Machine-learning aided identification of non-antibiotic natural bgcs in bacteria
A cutting-edge bioinformatics solution utilizing machine learning to identify novel non-antibiotic natural products in bacteria by refining BGC predictions and integrating metabolite analysis.
Overview
This solution offers a sophisticated bioinformatics approach to identify unique biosynthetic gene clusters (BGCs) in bacteria that may encode novel non-antibiotic natural products. By employing machine learning and advanced metabolite profiling, this approach seeks to unveil valuable natural compounds often overlooked by conventional methods.
Technical specifications
- Machine Learning Model: Utilizes Scikit-learn to train on known BGCs, excluding antibiotics, to predict atypical BGCs.
- Metabolite Analysis: Employs high-throughput mass spectrometry (GNPS) to identify unknown metabolites in bacterial samples.
- Data Integration: Combines BGC predictions with metabolite profiles, filtering out known antibiotic signatures, to identify novel natural product pathways.
Technology readiness level
This research is currently at Technology Readiness Level 2, indicating that the basic principles have been formulated and the system's potential applications are being explored through analytical studies.
About R.V. College of Engineering
RV College of Engineering (RVCE) is an autonomous, self‑financing engineering institution in Bengaluru, affiliated to Visvesvaraya Technological University and accredited NAAC A+. It connects to industry through an active Industry Institute Interaction Cell, 150+ MoUs, and co‑located, industry‑sponsored labs and Centers of Excellence that enable joint training, prototyping, and upskilling on campus. Proximity to Bengaluru’s technology cluster supports internships, capstone co‑supervision, and consultancy engagements throughout the year. Research here is supported by competitive national programs and industry, including DRDO/NRB, AICTE, and ISRO collaborations. An IP Coordination Cell, together with incubation resources and a student‑run Entrepreneurship Development Cell, assists with patenting, licensing, and venture formation.
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