Identification of potential drug targets and vaccine candidates in Clostridium botulinum using subtractive genomics approach

In silico identification and characterization of potential druggable targets among hypothetical proteins of Leptospira interrogans serovar Copenhageni: a comprehensive bioinformatics approach

Leptospirosis is one of the neglected zoonosis, affecting human and animal populations worldwide. Reliable effective therapeutics and concerns to look for more research into the molecular analysis of its genome is therefore needed. In the genomic pool of the Leptospira interrogans many hypothetical proteins are still uncharacterized. In the current research, we performed extensive in silico analysis to prioritize the potential hypothetical proteins of L. interrogans serovar Copenhageni via stepwise reducing the available hypothetical proteins (Total 3606) of the assembly to only 15, based on non-homologous to homosapien, essential, functional, virulent, cellular localization. Out of them, only two proteins WP_000898918.1 (Hypothetical Protein 1) & WP_001014594.1 (Hypothetical Protein 2) were found druggable and involved in protein-protein interaction network. The 3 D structures of these two target proteins were predicted via ab initio homology modeling followed by structures refinement and validation, as no structures were available till date. The analysis also revealed that the functional domains, families and protein-protein interacting partners identified in both proteins are crucial for the survival of the bacteria. The binding cavities were predicted for both the proteins through blind and specific protein-ligand docking with their respective ligands and inhibitors and were found to be in accordance with the druggable sites predicted by DoGSiteScorer. The docking interactions were found within the active functional domains for both the proteins while for Hypothetical Protein 2, the same residues were involved in interactions with Cytidine-5'-triphosphate in blind and specific docking. Furthermore, the simulations of molecular dynamics and free binding energy revealed the stable substrate binding and efficient binding energies, and were in accordance to our docking results. The work predicted two unique hypothetical proteins of L. interrogans as a potential druggable targets for designing of inhibitors for them.Communicated by Ramaswamy H. Sarma.

Design of multi-epitope based vaccine against Mycobacterium tuberculosis: a subtractive proteomics and reverse vaccinology based immunoinformatics approach

Tuberculosis is an airborne transmissible disease caused by Mycobacterium tuberculosis that infects millions of lives worldwide. There is still no single comprehensive therapy or preventative available for the lethal illness. Currently, the available vaccine, BCG is ineffectual in preventing the prophylactic adult pulmonary TB and reactivation of latent tuberculosis. Therefore, this investigation was intended to design a new multi-epitope vaccine that can address the existing problems. The subtractive proteomics approach was implemented to prioritize essential, virulence, druggable, and antigenic proteins as suitable vaccine candidates. Furthermore, a reverse vaccinology-based immunoinformatics technique was employed to identify potential B-cell, helper T lymphocytes (HTL), and cytotoxic T lymphocytes (CTL) epitopes from the target proteins. Immune-stimulating adjuvant, linkers, and PADRE (Pan HLA-DR epitopes) amino acid sequences along with the selected epitopes were used to construct a chimeric multi-epitope vaccine. The molecular docking and normal mode analysis (NMA) were carried out to evaluate the binding mode of the designed vaccine with different immunogenic receptors (MHC-I, MHC-II, and Tlr4). In addition, the MD simulation, followed by essential dynamics study and MMPBSA analysis, was carried out to understand the dynamics and stability of the complexes. In-silico cloning was accomplished using E.coli as an expression system to express the designed vaccine successfully. Finally, the immune simulation study has foreseen that our designed vaccine could induce a significant immune response by elevation of different immunoglobulins in the host. However, there is an imperative need for the experimental validation of the designed vaccine in animal models to confer effectiveness and safety.HIGHLIGHTSMulti-epitope based vaccine was designed against Mycobacterium tuberculosis using subtractive proteomics and Immunoinformatics approach.The vaccine was found to be antigenic, non-allergenic, immunogenic, and stable based on in-silico prediction.Population coverage analysis of the proposed vaccine predicts an effective response in the world population.The molecular docking, MD simulation, and MM-PBSA study confirm the stable interaction of the vaccine with immunogenic receptors.In silico cloning and immune simulation of the vaccine demonstrated its successful expression in E.coli and induction of immune response in the host. Communicated by Ramaswamy H. Sarma.

Subtractive genomics profiling for potential drug targets identification against Moraxella catarrhalis

Moraxella catarrhalis (M. catarrhalis) is a gram-negative bacterium, responsible for major respiratory tract and middle ear infection in infants and adults. The recent emergence of the antibiotic resistance M. catarrhalis demands the prioritization of an effective drug target as a top priority. Fortunately, the failure of new drugs and host toxicity associated with traditional drug development approaches can be avoided by using an in silico subtractive genomics approach. In the current study, the advanced in silico genome subtraction approach was applied to identify potential and pathogen-specific drug targets against M. catarrhalis. We applied a series of subtraction methods from the whole genome of pathogen based on certain steps i.e. paralogous protein that have extensive homology with humans, essential, drug like, non-virulent, and resistant proteins. Only 38 potent drug targets were identified in this study. Eventually, one protein was identified as a potential new drug target and forwarded to the structure-based studies i.e. histidine kinase (UniProt ID: D5VAF6). Furthermore, virtual screening of 2000 compounds from the ZINC database was performed against the histidine kinase that resulted in the shortlisting of three compounds as the potential therapeutic candidates based on their binding energies and the properties exhibited using ADMET analysis. The identified protein gives a platform for the discovery of a lead drug candidate that may inhibit it and may help to eradicate the otitis media caused by drug-resistant M. catarrhalis. Nevertheless, the current study helped in creating a pipeline for drug target identification that may assist wet-lab research in the future.

Pathogenomic in silico approach identifies NSP-A and Fe-IIISBP as possible drug targets in Neisseria Meningitidis MC58 and development of pharmacophores as novel therapeutic candidates

Meningitis creates a life-threatening clinical crisis. Moreover, the administered antibiotics result into multi-drug resistance, thereby necessitating development of alternative therapeutic strategies. This study aimed at identifying novel-drug targets in Neisseria meningitidis and therapeutic molecules which can be exploited for the treatment of meningitis. Novel targets were identified by applying a pathogenomic approach involving protein data-set mining, subtractive channel analysis and subsequent qualitative analysis comprising of in silico pharmacokinetics, molecular docking and pharmacophore generation. Pathogenomic studies revealed Neisserial Surface Protein A (NSP-A) and Iron-III-Substrate Binding Protein (Fe-IIISBP) as potential targets. Two pharmacophore models comprising of 2-(biaryl) carbapenems, efavirenz, praziquantel and pyrimethamine for NSP-A and 2-(biaryl) carbapenems, trimipramine and pyrimethamine for Fe-IIISBP, showed successful docking, followed drug-likeness criteria and generated pharmacophore model with a score of 8.08 and 8.818, respectively, which had further been docked to the target stably. Thus, our study identifies NSP-A and Fe-IIISBP as novel targets in Neisseria meningitidis for which 2-(biaryl) carbapenems, efavirenz, praziquantel, trimipramine and pyrimethamine may be employed for effective treatment of meningitis.

Computational Design of a Multi-Epitope Vaccine Against Porphyromonas gingivalis

Porphyromonas gingivalis is a Gram-negative pathogenic bacterium associated with chronic periodontitis. The development of a chimeric peptide-based vaccine targeting this pathogen could be highly beneficial in preventing oral bone loss as well as other severe gum diseases. We applied a computational framework to design a multi-epitope-based vaccine candidate against P. gingivalis. The vaccine comprises epitopes from subunit proteins prioritized from the P. gingivalis reference strain (P. gingivalis ATCC 33277) using several reported vaccine properties. Protein-based subunit vaccines were prioritized through genomics techniques. Epitope prediction was performed using immunoinformatic servers and tools. Molecular modeling approaches were used to build a putative three-dimensional structure of the vaccine to understand its interactions with host immune cells through biophysical techniques such as molecular docking simulation studies and binding free energy methods. Genome subtraction identified 18 vaccine targets: six outer-membrane, nine cytoplasmic membrane-, one periplasmic, and two extracellular proteins. These proteins passed different vaccine checks required for the successful development of a vaccine candidate. The shortlisted proteins were subjected to immunoinformatic analysis to map B-cell derived T-cell epitopes, and antigenic, water-soluble, non-toxic, and good binders of DRB1*0101 were selected. The epitopes were then modeled into a multi-epitope peptide vaccine construct (linked epitopes plus adjuvant) to enhance immunogenicity and effectively engage both innate and adaptive immunity. Further, the molecular docking approach was used to determine the binding conformation of the vaccine to TLR2 innate immune receptor. Molecular dynamics simulations and binding free energy calculations of the vaccine–TLR2 complex were performed to highlight key intermolecular binding energies. Findings of this study will be useful for vaccine developers to design an effective vaccine for chronic periodontitis pathogens, specifically P. gingivalis.

Prediction of Potential Drug Targets and Vaccine Candidates Against Antibiotic-Resistant Pseudomonas aeruginosa

Pseudomonas aeruginosa is one of the leading causes of nosocomial infections, characterized by increasing antibiotic resistance, severity and mortality. Therefore, numerous efforts have been made nowadays to identify new therapeutic targets. This study aimed to find potential drug targets and vaccine candidates in drug-resistant strains of P. aeruginosa. Extensive antibiotic-resistant and carbapenem-resistant strains of P. aeruginosa with complete genome were selected and ten common hypothetical proteins (HPs) containing more than 200 amino acids were obtained. The structural, functional and immunological predictions of these HPs were performed with the utility of bioinformatics approaches. Two common HPs (Gene ID: 2877781645 and 2877781936) among other investigated proteins were revealed as potential candidates for pharmaceutical and vaccine purposes based on structural and physicochemical properties, functional domains, subcellular localizations, signal peptides, toxicity, virulence factor, antigenicity, allergenicity and immunoinformatic predictions. The consequence of this predictive study will assist in novel drug and vaccine design through experimental investigations.

Identification of a Novel Therapeutic Target against XDR Salmonella Typhi H58 Using Genomics Driven Approach Followed Up by Natural Products Virtual Screening

Typhoid fever is caused by a pathogenic, rod-shaped, flagellated, and Gram-negative bacterium known as Salmonella Typhi. It features a polysaccharide capsule that acts as a virulence factor and deceives the host immune system by protecting phagocytosis. Typhoid fever remains a major health concern in low and middle-income countries, with an estimated death rate of ~200,000 per annum. However, the situation is exacerbated by the emergence of the extensively drug-resistant (XDR) strain designated as H58 of S. Typhi. The emergence of the XDR strain is alarming, and it poses serious threats to public health due to the failure of the current therapeutic regimen. A relatively newer computational method called subtractive genomics analyses has been widely applied to discover novel and new drug targets against pathogens, particularly drug-resistant ones. The method involves the gradual reduction of the complete proteome of the pathogen, leading to few potential and novel drug targets. Thus, in the current study, a subtractive genomics approach was applied against the Salmonella XDR strain to identify potential drug targets. The current study predicted four prioritized proteins (i.e., Colanic acid biosynthesis acetyltransferase wcaB, Shikimate dehydrogenase aroE, multidrug efflux RND transporter permease subunit MdtC, and pantothenate synthetase panC) as potential drug targets. Though few of the prioritized proteins are treated in the literature as the established drug targets against other pathogenic bacteria, these drug targets are identified here for the first time against S. Typhi (i.e., S. Typhi XDR). The current study aimed at drawing attention to new drug targets against S. Typhi that remain largely unexplored. One of the prioritized drug targets, i.e., Colanic acid biosynthesis acetyltransferase, was predicted as a unique, new drug target against S. Typhi XDR. Therefore, the Colanic acid was further explored using structure-based techniques. Additionally, ~1000 natural compounds were docked with Colanic acid biosynthesis acetyltransferase, resulting in the prediction of seven compounds as potential lead candidates against the S. Typhi XDR strain. The ADMET properties and binding energies via the docking program of these seven compounds characterized them as novel drug candidates. They may potentially be used for the development of future drugs in the treatment of Typhoid fever.

Pan-proteome profiling of emerging and re-emerging zoonotic pathogen Orientia tsutsugamushi for getting insight into microbial pathogenesis

With the occurrence and evolution of antibiotic and multidrug resistance in bacteria most of the existing remedies are becoming ineffective. The pan-proteome exploration of the bacterial pathogens helps to identify the wide spectrum therapeutic targets which will be effective against all strains in a species. The current study is focused on the pan-proteome profiling of zoonotic pathogen Orientia tsutsugamushi (Ott) for the identification of potential therapeutic targets. The pan-proteome of Ott is estimated to be extensive in nature that has 1429 protein clusters, out of which 694 were core, 391 were accessory, and 344 were unique. It was revealed that 622 proteins were essential, 222 proteins were virulent factors, and 42 proteins were involved in antibiotic resistance. The potential therapeutic targets were further classified into eleven broad classes among which gene expression and regulation, transport, and metabolism were dominant. The biological interactome analysis of therapeutic targets revealed that an ample amount of interactions were present among the proteins involved in DNA replication, ribosome assembly, cellwall metabolism, cell division, and antimicrobial resistance. The predicted therapeutic targets from the pan-proteome of Ott are involved in various biological processes, virulence, and antibiotic resistance; hence envisioned as potential candidates for drug discovery to combat scrub typhus.

Computational identification of putative common genomic drug and vaccine targets in Mycoplasma genitalium

Mycoplasma genitalium is an obligate intracellular bacterium that is responsible for several sexually transmitted infections, including non-gonococcal urethritis in men and several inflammatory reproductive tract syndromes in women. Here, we applied subtractive genomics and reverse vaccinology approaches for in silico prediction of potential vaccine and drug targets against five strains of M. genitalium. We identified 403 genes shared by all five strains, from which 104 non-host homologous proteins were selected, comprising of 44 exposed/secreted/membrane proteins and 60 cytoplasmic proteins. Based on the essentiality, functionality, and structure-based binding affinity, we finally predicted 19 (14 novel) putative vaccine and 7 (2 novel) candidate drug targets. The docking analysis showed six molecules from the ZINC database as promising drug candidates against the identified targets. Altogether, both vaccine candidates and drug targets identified here may contribute to the future development of therapeutic strategies to control the spread of M. genitalium worldwide.

A systematic strategy for the investigation of vaccines and drugs targeting bacteria

Infectious and epidemic diseases induced by bacteria have historically caused great distress to people, and have even resulted in a large number of deaths worldwide. At present, many researchers are working on the discovery of viable drug and vaccine targets for bacteria through multiple methods, including the analyses of comparative subtractive genome, core genome, replication-related proteins, transcriptomics and riboswitches, which plays a significant part in the treatment of infectious and pandemic diseases. The 3D structures of the desired target proteins, drugs and epitopes can be predicted and modeled through target analysis. Meanwhile, molecular dynamics (MD) analysis of the constructed drug/epitope-protein complexes is an important standard for testing the suitability of these screened drugs and vaccines. Currently, target discovery, target analysis and MD analysis are integrated into a systematic set of drug and vaccine analysis strategy for bacteria. We hope that this comprehensive strategy will help in the design of high-performance vaccines and drugs.

Dtar-Finder: program for drug target identification and characterization in bacteria

The drug target identification is the primary step for drug discovery. Recent development of computational techniques and availability of sequencing data has provided numerous opportunities for target identification but very few of them are fully automated. Here, we have developed a Perl program named Dtar-Finder for drug target identification and its characterization. Dtar-Finder predicts the drug targets which are essential to pathogen and non homologous to human, essential human anti-targets and gut microflora. This program is divided in 6 modules where modules 1-4 extract drug targets while module 5-6 predicts druggability and broad spectrum ability of identified candidates. The performance of this program in terms of sensitivity and specificity is calculated where specificity score was better compare to sensitivity score. Further, we have tested our script on C. botulinum (3572 proteins) and 35 potential drug targets have been identified. Out of which 16 broad spectrums candidates were predicted whereas 8 candidates are found to be druggable whiles remaining are considered to be 'novel'. These drug targets were cross-validated through literature showing 77.14% accuracy. Thus, the idea behind this work was to develop a fast, robust and generic program capable of finding drug targets in bacteria, which has been fulfilled satisfactorily.