Computational strategies for uncovering bacterial biocatalysts in the biodegradation of persistent organic pollutants.

The rapid accumulation of persistent organic pollutants (POPs) in soil, sediment, and aquatic environments presents a critical global challenge that demands sustainable and efficient remediation strategies. In this context, computational enzymology has emerged as a powerful framework for accelerating the discovery, validation, and optimization of pollutant-degrading enzymes. However, prior POP-biodegradation reviews have typically treated enzymes, docking, molecular dynamics (MD), metagenomics, and artificial intelligence (AI) as separate topics (e.g., docking-focused reviews (1), metagenomics-focused reviews (2), and structural-mechanism-focused reviews (3) rather than as parts of a single bacterial-enzyme discovery pipeline. This review fills that gap by focusing specifically on bacterial biocatalysts and by integrating structure prediction, docking, MD, metagenomic mining, and machine learning-guided design into one workflow. Its main contribution is a unified framework that links sequence discovery to structural screening, dynamic validation, and experimental prioritization. This work provides a comprehensive synthesis of molecular docking, molecular dynamics (MD) simulations, and integrative artificial intelligence (AI)-driven approaches applied to biodegradation research. We highlight how molecular docking functions as a high-throughput, structure-based filter for prioritizing enzyme-pollutant interactions, while MD simulations supply the essential temporal and mechanistic resolution required to evaluate enzyme flexibility, substrate access pathways, and catalytic competence under realistic environmental conditions. Case studies across diverse pollutant classes including polycyclic aromatic hydrocarbons, organochlorine pesticides, polychlorinated biphenyls, and plastic additives demonstrate that workflows combining docking with microsecond-scale MD and MM-PBSA/GBSA free-energy calculations show markedly higher experimental reproducibility than static docking alone. Beyond individual methods, this article emphasizes the growing importance of integrative computational strategies that unite metagenomics, AI-based structure prediction, enhanced-sampling MD, and machine learning-guided directed evolution within a closed-loop Design-Build-Test-Learn (DBTL) paradigm. Such pipelines enable systematic navigation of vast biological sequence space while simultaneously balancing enzyme stability, conformational flexibility, and catalytic efficiency. Finally, we discuss prevailing challenges encompassing computational cost, structural uncertainty in apo-state predictions, force-field limitations for halogenated substrates, and the translational gap between in silico predictions and environmental field deployment, and outline future directions toward scalable, low-energy, and environmentally robust bioremediation technologies. Collectively, these advances position computational modeling as a cornerstone of next-generation, eco-friendly enzyme discovery.