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Background <p>The identification of B-cell epitopes, the regions that bind to antibodies, is essential for creating effective prophylactic treatments against infectious diseases and cancer, particularly in the realm of reverse vaccinology. While experimental techniques like X-ray crystallography and peptide arrays help identify epitopes, they are expensive, time-consuming and differ in throughput and precision.</p> Methods <p>This review examines how predictive techniques and datasets have evolved for the problem, highlighting recent breakthroughs in data-driven algorithms used to predict B-cell epitopes. We specifically examine how methodologies have progressed from traditional machine learning to cutting-edge deep learning models.</p> Conclsion <p>The review summarizes significant research contributions in this domain including linear and conformational epitope prediction techniques, addresses methodological biases, dataset limitations, systematic evaluation challenges that plague the field, and explores future opportunities for innovation.</p> Graphical Abstract <p></p>

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B-cell epitope prediction in the age of machine learning: advancements and challenges

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月22日

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  • Fabrizio Gabellieri,
  • Ankita Singh,
  • Sukrit Gupta,
  • Halima Bensmail,
  • Filippo Castiglione,
  • Raghvendra Mall

摘要

Background

The identification of B-cell epitopes, the regions that bind to antibodies, is essential for creating effective prophylactic treatments against infectious diseases and cancer, particularly in the realm of reverse vaccinology. While experimental techniques like X-ray crystallography and peptide arrays help identify epitopes, they are expensive, time-consuming and differ in throughput and precision.

Methods

This review examines how predictive techniques and datasets have evolved for the problem, highlighting recent breakthroughs in data-driven algorithms used to predict B-cell epitopes. We specifically examine how methodologies have progressed from traditional machine learning to cutting-edge deep learning models.

Conclsion

The review summarizes significant research contributions in this domain including linear and conformational epitope prediction techniques, addresses methodological biases, dataset limitations, systematic evaluation challenges that plague the field, and explores future opportunities for innovation.

Graphical Abstract