01-05 December 2025
INCOIS, Hyderabad, India.
| Abstract Submission No. | ABS-01-0246 |
| Title of Abstract | Computational mining of Indian marine metagenomic data for novel antimicrobial peptides targeting ESKAPE pathogens |
| Authors | Sreelakshmi K V*, Dr. Budheswar Dehury |
| Organisation | Manipal School of Life Sciences, MAHE, Manipal, Karnataka |
| Address | Manipal School of Life Sciences, MAHE, Manipal Manipal, Karnataka, India Pincode: 576104 E-mail: sreelakshmi.mslsmpl2023@learner.manipal.edu |
| Country | India |
| Presentation | Poster |
| Abstract | The Indian Ocean is a cradle of immense microbial diversity, yet its potential for translational applications to address urgent human health challenges remains largely untapped. Antimicrobial resistance (AMR), driven by the limited efficacy of existing antibiotics against multidrug-resistant bacteria, especially the notorious ESKAPE pathogens, is among the most pressing global threats. Harnessing the power of marine microbiomes, our study explores the Indian Ocean as a frontier for the discovery of novel antimicrobial peptides (AMPs) with therapeutic promise. A total of five high-resolution shotgun metagenomic datasets, comprising 59 samples from diverse Indian marine habitats, including sediments, coral reefs, sponges, and seawater, were analyzed in search of potent therapeutic peptide targeting the outer membrane of ESKAPE pathogens. Quality-controlled reads were assembled with MEGAHIT, and small open reading frames were predicted using MetaProdigal, yielding a non-redundant peptide library. To identify high-confidence candidates, we employed a machine learning pipeline integrating six prediction tools i.e. AMPScanner v2, AMPLify, amPEPpy, AI4AMP, ampir, and APIN, where peptides consistently predicted across all tools were retained, reducing false positives. Biophysical filtering prioritized cationic, amphipathic peptides with membrane-disruptive potential. AlphaFold3-generated 3D structures of top candidates were subjected to all-atoms molecular dynamics simulations in gram-negative membrane mimetic models. Our integrative pipeline identified 51,185 putative AMPs, with ten shortlisted for strong membrane-active features. Two lead peptides, c_AMP_1 and c_AMP_2, revealed distinct disruption mechanisms: c_AMP_1 remained surface-aligned in an α-helical conformation, destabilizing membranes via a carpet-like mechanism, while c_AMP_2 embedded into the bilayer, inducing curvature and thinning consistent with toroidal pore formation. Both peptides showed remarkable stability, with arginine and tryptophan residues acting as anchors for membrane perturbation. By bridging Indian Ocean microbial diversity with computational bioprospecting, this work highlights the translational potential of ocean science to combat AMR. |
| Are you part of IIOE-2 endorsed project | no |
| Keywords | antimicrobial resistance, antimicrobial peptides, machine learning, drug resistance, Indian ocean |
| For Awards | yes |
| Date Of Birth | 01-05-2001 |
| ECSN Registration Number | IIOE2-ECSN-0200 |