This review proposes a structured immunoinformatics framework tailored for diagnostic applications, addressing the current gap in standardized pipelines compared to well-established workflows in reverse vaccinology. Immunoinformatics integrates experimental immunology with computational approaches to predict antigen-epitope recognition by B and T cell immune receptors, supporting the identification of diagnostic and therapeutic targets. It enables rapid and cost-efficient prediction of peptide-MHC binding affinity and epitope immunogenicity through machine learning models and specialized algorithms trained on curated immunological data sets. Although epitope prediction pipelines are well-established in vaccinology, standardized frameworks for their application in diagnostic assays remain underdeveloped. This gap reflects challenges in integrating and implementing prediction tools within diagnostic development protocols, which demand distinct validation criteria and clinical applicability compared to vaccine design. We examine key methodological developments, and practical applications are illustrated through case studies involving viral, bacterial, parasitic, and fungal pathogens. Drawing from this assessment, we outline a modular pipeline for epitope prioritization that integrates sequence analysis, structural modeling, consensus-based prediction, and validation strategies. Analysis of the current literature suggests that prediction algorithms utilizing artificial intelligence models yield high accuracy in epitope identification. Following experimental validation, this approach demonstrates considerable potential for implementation in diagnostics. This integrative strategy underscores the value of combining AI-driven prediction, structural modeling, and multiepitope design in translational diagnostics. Epitope-centric approaches promise significant advances in biomarker platforms for diagnostics, vaccine development, and therapeutic design. This review highlights the integrative value of widely adopted immunoinformatics tools and their applicability to serological diagnostics.
Bulla et al. (Thu,) studied this question.