Mapping immune imprinting zones enables predictive vaccination optimization

Mapping immune imprinting zones enables predictive vaccination optimization | 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 Mapping immune imprinting zones enables predictive vaccination optimization Jian Lu, Wei Yang, Kaichun Jin, Wei Li, Zhixing Ding, Houze Yu, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7719056/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 Immune imprinting, where prior exposures shape antibody responses to variant antigens, remains a central obstacle to optimizing vaccination against evolving viruses. Here, we present DynaVac, a mechanistic framework that models antigen-specific B cell dynamics, particularly the competition between memory and naive compartments across antigenic distances. Calibrated on neutralization titers from murine and human studies spanning diverse SARS-CoV-2 vaccine platforms, DynaVac accurately predicts antibody responses across complex heterologous and multivalent regimens. In silico simulations reveal three imprinting zones—protection, pitfall, and breakthrough—that determine when updated vaccinations amplify, suppress, or bypass preexisting immunity. Unlike prior models limited to qualitative or single-exposure settings, DynaVac integrates empirical cross-neutralization matrices and enables prospective simulation of continuous booster responses across antigenic variants, dosages, and intervals. DynaVac also provides an actionable strategy for guiding real-time vaccine updates and strain selection. While DynaVac is calibrated on SARS-CoV-2, its structure is generalizable to other fast-evolving pathogens. Biological sciences/Immunology/Vaccines Biological sciences/Immunology/Adaptive immunity/Humoral immunity/Immunological memory Health sciences/Pathogenesis/Immunopathogenesis/Adaptive immunity/Humoral immunity/Immunological memory Immune imprinting Vaccination strategies Immunogenicity evolution Dynamic vaccination modeling SARS-CoV-2 evolution Full Text Additional Declarations Yes there is potential Competing Interest. Z.S.Z., J.L., W.Y., and K.J. are the inventors of a patent (application number: 202411227694.2). The remaining authors declare no competing interests. Supplementary Files SupplementaryTable16.xlsx Supplementary Table 16 SupplementaryTable15.xlsx Supplementary Table 15 SupplementaryTable18.xlsx Supplementary Table 18 SupplementaryTable17.xlsx Supplementary Table 17 SupplementaryTable14.xlsx Supplementary Table 14 SupplementaryInformation.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. 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-7719056","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":522811230,"identity":"c4f26cf5-02b7-4826-927f-8de1ec52a9ce","order_by":0,"name":"Jian 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