Deciphering the Dynamics of Therapeutic Proteins from Nosocomial Pathogens

Abstract

Computer aided vaccine and drug designing are emerged as powerful approaches for over three decades playing critical roles in the development of new vaccines and drug molecules for bacterial pathogens, respectively. The present dissertation focused primarily on the applications of these two fields to make available all possible vaccines and drug targets in the sequenced genome of selected nosocomial pathogens especially the Acinetobacter baumannii. Further, computational structure modeling, structure based high throughput screening, molecular simulations and binding free energies calculation studies were also taken into account to elucidate the structural and functional characteristics of shortlisted biological systems in context of screened antigens and small drug molecules. The first chapter of this dissertation addresses a general introduction of nosocomial infections and nosocomial pathogens with emphasis on A. baumannii, thus providing the background and motivation for the current research objectives. The second chapter is focused on the theoretical details of computational techniques and analysis employed for identification of antigenic peptides and druggable protein targets/drugs. The third chapter describes a multi-epitope peptide vaccine designing for tigecycline-resistant A. baumannii superbug. In this chapter, a comprehensive computational framework is designed keeping in view the limitations of conventional subunit and peptide vaccines. A multi-epitope peptide vaccine is formulated by linking the shortlisted B-cell derived T-cell antigenic eptiopes from prioritized vaccine proteins that fulfilled the requirements of appropiate vaccine candidates. Further, molecular docking and molecular dynamics (MD) simulation studies have been undertaken to probe the binding conformation and dynamics of the modeled peptide with respect to the TLR4 receptor. In the fourth chapter, a novel virulome based reverse vaccinology (RV) approach is demonstrated to predict broad-spectrum antigenic peptides harboring proteins for induction of targeted immune responses against multi-drug resistant A. baumannii. The fifth chapter deals with the identification of promising and broad-spectrum drug targets for A. baumannii using an extensive comparative subtractive proteomics methodology for 35 strains of A. baumannii. In total, 10 protein targets: KdsA, KdsB, LpxA, LpxC, LpxD, GpsE, PhoB, UvrY, KdpE and OmpR were identified as ideal candidates for designing novel antibiotics. Further in this chapter, KdsA protein from 3-deoxy-Dmanno-octulosonate (KDO) biosynthesis pathway was used as a receptor macromolecule in computer aided drug designing applications of structure modeling, virtual screening of Asinex antibacterial library, dynamics understanding and binding free energy calculations. The sixth chapter of the dissertation focuses majorly on KdsB enzyme dynamics in the presence of an inhibitor in its cavity and binding free energy calculations. D-alanine-D-alanine ligase (Ddl) enzyme of the peptidoglycan biosynthesis machinery was targeted for screening of potent antibacterial drugs in the seventh chapter. Radial Distribution Function (RDF) and an in-house developed Axial Frequency Distribution (AFD) demonstrated Lys176 and Trp177 as critical residues from the enzyme active site for binding, anchoring and bridging strong hydrogen and hydrophobic contacts with the virtually screened inhibitor. In the eighth chapter, MurC ligase enzyme of the peptidoglycan biosynthesis was targeted to block its catalytic mechanism by identifying the most promising inhibitor for the Ligand binding (LB) domain of the enzyme. The complex was further analyzed for free energies calculation using MM(PB/GB)SA and WaterSwap. At residue level, RDF and AFD illustrated Asp334 as the most critical amino acid that drives recognition and binding of the shortlisted compounds. An ethyl pyridine substituted 3-cyanothiophene was predicted in the second last chapter as the most active inhibitor for A. baumannii MurF ligase enzyme that catalyzes the final cytoplasmic step of bacterial peptidoglycan biosynthesis. Protein active site residues: Thr42 and Asp43 were found to show high affinity for inhibitor binding during simulation studies. The final chapter of the dissertation revealed α4-β5-α5 face of A. baumannii BfmR enzyme as the hot spot for future antibiotics designing. In conclusion, the findings of this dissertation could provide new foundations for discovery of novel therapeutics against the notorious A. baumannii.