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Multi-perspectives and challenges in identifying B-cell epitopes.

Nishant Kumar1, Nisha Bajiya1, Sumeet Patiyal1

  • 1Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.

Protein Science : a Publication of the Protein Society
|September 21, 2023
PubMed
Summary

This review comprehensively covers B-cell epitope identification methods, including experimental and computational approaches for both linear and conformational epitopes. It addresses challenges and resources for developing vaccines and immunotherapies.

Keywords:
B-cell epitope predictionclass-specific epitopesconformational B-cell epitopedatabasesdeep learninglinear B-cell epitopemachine learning

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Area of Science:

  • Immunology
  • Bioinformatics
  • Vaccine Development

Background:

  • B-cell epitopes (BCEs) are critical for designing vaccines and immunotherapies.
  • Existing reviews lack a comprehensive overview of BCE identification methods.
  • In silico prediction of BCEs has advanced significantly over four decades.

Purpose of the Study:

  • To provide a systematic and comprehensive review of all aspects of B-cell epitope identification.
  • To cover experimental techniques, historical perspectives, and computational methods for BCE prediction.
  • To discuss the challenges in identifying both linear and conformational epitopes.

Main Methods:

  • Systematic review of experimental techniques for epitope identification.
  • Review of historical resources for validated BCE information.
  • Extensive review of in silico methods for predicting conformational and linear BCEs.
  • Discussion of challenges in epitope identification.

Main Results:

  • Detailed overview of experimental methods, their limitations, and challenges.
  • Summary of historical data and resources for BCEs.
  • Comprehensive review of computational tools for predicting conformational and linear BCEs.
  • Identification of overarching challenges in epitope mapping.

Conclusions:

  • A comprehensive understanding of BCE identification is essential for advancing vaccine and immunotherapy development.
  • This review consolidates knowledge on experimental and computational approaches, addressing current limitations.
  • Further research and development of robust prediction tools are needed to overcome identification challenges.