Computation-Guided Discovery of Agricultural Waste-Derived Biosorbents for Sustainable Heavy-Metal Remediation

Heavy metal pollution in water is a critical issue in India requiring immediate attention, caused by increased urbanisation, exploitation of natural resources, and agricultural and domestic waste run-off. Recent studies focusing on heavy metal removal using engineered adsorbents and biodegradable chelating agents in soil and sludge systems have highlighted the need to investigate the effectiveness of simple organic chelating agents in removing heavy metals from aqueous environments. This study uses bioinformatics analysis to evaluate proteins derived from agricultural waste for heavy metal removal from water. Datasets were collected from open-access repositories and bioinformatics databases related to heavy metal contamination and different properties of agricultural waste biosorbents, including orange, lemon, banana peel, and sugarcane bagasse. Molecular docking was performed using SMINA tool of Tamarind Bio to examine binding interactions between six proteins retrieved from the RCSB Protein Data Bank and heavy metal ion ligands retrieved from PubChem. Docked complexes were visualised in PyMOL, and distances were calculated to assess binding validity, and cavity analysis was carried out using pyKVFinder to characterise the binding pockets. Peel metallothionines emerged as the strongest receptor, recording binding affinities of −10.1Kcal/mol for cadmium, nickel, copper, zinc, and iron, supported by valid interaction distances and a favourable cavity environment, while the peroxidase from banana peel performed strongly among the remaining proteins. These findings are purely computational demonstrating the potential of agricultural wastes as low-cost biosorbents. The actual potential for designing targeted solutions for heavy metal removal from contaminated water should be experimentally validated.