Mining for Drug and Vaccine Candidates Against Stenotrophomonas maltophilia Using In silico Pan proteomic Analyses

Abstract

Computational techniques have demonstrated their effectiveness and strength in assisting with vaccine, drug discovery and development for bacterial pathogens during the past thirty years. The current dissertation incorporates computer-aided vaccine and drug design applications in the endeavor to identify potential vaccines and drug targets in the sequenced proteome of Stenotrophomonas maltophilia. This multi-drug-resistant Gram-negative bacterium has been identified as the causal agent in respiratory tract infections, endocarditis, bloodstream infection, meningitis, and urinary system infections, primarily in immunocompromised people. However, there is still no approved vaccine or medicine to tackle the diseases caused by this pathogen. As the antibiotics resistant spectrum of S. maltophilia is constantly expanding, development of effective therapeutic agents is urgently needed to overcome the resistance imposed by bacterial pathogens. The current work employed in silico subtractive proteomic strategy to sort pathogen specific proteins with high probability as drug and vaccine candidates based on set parameters. In addition, various computational methods such as comparative structure modelling, molecular docking, and molecular dynamics simulation are applied to learn more about the structural and functional insights of biological systems that were shortlisted in line with screened antigens and small drug molecules. This dissertation's first chapter provides background information and motivation for the current study goals by offering an overview of the pathogen S. maltophilia. The theoretical underpinning of the computational approaches and analyses used to identify antigenic vaccine peptides and druggable protein targets/drugs are the subjects of the second chapter. The third chapter deals with the designing of an efficient multi-epitope peptide vaccine against Stentrophomonas maltophilia, a silently rising superbug. The reverse vaccinology technique is applied in this study to locate potential vaccine antigens using the fully sequenced proteome of S. maltophilia. The limitations of traditional subunit and peptide vaccines are taken into consideration when constructing the vaccine utilizing the computational framework. The immune-dominant B cell inferred T-cell antigenic epitopes of the prioritized vaccine proteins are connected to form a multi-epitope peptide vaccine. The designed multi-epitope vaccine construct was docked into TLR4 receptor protein to unveil its binding conformation. It demonstrated that the vaccine had potent interactions and secure binding positions with the receptor. Complex stability was demonstrated by molecular dynamics simulations, which corroborated the docking outcomes. Immune responses to designed vaccine were robust when simulated computationally. These ix encouraging findings could significantly aid in the creation of an efficacious S. Maltophilia vaccine. The fifth chapter discusses the uncovering of promising and all-inclusive drug targets for S. maltophilia using a comprehensive subtractive proteomics technique for a representative strain of the bacterium. Subtractive proteome characterization identified the pathogen-specific enzyme dtdp-4-dehydrorhamnose reductase RmlD from the dTDP Rhamnose pathway as a promising drug target against stenotrophomonas maltophilia. The target RmID is a potent target for new antibacterial therapeutics because it is the last enzyme involved in the production of L-rhamnose, which is a critical constituent of many bacterial pathogens' cell walls and capsules. Given the absence of the target protein's experimental structure, a homology modelling strategy was utilized for structure prediction. Different analysis servers, including ERRAT, PDBSum, Verify3D, and ProsA, were used to further validate the results of homology modelling. Through literature research and multiple sequence alignment, the active site of RmlD was identified and used in molecular docking with the library of selected inhibitors. Furthermore, MD simulations for the best-docked complex were run at 200 nanoseconds (ns) to investigate the protein's dynamic behaviour, binding mode, inhibitor stability upon binding, and the possibility of using the compound as a lead. The study's findings could be used to identify new therapeutic drug targets for S. maltophilia. To summarize, the findings of this dissertation may lay new groundwork for the development of novel therapeutics (vaccine and drug) against the notorious S. maltophilia.