COMPUTATIONAL SCREENING OF PHYTOCHEMICAL–CHITOSAN NANO-CONJUGATES AGAINST WHO-PRIORITY PATHOGENS IN CONTAMINATED FRESHWATER ECOSYSTEMS

Abstract

Urban freshwater lakes, including those in rapidly growing cities like Chennai, act as important environmental reservoirs for antibiotic-resistant bacteria, driven largely by anthropogenic pollution and wastewater inputs. In this computational study, we synthesized published data on bacterial communities from Chennai lake water, with emphasis on two high-priority WHO pathogens: carbapenem-resistant Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus. Resistance gene profiles were characterized through literature mining and visualized using Python libraries (Pandas, Matplotlib, Seaborn), highlighting dominant mechanisms including efflux pumps, enzymatic inactivation, and target-site alterations. Blind molecular docking simulations (CB-Dock2) assessed the binding potential of key phytochemicals isolated from Caesalpinia sappan—brazilin, brazilein, sappanchalcone, caesalpinin, and cholic acid—both alone and conjugated with chitosan nanoparticles, targeting the aminoglycoside-modifying enzyme Aac(6′)-Ib (PDB ID: 2QIR). Results demonstrated that chitosan conjugation markedly improved binding affinities for all tested ligands, with the most pronounced enhancements observed for brazilein (from −9.1 to −14.8 kcal/mol) and cholic acid (from −9.3 to −14.7 kcal/mol). These in silico findings indicate that C. sappan-derived compounds, particularly when combined with chitosan, may offer promising natural scaffolds for inhibiting resistance enzymes in contaminated aquatic systems. The work underscores the value of computational approaches in exploring sustainable bioremediation options while emphasizing the essential need for subsequent experimental confirmation, including in vitro assays and molecular dynamics simulations, to combat antimicrobial resistance in polluted freshwater environments.