Genome Mining and Modeling of Biosynthetic Gene Clusters with Bioprospecting Potential in Environmental Bacteria: From In Silico to In Vitro

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Abstract
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.

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