Flavobacterium. Starting from the complete Flavobacterium proteome, Biomni-derived antigenicity, subcellular, and virulence features were integrated with high-confidence STRING associations into a unified PPI graph in which isolated and weakly connected proteins were explicitly retained. We evaluated three representative GNN architectures Graph Convolutional Network (GCN), Graph Attention Network (GAT), and GraphSAGE under a consistent three-fold cross-validation scheme and performed priorityaware assessment of predicted high-confidence links (posterior probability ≥ 0.90). GraphSAGE achieved the highest best-fold ROC-AUC (0.5585), followed by GAT (0.5536) and GCN (0.5000), reflecting modest yet meaningful discriminative performance in this sparse, single-species setting. Class-pair analyses revealed that GCN predominantly reconstructed links among lower-priority nodes, while GAT modestly increased coverage of interactions involving High-priority proteins but 1 remained biased toward low-tier combinations. In contrast, GraphSAGE produced a more balanced distribution of predicted links across Biomni priority tiers, including enriched High–Medium and High–Low connections, indicating more effective use of antigenicity and virulence features for inductive generalization to under-characterized proteins. Collectively, these results demonstrate that integrating AI-driven prioritization with inductive GNNs enables biologically informed exploration of missing PPIs and highlights previously overlooked Flavobacterium proteins as plausible vaccine candidates. The proposed Biomni–STRING–GNN framework is modular and transferable, offering a principled template for priority-aware, network-based vaccine target discovery in data-sparse pathogen systems. Aquaculture and Mariculture Reverse vaccinology Graph neural networks (GNNs) Biomni prioritization Protein–protein interaction networks Vaccine candidate discovery Inductive representation learning Full Text Additional Declarations The authors declare no competing interests. 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. 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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-8107499","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544657515,"identity":"7963e67e-8d74-4334-8426-7db89e1a3e8c","order_by":0,"name":"Muhammad Kazim","email":"","orcid":"","institution":"North Dakota State University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Kazim","suffix":""},{"id":544657516,"identity":"33fbcf2e-9533-4d13-9c5d-3557dbb4a98f","order_by":1,"name":"Faria Farzana","email":"","orcid":"","institution":"North Dakota State 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