OncoMAPPER, is a next-generation, computationally engineered bioinformatics pipeline designed to revolutionize precision drug discovery in gastric cancer by targeting gut microbiome-associated enzymes. Current approaches often overlook the microbial component of cancer progression, leading to suboptimal therapeutic outcomes. Despite growing evidence of microbial influence in oncogenesis, there is a significant gap in targeted, enzyme-level interventions within the microbiome space particularly using scalable in silico frameworks. This creates a major bottleneck in early-stage therapeutic screening and microbiome-modulating drug development.
SOLUTION:
OncoMAPPER integrates comprehensive metagenomic profiling, enzyme target identification, and advanced structure-based virtual screening into a seamless computational platform. The pipeline leverages precision enzyme prediction, KEGG-annotated microbial enzymes and high-throughput docking simulations to screen for potent inhibitory ligands—highlighting key binders with optimal hydrogen bonding (up to 7 bonds) and favorable binding energies (and favorable binding energies (< -7.5 kcal/mol). Post-dock analysis, including functional annotation, confirms enzymatic suppression, enabling prioritization of high-affinity candidates.
OUTCOMES:
- A validated dataset of oncogenic microbial enzymes and inhibitory ligands with nanomolar binding potentials.
- Demonstrated suppression of enzyme functionality post-docking, offering proof-of-concept for microbiome-targeted therapy.
- A scalable, automation-ready framework for early-stage therapeutic pipeline acceleration, reducing lab screening costs by up to 60%.
- A novel computational toolset ready for integration into pharma pipelines or academic collaborations, with real-world applications in cancer precision medicine.
- Establishment of a data-driven foundation for clinical biomarker identification and non-invasive microbiome diagnostics.
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About the Entrant
- Name:Subhikssha B
- Type of entry:teamTeam members:
- ESWARA G R
- JOYCE ANGEL JAYAKUMAR
- Software used for this entry:AutoDock, PyMOl, PYrx, Biovia Discovery Studio
- Patent status:none