Genomics driven approach for identification of novel therapeutic targets in Salmonella enterica

Unveiling a Comprehensive Multi-epitope Subunit Vaccine Strategy Against Salmonella subsp. enterica: Bridging Core, Subtractive Proteomics, and Immunoinformatics

Salmonella subsp. enterica (SE) presents a significant global health challenge in both developed and developing countries. Current SE vaccines have limitations, targeting specific strains and demonstrating moderate efficacy in adults, while also being unsuitable for young children and often unaffordable in regions with lower income levels where the disease is prevalent. To address these challenges, this study employed a computational approach integrating core proteomics, subtractive proteomics, and immunoinformatics to develop a universal SE vaccine and identify potential drug targets. Analysis of the core proteome of 185 SE strains revealed 1964 conserved proteins. Subtractive proteomics identified 9 proteins as potential vaccine candidates and 41 as novel drug targets. Using reverse vaccinology-based immunoinformatics, four multi-epitope-based subunit vaccine constructs (MESVCs) were designed, aiming to stimulate cytotoxic T lymphocyte, helper T lymphocyte, and linear B lymphocyte responses. These constructs underwent comprehensive evaluations for antigenicity, immunogenicity, toxicity, hydropathicity, and physicochemical properties. Predictive modeling, refinement, and validation were conducted to determine the secondary and tertiary structures of the SE-MESVCs, followed by docking studies with MHC-I, MHC-II, and TLR4 receptors. Molecular docking assessments showed favorable binding with all three receptors, with SE-MESVC-4 exhibiting the most promising binding energy. Molecular dynamics simulations confirmed the binding affinity and stability of SE-MESVC-4 with the TLR4/MD2 complex. Additionally, codon optimization and in silico cloning verified the efficient translation and successful expression of SE-MESVC-4 in Escherichia coli (E. coli) str. K12. Subsequent in silico immune simulation evaluated the efficacy of SE-MESVC-4 in triggering an effective immune response. These results suggest that SE-MESVC-4 may induce both humoral and cellular immune responses, making it a potential candidate for an effective SE vaccine. However, further experimental investigations are necessary to validate the immunogenicity and efficacy of SE-MESVC-4, bringing us closer to effectively combating SE infections.

Manipulation of host immune defenses by effector proteins delivered from multiple secretion systems of Salmonella and its application in vaccine research

Salmonella is an important zoonotic bacterial species and hazardous for the health of human beings and livestock globally. Depending on the host, Salmonella can cause diseases ranging from gastroenteritis to life-threatening systemic infection. In this review, we discuss the effector proteins used by Salmonella to evade or manipulate four different levels of host immune defenses: commensal flora, intestinal epithelial-mucosal barrier, innate and adaptive immunity. At present, Salmonella has evolved a variety of strategies against host defense mechanisms, among which various effector proteins delivered by the secretory systems play a key role. During its passage through the digestive system, Salmonella has to face the intact intestinal epithelial barrier as well as competition with commensal flora. After invasion of host cells, Salmonella manipulates inflammatory pathways, ubiquitination and autophagy processes with the help of effector proteins. Finally, Salmonella evades the adaptive immune system by interfering the migration of dendritic cells and interacting with T and B lymphocytes. In conclusion, Salmonella can manipulate multiple aspects of host defense to promote its replication in the host.

Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions

Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.

Chlamydia trachomatis core genome data mining for promising novel drug targets and chimeric vaccine candidates identification

Chlamydia trachomatis is involved in most sexually transmitted diseases. The species has emerged as a major public health threat due to its multidrug-resistant capabilities, and new therapeutic target inferences have become indispensable to combat its pathogenesis. However, no commercial vaccine is yet available to treat the C. trachomatis infection. In this study, we used the publicly available complete genome sequences of C. trachomatis and performed comparative proteomics and reverse vaccinology analyses to explore novel drug and vaccine targets against this devastating pathogen. We identified 713 core proteins from 71 C. trachomatis complete genome sequences and prioritized them based on their cellular essentiality, virulence, and available antibiotic resistance. The analyses led to the identification of 16 pathogen-specific proteins with no resolved 3D structures, though holding significant druggable potential. The sequences of the three shortlisted candidates' membrane proteins were used for designing vaccine constructs. The antigenicity, toxicity, and solubility profile-based lead epitopes were prioritized for multi-epitope-based vaccine constructs in combination with specific linkers, PADRE sequences, and molecular adjuvants for immunogenicity enhancement. The molecular-level interactions of the prioritized vaccine construct with human immune cells HLA and TLR4/MD were validated by molecular docking and molecular dynamic simulation analyses. Furthermore, the cloning and expression potential of the lead vaccine construct was predicted in the E. coli cloning vector system. Additional testing and experimental validation of these multi-epitope constructs appear promising against C. trachomatis-mediated infection.

Repurposing of FDA approved drugs and their validation against potential drug targets for Salmonella enterica through molecular dynamics simulation

Salmonella is a widely distributed pathogen causing infection of intestinal tract, typhoid, and paratyphoid fever. Number of drugs was developed against salmonella, but in the last few decades due to the emergence of drug resistant strains, most of these drugs became dormant. As a result Salmonellosis emerges as a trivial cause of human mortality worldwide; therefore, there is an urgent need for unexploited drug targets and drugs to treat Salmonellosis. As development of new drug molecules is very time consuming and costly, drug repurposing is in consideration as a better alternative. With the aim to identify a new drug molecule against the Salmonella through repurposing approach, we utilized 14 well reported druggable targets known to play a vital role in the life cycle of pathogens. These targets were used to screen DrugBank and got 53 FDA approved drugs against them. To find the interaction between considered target proteins and screened drugs, molecular docking was performed. Fourteen docked drug-target complexes with reasonable binding affinities were subjected to Molecular Dynamics Simulation (MDS) at 150 ns, using Amber18. At the end MMPBSA and MMGBSA calculations were performed for all stable complexes and finally, got 3 precise and favourable complexes, i.e. ArcB-Cefpiramide, MrcB-Cefoperazone, and PhoQ-Carindacillin. Rigorous structural and energetic analysis for these complexes validates the potential of drug molecules to act as therapeutic drugs against Salmonella enterica. With this study we hypothesize that the drugs Cefpiramide (DB00430), Cefoperazone (DB01329) and Carindacillin (DB09319) will be the good repurposed-drugs for the treatment of Salmonellosis.Communicated by Ramaswamy H. Sarma.

Identification of Therapeutic Drug Target of Stenotrophomonas maltophilia Through Subtractive Genomic Approach and in-silico Screening Based on 2D Similarity Filtration and Molecular Dynamic Simulation

Background:

Stenotrophomonas maltophilia is a multi-drug resistant, gram-negative bacterium that causes opportunistic infections and is associated with high morbidity and mortality in severely immunocompromised individuals.

Aim:

The study aimed to find out the drug target and a novel inhibitor for Stenotrophomonas maltophilia.

Objectives:

The current study focused on identifying specific drug targets by subtractive genomes analysis to determine the novel inhibitor for the specified target protein by virtual screening, molecular docking, and molecular simulation approach.

Materials and Methods:

In this study, we performed a subtractive genomics approach to identify the novel drug target for S.maltophilia. After obtaining the specific target, the next step was to identify inhibitors that include calculating 2D similarity search, molecular docking, and molecular simulation for drug development for S.maltophilia.

Results:

With an efficient subtractive genomic approach, five unique targets as the impressive therapeutics founded out of 4386 protein genes. In which UDP-D-acetylmuramic (murF) was the most remarkable target. Further virtual screening, docking, and dynamics resulted in the identification of seven novel inhibitors.

Conclusion:

Further, in vitro and in vivo bioassay of the identified novel inhibitors could facilitate effective drug use against S.maltophilia.

Network-Based Metabolism-Centered Screening of Potential Drug Targets in Klebsiella pneumoniae at Genome Scale

Klebsiella pneumoniae is an opportunistic bacterial pathogen leading to life-threatening nosocomial infections. Emergence of highly resistant strains poses a major challenge in the management of the infections by healthcare-associated K. pneumoniae isolates. Thus, despite intensive efforts, the current treatment strategies remain insufficient to eradicate such infections. Failure of the conventional infection-prevention and treatment efforts explicitly indicates the requirement of new therapeutic approaches. This prompted us to systematically analyze the K. pneumoniae metabolism to investigate drug targets. Genome-scale metabolic networks (GMNs) facilitating the systematic analysis of the metabolism are promising platforms. Thus, we used a GMN of K. pneumoniae MGH 78578 to determine putative targets through gene- and metabolite-centric approaches. To develop more realistic infection models, we performed the bacterial growth simulations within different host-mimicking media, using an improved biomass formation reaction. We selected more suitable targets based on several property-based prioritization procedures. KdsA was identified as the high-ranked putative target satisfying most of the target prioritization criteria specified under the gene-centric approach. Through a structure-based virtual screening protocol, we identified potential KdsA inhibitors. In addition, the metabolite-centric approach extended the drug target list based on synthetic lethality. This revealed the importance of combined metabolic analyses for a better understanding of the metabolism. To our knowledge, this is the first comprehensive effort on the investigation of the K. pneumoniae metabolism for drug target prediction through the constraint-based analysis of its GMN in conjunction with several bioinformatic approaches. This study can guide the researchers for the future drug designs by providing initial findings regarding crucial components of the Klebsiella metabolism.

Computational screening of potential drug targets for pathogens causing bacterial pneumonia

Streptococcus pneumoniae is widely recognized as the main cause of bacterial pneumonia among all age groups. Other important gram-positive, gram-negative and atypical bacteria causing pneumonia majorly infect children and infants. Despite abundant occurrence of bacterial pneumonia, there is no specific antibiotic therapy available. On the other hand non-specific therapies are less effective and may influence bacterial resistance. Therefore, search for novel drug targets for pathogen is highly necessary. The current study suggested novel potential drug targets through the subtractive and comparative genomics approach. Putative drug targets were identified from highly virulent strain of Streptococcus pneumoniae using target identification (TiD) software and compared with other 12 pneumonia causing pathogens. The putative targets were prioritized through druggability analysis, virulence analysis, metabolic pathway enrichment followed by functional annotations and interactome network. Prioritization of 74 drug targets revealed that 42 of them were enzymes which included 29 new targets and seven chokepoint enzymes. Twenty (out of 74) potential targets are proposed as hub genes through interactome analysis and explored their significance in survival of the pathogen. Comparative analysis of 20 hub genes represents that 15 are enzymes and five are non-enzymes. Functional annotation of two chokepoint hub enzymes namely, peptidoglycan bridge formation alanyltransferase MurN (fibB) and PTS mannitol transporter subunit IIA (mltF) were significantly enriched in peptidoglycan biosynthesis and phosphotransferase system (PTS) respectively. Therefore these enzymes would be of prior interest for rational design of targeted therapy against bacterial pneumonia.

An in silico approach in identification of drug targets in Leishmania: A subtractive genomic and metabolic simulation analysis

Leishmaniasis is one of the major health issue in developing countries. The current therapeutic regimen for this disease is less effective with lot of adverse effects thereby warranting an urgent need to develop not only new and selective drug candidates but also identification of effective drug targets. Here we present subtractive genomics procedure for identification of putative drug targets in Leishmania. Comprehensive druggability analysis has been carried out in the current work for identified metabolic pathways and drug targets. We also demonstrate effective metabolic simulation methodology to pinpoint putative drug targets in threonine biosynthesis pathway. Metabolic simulation data from the current study indicate that decreasing flux through homoserine kinase reaction can be considered as a good therapeutic opportunity. The data from current study is expected to show new avenue for designing experimental strategies in search of anti-leishmanial agents.