Intelligent Batch Epitope Identification and Partitioning: an intelligent tool for the identification of B cell dominant epitopes

Identifying B cell dominant epitopes helps to improve vaccine design and better understand immune evasion of pathogens. Herein, we present the Intelligent Batch Epitope Identification and Partitioning (IBEIP), an intelligent tool for identifying B cell dominant epitope regions based on antigen-neutralizing antibody (Ag-nAb) complex data. IBEIP can accurately map the epitopes on any appointed Ag-nAb complex by analyzing antigen-antibody interactions at a molecular level. Combined with a hierarchical iterative merging model, IBEIP can intelligently merge and analyze mapped epitopes to identify B cell dominant epitopes. It is also applicable to analyzing high-mutant antigens and complex epitope structures. We demonstrated the performance of IBEIP by analyzing 127 Ag-nAb complexes from the respiratory syncytial virus (RSV) fusion, SARS-CoV-2 spike, and high-mutant influenza hemagglutinin. Over 90% of the residues overlapped between IBEIP and reported epitopes, confirming its reliability. IBEIP also uncovered new and important B cell dominant epitope regions and structures of these pathogens for researchers. Our study provides a reliable, intelligent tool for B cell dominant epitope analysis and offers some valuable insights for preventing RSV, SARS-CoV-2, and influenza infections.

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Publication Details

Journal
Briefings in Bioinformatics
Published
2025-07-01
DOI
https://doi.org/10.1093/bib/bbaf310
Primary Topic
Immunotherapy and Immune Responses
Type
article
Field-Weighted Citation Impact
0.00

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article

Intelligent Batch Epitope Identification and Partitioning: an intelligent tool for the identification of B cell dominant epitopes

Ye Liu, Yun-Fei Ma
Briefings in Bioinformatics
Immunotherapy and Immune Responses
article

Intelligent Batch Epitope Identification and Partitioning: an intelligent tool for the identification of B cell dominant epitopes

Ye Liu, Yun-Fei Ma
article en

Abstract

Identifying B cell dominant epitopes helps to improve vaccine design and better understand immune evasion of pathogens. Herein, we present the Intelligent Batch Epitope Identification and Partitioning (IBEIP), an intelligent tool for identifying B cell dominant epitope regions based on antigen-neutralizing antibody (Ag-nAb) complex data. IBEIP can accurately map the epitopes on any appointed Ag-nAb complex by analyzing antigen-antibody interactions at a molecular level. Combined with a hierarchical iterative merging model, IBEIP can intelligently merge and analyze mapped epitopes to identify B cell dominant epitopes. It is also applicable to analyzing high-mutant antigens and complex epitope structures. We demonstrated the performance of IBEIP by analyzing 127 Ag-nAb complexes from the respiratory syncytial virus (RSV) fusion, SARS-CoV-2 spike, and high-mutant influenza hemagglutinin. Over 90% of the residues overlapped between IBEIP and reported epitopes, confirming its reliability. IBEIP also uncovered new and important B cell dominant epitope regions and structures of these pathogens for researchers. Our study provides a reliable, intelligent tool for B cell dominant epitope analysis and offers some valuable insights for preventing RSV, SARS-CoV-2, and influenza infections.

Briefings in BioinformaticsVol. 26(4)
Oklahoma State Department of Health (US), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Ministry of Education (RO)
National Key Research and Development Program of China
Good health and well-being
Openalex Percentile: Top 82%
Immunotherapy and Immune Responses
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Intelligent Batch Epitope Identification and Partitioning: an intelligent tool for the identification of B cell dominant epitopes — Ye Liu, Yun-Fei Ma · Briefings in Bioinformatics (2025) | TGRS Research Map | TGRS