<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Zhang X</submitter><funding>Beijing Municipal Natural Science Foundation</funding><funding>Guangdong-PHOENIX Center Joint Lab on Chinese Medicine</funding><funding>State Key Laboratory of Proteomics</funding><funding>Science and Technology Planning Project of Guangdong Province</funding><funding>National Key R&amp;D Program of China</funding><funding>Capital Health Development Research Project</funding><funding>National Key Research and Development Program of China</funding><funding>Beijing Municipal International Cooperation and Exchange Project</funding><funding>Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine</funding><pagination>e2402639</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11516136</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>11(40)</volume><pubmed_abstract>Host immunity is central to the virus's spread dynamics, which is significantly influenced by vaccination and prior infection experiences. In this work, we analyzed the co-evolution of SARS-CoV-2 mutation, angiotensin-converting enzyme 2 (ACE2) receptor binding, and neutralizing antibody (NAb) responses across various variants in 822 human and mice vaccinated with different non-Omicron and Omicron vaccines is analyzed. The link between vaccine efficacy and vaccine type, dosing, and post-vaccination duration is revealed. The classification of immune protection against non-Omicron and Omicron variants is co-evolved with genetic mutations and vaccination. Additionally, a model, the Prevalence Score (P-Score) is introduced, which surpasses previous algorithm-based models in predicting the pote</pubmed_abstract><journal>Advanced science (Weinheim, Baden-Wurttemberg, Germany)</journal><pubmed_title>SARS-CoV-2 Evolution: Immune Dynamics, Omicron Specificity, and Predictive Modeling in Vaccinated Populations.</pubmed_title><pmcid>PMC11516136</pmcid><funding_grant_id>ZYYCXTD-C-202204</funding_grant_id><funding_grant_id>2024-1-4093</funding_grant_id><funding_grant_id>2022YFE0210400</funding_grant_id><funding_grant_id>2023YFC0872400</funding_grant_id><funding_grant_id>2020B1111100006</funding_grant_id><funding_grant_id>Z231100002723004</funding_grant_id><funding_grant_id>L234034</funding_grant_id><funding_grant_id>SKLP-O202007</funding_grant_id><funding_grant_id>SKLP-O202205</funding_grant_id><funding_grant_id>2021YFA1301604</funding_grant_id><funding_grant_id>YN2021DB06</funding_grant_id><funding_grant_id>2024-1-1201</funding_grant_id><funding_grant_id>M23010</funding_grant_id><funding_grant_id>YN2021DB03</funding_grant_id><pubmed_authors>Li H</pubmed_authors><pubmed_authors>Zhu Y</pubmed_authors><pubmed_authors>Wang B</pubmed_authors><pubmed_authors>Zhang N</pubmed_authors><pubmed_authors>Teng F</pubmed_authors><pubmed_authors>Guo S</pubmed_authors><pubmed_authors>Li M</pubmed_authors><pubmed_authors>Li Y</pubmed_authors><pubmed_authors>Zhang X</pubmed_authors><pubmed_authors>Jia Y</pubmed_authors><pubmed_authors>Xu X</pubmed_authors><pubmed_authors>Wang Y</pubmed_authors><pubmed_authors>Yu X</pubmed_authors><pubmed_authors>Qin C</pubmed_authors></additional><is_claimable>false</is_claimable><name>SARS-CoV-2 Evolution: Immune Dynamics, Omicron Specificity, and Predictive Modeling in Vaccinated Populations.</name><description>Host immunity is central to the virus's spread dynamics, which is significantly influenced by vaccination and prior infection experiences. In this work, we analyzed the co-evolution of SARS-CoV-2 mutation, angiotensin-converting enzyme 2 (ACE2) receptor binding, and neutralizing antibody (NAb) responses across various variants in 822 human and mice vaccinated with different non-Omicron and Omicron vaccines is analyzed. The link between vaccine efficacy and vaccine type, dosing, and post-vaccination duration is revealed. The classification of immune protection against non-Omicron and Omicron variants is co-evolved with genetic mutations and vaccination. Additionally, a model, the Prevalence Score (P-Score) is introduced, which surpasses previous algorithm-based models in predicting the pote</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Oct</publication><modification>2026-04-30T00:11:37.464Z</modification><creation>2025-04-04T00:30:02.57Z</creation></dates><accession>S-EPMC11516136</accession><cross_references><pubmed>39206813</pubmed><doi>10.1002/advs.202402639</doi></cross_references></HashMap>