IISER-Bhopal team develops web-based solution to predict bacterial enzymes for pollutant breakdown
Bhopal: The widespread industrial growth and urbanisation have led to greater use of synthetic chemicals, including pesticides, fertilisers, and various plastics (PE, PET, PU, PVC).
Poor waste management practices resulted in these chemicals and heavy metals building up in soil and water systems.Traditional
bioremediation
methods require extensive laboratory work and costly analytical techniques. While natural microbial enzymes can break down complex pollutants, their identification using current methods is time-consuming and requires significant resources. A team in Bhopal plans to simplify and streamline the bioremediation process.
XenoBug
, a web-based solution developed by a team at
IISER Bhopal
, uses machine learning, neural networks, and chemo-informatics to predict bacterial metabolic enzymes capable of biodegrading specific contaminants.
The system houses a comprehensive database containing approximately 3.3 million enzyme sequences from environmental metagenomes and 16 million enzymes from 38,000 bacterial genomes. The study was recently published in Nucleic Acids Research Genomics and Bioinformatics (2025).
A team led by Dr Vineet Sharma, professor department of biological sciences in IISER has done the research work and the team also includes Dr Aditya S Malwe and Usha Longwani. The platform uses 6,814 enzyme substrates to train Random Forest and Artificial Neural Network classifiers. XenoBug operates through three distinct modules: Module-1 employs two multilabel classifiers for reaction class prediction, Module-2 uses six multilabel models for reaction subclass prediction, and Module-3 utilises structural similarity searches for complete reaction prediction. This tool helps identify
bacterial enzymes
for
pollutant breakdown
, determine bacterial groups and pathways linked to specific pollutant degradation, helping in developing effective bioremediation approaches.
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It enhances knowledge of pollutant-bacterial interactions and their biodegradation mechanisms.This research stands out for its extensive enzyme substrate database, coverage of environmental bacterial genomes and metagenomes, and proven effectiveness across various pollutant types, including pesticides, environmental contaminants, pharmaceutical products, and hydrocarbons.
The predictive algorithms analyse chemical structures and connect them with possible degradative pathways, simplifying the discovery of new bioremediation solutions.The system's database includes numerous environmental samples, providing reliable predictions for various pollutant types. It handles queries related to persistent organic pollutants, pharmaceutical compounds, industrial chemicals, and agricultural pesticides. The machine learning models, trained using verified enzyme-substrate interactions, deliver reliable predictions. XenoBug's modular design enables systematic analysis of degradation pathways. "The practical benefits include faster identification of suitable bacterial strains for bioremediation projects, reduced laboratory testing costs, and more targeted experimental designs," said Prof Sharma. Environmental scientists can use these predictions to develop more effective clean-up strategies for contaminated areas, he added. This computational approach provides insights that are typically challenging to obtain through conventional laboratory methods.
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