Identification and molecular characterization of drug targets of methicillin resistant Staphylococcus aureus

Antimicrobial resistance is a major world health concern and drug-resistant Staphylococcus aureus is a serious threat. Due to the emergence of multidrug-resistant bacterial strains, there is an urgent need to develop novel drug targets to meet the challenge of multidrug-resistant organisms. The main objective of the current study was to determine molecular targets against S. aureus using by computational approach. S. aureus was cultured in brain heart infusion broth medium and MRSA (Methicillin resistant S. aureus) protein was extracted acetone-sodium dodecyl sulfate method. The cell lysate was treated with various antibiotics and proteinase K stable proteins were analyzed. The molecular weight of Geninthiocin-targeted protein of interest in S. aureus ranged from 46 to 50 kDa. A prominent protein band in SDS-PAGE indicated that the protein corresponding 50 kDa was resistant against proteinase K. The SDS-PAGE separated sample was excised and trypsinated, and the peptides were characterized using Nano Liquid Chromatography with tandem mass spectrometry (LC-MS/MS) analysis. Spectrum with clusters of molecular peptides and peptide fragments ranging from 110.0716 to 1002.7093 mass/charge ratio (m/z) were displayed against intensity or relative abundance in the excised gel band. The spectral data from nano LC-MS/MS was subjected to mascot search in the NCBIprot database (taxonomy-bacteria (eubacteria), resulting in seven bacterial proteins. Geninthiocin target proteins were determined against MRSA. To conclude, antibiotic target proteins were identified using a machine learning approach and these targets may have a lot of applications in developing a novel lead molecule against drug-resistant bacteria