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A High-Yield Streptomyces Transcription-Translation Toolkit for Synthetic Biology and Natural Product Applications
Published on: September 10, 2021
Genome-guided discovery and computational prioritization of next generation drug development from Streptomyces sp.
Veilumuthu Pattapulavar1, Manisha Shah2, Praisy Joy Bell I3
1Department of Biomedical Sciences, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India.
Abstract:
The rapid emergence of antimicrobial resistance necessitates the discovery of new bioactive metabolites and integrative strategies capable of accelerating antibiotic discovery. In this study, we employed a genome-guided experimental and computational workflow to investigate the antibacterial potential of Streptomyces sp. VITGV156 (MCC 4965). The ethyl acetate crude extract exhibited concentration-dependent antibacterial activity against Gram-positive and Gram-negative bacteria, including Bacillus subtilis (MTCC 2756), Staphylococcus aureus (MTCC 737), Escherichia coli (MTCC 1687), and Klebsiella pneumoniae (MTCC 109), with inhibition zones ranging from 24.33 ± 0.47 mm to 25.33 ± 0.47 mm at 100 μL, while the DMSO control showed no activity. Metabolomic profiling using GC-MS and LC-MS confirmed the production of diverse bioactive metabolites, providing experimental evidence supporting genome-mined biosynthetic potential. A total of 29 predicted secondary metabolites were subsequently prioritized using structure-based virtual screening against two clinically relevant antibiotic-resistance targets: the fluoroquinolone resistance protein QnrB1 and the tigecycline-inactivating monooxygenase Tet(X4). Docking validation confirmed the robustness of the computational protocol (RMSD = 1.71 Å), and several metabolites exhibited strong binding affinities (-5.0 to -12.3 kcal/mol). PASS bioactivity prediction and toxicity screening identified vicenistatin, prejadomycin, and ectoine as top candidates. Exhaustive docking and 200-ns molecular dynamics simulations further demonstrated stable protein-ligand interactions, particularly for vicenistatin, which enhanced structural stability of both target proteins. Overall, this study integrates antibacterial assays, metabolomics, genome mining, and molecular modeling to bridge the gap between genotype and phenotype, providing a rational pipeline for prioritizing natural products targeting antibiotic-resistance mechanisms. These findings highlight Streptomyces sp. VITGV156 as a promising source of antibacterial metabolites and support future purification and experimental validation of prioritized compounds.
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