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.

Background/Objectives: The global crisis of antimicrobial resistance and limited discov-ery of new antimicrobial classes have intensified the search for alternative sources of bio-active natural products. Environmental bacteria harbor largely unexplored biosynthetic diversity, much of which is encoded in biosynthetic gene clusters (BGCs). This review examines current strategies for genome-guided BGC bioprospecting, emphasizing antimi-crobial and environmental applications, and the transition from computational prediction to experimental validation. Methods: We review advances in bacterial genome mining, protein structure prediction, molecular docking, and experimental approaches for evalu-ating antimicrobial and bioremediation potential. Emphasis is placed on representative environmental bacterial genera and the integration of genomic, structural, metabolomic, microbiological, and environmental approaches. Results: Genome mining has revealed substantial biosynthetic potential across diverse environmental bacteria. Comparative ge-nomics and BGC similarity analyses support dereplication and prioritization, while AI-based approaches can expand the search toward atypical and poorly characterized bi-osynthetic systems. Protein structure prediction and molecular docking provide mecha-nistic hypotheses but cannot independently demonstrate metabolite production or biolog-ical activity. Experimental approaches, including One Strain-Many Compounds, heterol-ogous expression, metabolomics, functional genetics, antimicrobial assays, and environ-mentally relevant microcosms, are therefore essential for linking genomic potential to me-tabolite production and biological function. Conclusions: BGC-driven bioprospecting pro-vides a powerful framework for expanding microbial natural product discovery beyond conventional screening. Its greatest potential lies in multi-omics approaches and experi-mental validation. Future progress will depend on improving the prioritization and acti-vation of cryptic BGCs and establishing stronger gene-to-metabolite-to-function relation-ships, ultimately supporting the development of novel antimicrobials and environmental-ly relevant biotechnological applications.