<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Yuan L</submitter><funding>Beijing Natural Science Foundation Project</funding><funding>Guangdong Basic and Applied Basic Research Foundation</funding><funding>Sanming Project of Medicine in Shenzhen Nanshan</funding><funding>CAMS Innovation Fund for Medical Sciences</funding><funding>Science and Technology Projects in Guangzhou</funding><funding>Shenzhen Science and Technology Program</funding><pagination>357</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12937623</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>15(4)</volume><pubmed_abstract>Influenza's pandemic threat is driven by antigenic drift, which limits the efficacy of conventional vaccines. To address this challenge, we established a clinical serum-anchored computational design pipeline for a broad-spectrum multi-epitope mRNA vaccine (MEMV), bridging the gap between pure in silico design and clinical applicability. Using 36 longitudinal sera (d0/d28/d365) from 12 well-characterized human cohorts (6 vaccine recipients and 6 influenza patients) and high-density antibody-peptide microarrays, we empirically identified 12 immunodominant B-cell linear epitopes from the nucleoprotein (NP) of influenza A (H1N1/H3N2) and B viruses. These experimentally validated epitopes were combined with in silico-predicted conserved helper T-lymphocyte (HTL)/cytotoxic T-lymphocyte (CTL) epi</pubmed_abstract><journal>Biology</journal><pubmed_title>Clinical Serum-Anchored Computational Design Pipeline for a Broad-Spectrum Influenza Multi-Epitope mRNA Vaccine.</pubmed_title><pmcid>PMC12937623</pmcid><funding_grant_id>L254023</funding_grant_id><funding_grant_id>2022-I2M-1-021;2023-I2M-2-001</funding_grant_id><funding_grant_id>2024A04J4015</funding_grant_id><funding_grant_id>SZSM202103008</funding_grant_id><funding_grant_id>No. L242036</funding_grant_id><funding_grant_id>KCXFZ20211020172545006; KQTD20200820145822023; ZDSYS20230626091203007</funding_grant_id><funding_grant_id>2023A1515011767</funding_grant_id><pubmed_authors>Yan M</pubmed_authors><pubmed_authors>Bi R</pubmed_authors><pubmed_authors>Yuan L</pubmed_authors><pubmed_authors>Wen S</pubmed_authors><pubmed_authors>Luo H</pubmed_authors><pubmed_authors>Zhao Y</pubmed_authors><pubmed_authors>Song J</pubmed_authors><pubmed_authors>Li W</pubmed_authors><pubmed_authors>Li X</pubmed_authors><pubmed_authors>Bai T</pubmed_authors><pubmed_authors>Li Y</pubmed_authors><pubmed_authors>Chen Y</pubmed_authors><pubmed_authors>Ouyang Z</pubmed_authors><pubmed_authors>Shu Y</pubmed_authors><pubmed_authors>Wu Y</pubmed_authors></additional><is_claimable>false</is_claimable><name>Clinical Serum-Anchored Computational Design Pipeline for a Broad-Spectrum Influenza Multi-Epitope mRNA Vaccine.</name><description>Influenza's pandemic threat is driven by antigenic drift, which limits the efficacy of conventional vaccines. To address this challenge, we established a clinical serum-anchored computational design pipeline for a broad-spectrum multi-epitope mRNA vaccine (MEMV), bridging the gap between pure in silico design and clinical applicability. Using 36 longitudinal sera (d0/d28/d365) from 12 well-characterized human cohorts (6 vaccine recipients and 6 influenza patients) and high-density antibody-peptide microarrays, we empirically identified 12 immunodominant B-cell linear epitopes from the nucleoprotein (NP) of influenza A (H1N1/H3N2) and B viruses. These experimentally validated epitopes were combined with in silico-predicted conserved helper T-lymphocyte (HTL)/cytotoxic T-lymphocyte (CTL) epi</description><dates><release>2026-01-01T00:00:00Z</release><publication>2026 Feb</publication><modification>2026-07-11T03:16:26.018Z</modification><creation>2026-07-11T03:11:44.167Z</creation></dates><accession>S-EPMC12937623</accession><cross_references><pubmed>41744666</pubmed><doi>10.3390/biology15040357</doi></cross_references></HashMap>