Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps

Abstract

The immune system leverages B and T cells to recognize specific molecular patterns, known as epitopes, on pathogens and cancer cells to effectively combat infections and diseases. The critical success of immunotherapies in cancer treatment and COVID-19 vaccine development has established precise epitope identification as a central and rapidly growing priority in therapeutic design. Because traditional wet-lab based identification of B- and T-cell epitopes is expensive and timeconsuming, our systematic literature search (2015–2026) identified 148 Artificial Intelligence (AI) based linear B-cell, conformational B-cell, and T-cell epitope prediction models within the stated search scope. However, the true potential of these models remains unclear due to fragmented evaluation practices, with models rarely tested across a comprehensive range of datasets, insufficient comparison with existing predictors, systematic under-utilization of available public databases, and other methodological inconsistencies across five different stages of the predictive pipeline. Moreover, the 7 existing review papers fail to adequately highlight these research gaps or to support the development of robust AI models. This review paper consolidates the computational landscape of B-cell and T-cell epitope recognition and introduces a unified taxonomy that organises the field into twelve prediction tasks: linear and conformational B-cell prediction together with ten T-cell subtasks (T1–T10) that span the antigen-recognition cascade from human leukocyte antigen (HLA) typing to vaccine design. It analyses these twelve tasks across five major stages of a shared predictive pipeline. Within this taxonomy, the recognition sub-tasks (T1–T7) apply to the epitopes of any antigen, whereas neoantigen identification (T8) and tumor T-cell antigen (TTCA) classification (T9) are oncology-specific translational applications and multi-epitope vaccine design (T10) spans infectious-disease and cancer targets. It systematically examines 155 studies published from 2015 to 2026 to perform comprehensive categorization of 43 dedicated epitope/immunology databases, 144 benchmark datasets, 272 representation learning approaches, 148 classifiers, 54 evaluation and optimization approaches, and 148 predictive models accessibility status across all twelve prediction tasks. Additionally, it highlights persistent challenges across each stage of the predictive pipeline and offers key directions for improvement. This comprehensive analysis provides actionable recommendations for developing more robust, generalizable epitope predictors, and it ultimately accelerates the translation of computational predictions into effective immunotherapies against diverse diseases.