targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis

Biophysical and biochemical characterization of Rv3405c, a tetracycline repressor protein from Mycobacterium tuberculosis

Mycobacterium tuberculosis, the causative agent of tuberculosis disease, is one among the deadliest pathogens in the world. Due to long treatment regimen, HIV co-infection, persistence of bacilli in latent form and development of XDR and TDR strains of Mtb, tuberculosis has posed serious concerns for managing the disease, and calls for discovery of new drugs and drug targets. Using a computational pipeline involving analysis of the structural models of the Mtb proteome and an analysis of the ATPome, followed by a series of filters to identify druggable proteins, solubility and length of the protein, several candidate proteins were shortlisted. From this, Rv3405c, a tetR family of DNA binding protein involved in antibiotic resistance, was identified as one of the good drug targets. Rv3405c binds to the upstream non-coding region of Rv3406 and causes repression of Rv3406 activity there by affecting the downstream processes involved in antibiotic resistance was further characterized. The Rv3405c gene was cloned; the gene product was over-expressed in E. coli and purified by Ni NTA chromatography. DNA binding studies by EMSA showed that the recombinant Rv3405c protein binds to the DNA sequence corresponding to the promoter region of Rv3406 and upon addition of tetracycline, the DNA binding activity was lost. β-galactosidase reporter assay in E. coli using both wild type and a DNA binding defective mutant protein indeed proved that Rv3405c acts as a repressor.

Identification of membrane-associated proteins with pathogenic potential expressed by Corynebacterium pseudotuberculosis grown in animal serum

Objective

Previous works defining antigens that might be used as vaccine targets against Corynebacterium pseudotuberculosis, which is the causative agent of sheep and goat caseous lymphadenitis, have focused on secreted proteins produced in a chemically defined culture media. Considering that such antigens might not reflect the repertoire of proteins expressed during infection conditions, this experiment aimed to investigate the membrane-associated proteins with pathogenic potential expressed by C. pseudotuberculosis grown directly in animal serum.

Results

Its membrane-associated proteins have been extracted using an organic solvent enrichment methodology, followed by LC–MS/MS and bioinformatics analysis for protein identification and classification. The results revealed 22 membrane-associated proteins characterized as potentially pathogenic. An interaction network analysis indicated that the four potentially pathogenic proteins ciuA, fagA, OppA4 and OppCD were biologically connected within two distinct network pathways, which were both associated with the ABC Transporters KEGG pathway. These results suggest that C. pseudotuberculosis pathogenesis might be associated with the transport and uptake of nutrients; other seven identified potentially pathogenic membrane proteins also suggest that pathogenesis might involve events of bacterial resistance and adhesion. The proteins herein reported potentially reflect part of the protein repertoire expressed during real infection conditions and might be tested as vaccine antigens.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3180-5) contains supplementary material, which is available to authorized users.

Alanine mutation of the catalytic sites of Pantothenate Synthetase causes distinct conformational changes in the ATP binding region

The enzyme Pantothenate synthetase (PS) represents a potential drug target in Mycobacterium tuberculosis. Its X-ray crystallographic structure has demonstrated the significance and importance of conserved active site residues including His44, His47, Asn69, Gln72, Lys160 and Gln164 in substrate binding and formation of pantoyl adenylate intermediate. In the current study, molecular mechanism of decreased affinity of the enzyme for ATP caused by alanine mutations was investigated using molecular dynamics (MD) simulations and free energy calculations. A total of seven systems including wild-type + ATP, H44A + ATP, H47A + ATP, N69A + ATP, Q72A + ATP, K160A + ATP and Q164A + ATP were subjected to 50 ns MD simulations. Docking score, MM-GBSA and interaction profile analysis showed weak interactions between ATP (substrate) and PS (enzyme) in H47A and H160A mutants as compared to wild-type, leading to reduced protein catalytic activity. However, principal component analysis (PCA) and free energy landscape (FEL) analysis revealed that ATP was strongly bound to the catalytic core of the wild-type, limiting its movement to form a stable complex as compared to mutants. The study will give insight about ATP binding to the PS at the atomic level and will facilitate in designing of non-reactive analogue of pantoyl adenylate which will act as a specific inhibitor for PS.

PBIT: Pipeline Builder for Identification of drug Targets for infectious diseases

Summary

PBIT (Pipeline Builder for Identification of drug Targets) is an online webserver that has been developed for screening of microbial proteomes for critical features of human drug targets such as being non-homologous to human proteome as well as the human gut microbiota, essential for the pathogen's survival, participation in pathogen-specific pathways etc. The tool has been validated by analyzing 57 putative targets of Candida albicans documented in literature. PBIT integrates various in silico approaches known for drug target identification and will facilitate high-throughput prediction of drug targets for infectious diseases, including multi-pathogenic infections.

Availability and Implementation

PBIT is freely accessible at http://www.pbit.bicnirrh.res.in/ .

Contact

thomass@nirrh.res.in.

Supplementary information

Supplementary data are available at Bioinformatics online.

Adaptive gene profiling of Mycobacterium tuberculosis during sub-lethal kanamycin exposure

Resistance to anti-tuberculosis drugs is a formidable obstacle to effective tuberculosis (TB) treatment and prevention globally. New forms of multidrug, extensive drug and total drug resistance Mycobacterium tuberculosis (Mtb) causing a serious threat to human as well as animal's population. Mtb shows diverse adaptability under stress conditions especially antibiotic treatment, however underlying physiological mechanism remained elusive. In present study, we investigated Mtb's response and adaptation with reference to gene expression during sub-lethal kanamycin exposure. Mtb were cultured under sub-lethal drug and control conditions, where half were sub-cultured every 3-days to observe serial adaptation under same conditions and the remaining were subjected to RNA-seq. We identified 98 up-regulated and 198 down-regulated responsive genes compared to control through differential analysis, of which Ra1750 and Ra3160 were the most responsive genes. In adaptive analysis, we found Ra1750, Ra3160, Ra3161, Ra3893 and Ra2492 up-regulation at early stage and gradually showed low expression levels at the later stages of drug exposure. The adaptive expression of Ra1750, Ra3160 and Ra3161 were further confirmed by real time qPCR. These results suggested that these genes contributed in Mtb's physiological adaptation during sub-lethal kanamycin exposure. Our findings may aid to edify these potential targets for drug development against drug resistance tuberculosis.

A genome-wide structure-based survey of nucleotide binding proteins in M. tuberculosis

Nucleoside tri-phosphates (NTP) form an important class of small molecule ligands that participate in, and are essential to a large number of biological processes. Here, we seek to identify the NTP binding proteome (NTPome) in M. tuberculosis (M.tb), a deadly pathogen. Identifying the NTPome is useful not only for gaining functional insights of the individual proteins but also for identifying useful drug targets. From an earlier study, we had structural models of M.tb at a proteome scale from which a set of 13,858 small molecule binding pockets were identified. We use a set of NTP binding sub-structural motifs derived from a previous study and scan the M.tb pocketome, and find that 1,768 proteins or 43% of the proteome can theoretically bind NTP ligands. Using an experimental proteomics approach involving dye-ligand affinity chromatography, we confirm NTP binding to 47 different proteins, of which 4 are hypothetical proteins. Our analysis also provides the precise list of binding site residues in each case, and the probable ligand binding pose. As the list includes a number of known and potential drug targets, the identification of NTP binding can directly facilitate structure-based drug design of these targets.

Hydrolysis of diadenosine polyphosphates. Exploration of an additional role of Mycobacterium smegmatis MutT1

Diadenosine polyphosphates (Ap_nA, n=2-6), particularly Ap₄A, are involved in several important physiological processes. The substantial sequence identity of the Nudix hydrolase domain (domain 1) of Mycobacterium smegmatis MutT1 (MsMutT1) with a known Ap₄A hydrolase suggested that MsMutT1 could also hydrolyse diadenosine polyphosphates. Biochemical experiments yielded results in conformity with this suggestion, with Ap₄A as the best among the substrates. ATP is a product in all experiments; small amounts of ADP were also observed in the experiments involving Ap₄A and Ap₆A. Hydrolysis was inhibited by fluoride ions in all cases. The mechanism of action and its inhibition in relation to Ap_nA were explored through the X-ray analysis of the crystals of the MsMutT1 complexes with Ap₅A; Ap₅A and MnCl₂; Ap₄A; ATP; and ATP.NaF.MgCl₂. The aggregation pattern of molecules in the first four crystals is similar to that found in a majority of MsMutT1-NTP crystals. Substrate molecules occupy the primary binding site and ATP occupies a site at an intermolecular interface, in the first two. ATP occupies both the sites in the third and fourth crystal. The protein-ligand interactions observed in these crystal structures lead to an explanation of the molecular mechanism of hydrolysis of Ap_nA by MsMutT1. The fifth crystal exhibits a new packing arrangement. The structure of the complex provides an explanation for the fluoride inhibition of the activity of the enzyme. It would thus appear that MutT1 has a major role involving the hydrolysis of diadenosine polyphosphates, which could be elucidated at the molecular level.

Data Intensive Genome Level Analysis for Identifying Novel, Non-Toxic Drug Targets for Multi Drug Resistant Mycobacterium tuberculosis

We report the construction of a novel Systems Biology based virtual drug discovery model for the prediction of non-toxic metabolic targets in Mycobacterium tuberculosis (Mtb). This is based on a data-intensive genome level analysis and the principle of conservation of the evolutionarily important genes. In the 1623 sequenced Mtb strains, 890 metabolic genes identified through a systems approach in Mtb were evaluated for non-synonymous mutations. The 33 genes showed none or one variation in the entire 1623 strains, including 1084 Russian MDR strains. These invariant targets were further evaluated for their experimental and in silico essentiality as well as availability of their crystal structure in Protein Data Bank (PDB). Along with this, targets for the common existing antibiotics and the new Tb drug candidates were also screened for their variation across 1623 strains of Mtb for understanding the drug resistance. We propose that the reduced set of these reported targets could be a more effective starting point for medicinal chemists in generating new chemical leads. This approach has the potential of fueling the dried up Tuberculosis (Tb) drug discovery pipeline.

In silico approaches and chemical space of anti-P-type ATPase compounds for discovering new antituberculous drugs

Tuberculosis (TB) is one of the most important public health problems around the world. The emergence of multi-drug-resistant (MDR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis strains has driven the finding of alternative anti-TB targets. In this context, P-type ATPases are interesting therapeutic targets due to their key role in ion homeostasis across the plasma membrane and the mycobacterial survival inside macrophages. In this review, in silico and experimental strategies used for the rational design of new anti-TB drugs are presented; in addition, the chemical space distribution based on the structure and molecular properties of compounds with anti-TB and anti-P-type ATPase activity is discussed. The chemical space distribution compared to public compound libraries demonstrates that natural product libraries are a source of novel chemical scaffolds with potential anti-P-type ATPase activity. Furthermore, compounds that experimentally display anti-P-type ATPase activity belong to a chemical space of molecular properties comparable to that occupied by those approved for oral use, suggesting that these kinds of molecules have a good pharmacokinetic profile (drug-like) for evaluation as potential anti-TB drugs.

Mycobacterial cell wall biosynthesis: a multifaceted antibiotic target

Mycobacterium tuberculosis (Mtb), the etiological agent of tuberculosis (TB), is recognized as a global health emergency as promoted by the World Health Organization. Over 1 million deaths per year, along with the emergence of multi- and extensively-drug resistant strains of Mtb, have triggered intensive research into the pathogenicity and biochemistry of this microorganism, guiding the development of anti-TB chemotherapeutic agents. The essential mycobacterial cell wall, sharing some common features with all bacteria, represents an apparent ‘Achilles heel’ that has been targeted by TB chemotherapy since the advent of TB treatment. This complex structure composed of three distinct layers, peptidoglycan, arabinogalactan and mycolic acids, is vital in supporting cell growth, virulence and providing a barrier to antibiotics. The fundamental nature of cell wall synthesis and assembly has rendered the mycobacterial cell wall as the most widely exploited target of anti-TB drugs. This review provides an overview of the biosynthesis of the prominent cell wall components, highlighting the inhibitory mechanisms of existing clinical drugs and illustrating the potential of other unexploited enzymes as future drug targets.

Finding Potential Therapeutic Targets against Shigella flexneri through Proteome Exploration

Background:Shigella flexneri is a gram negative bacteria that causes the infectious disease “shigellosis.” S. flexneri is responsible for developing diarrhea, fever, and stomach cramps in human. Antibiotics are mostly given to patients infected with shigella. Resistance to antibiotics can hinder its treatment significantly. Upon identification of essential therapeutic targets, vaccine and drug could be effective therapy for the treatment of shigellosis.

Methods: The study was designed for the identification and qualitative characterization for potential drug targets from S. flexneri by using the subtractive proteome analysis. A set of computational tools were used to identify essential proteins those are required for the survival of S. flexneri. Total proteome (13,503 proteins) of S. flexneri was retrieved from NCBI and further analyzed by subtractive channel analysis. After identification of the metabolic proteins we have also performed its qualitative characterization to pave the way for the identification of promising drug targets.

Results: Subtractive analysis revealed that a list of 53 targets of S. flexneri were human non-homologous essential metabolic proteins that might be used for potential drug targets. We have also found that 11 drug targets are involved in unique pathway. Most of these proteins are cytoplasmic, can be used as broad spectrum drug targets, can interact with other proteins and show the druggable properties. The functionality and drug binding site analysis suggest a promising effective way to design the new drugs against S. flexneri.

Conclusion: Among the 53 therapeutic targets identified through this study, 13 were found highly potential as drug targets based on their physicochemical properties whilst only one was found as vaccine target against S. flexneri. The outcome might also be used as module as well as circuit design in systems biology.

Recognizing drug targets using evolutionary information: implications for repurposing FDA-approved drugs against Mycobacterium tuberculosis H37Rv

Drug repurposing to explore target space has been gaining pace over the past decade with the upsurge in the use of systematic approaches for computational drug discovery. Such a cost and time-saving approach gains immense importance for pathogens of special interest, such as Mycobacterium tuberculosis H37Rv. We report a comprehensive approach to repurpose drugs, based on the exploration of evolutionary relationships inferred from the comparative sequence and structural analyses between targets of FDA-approved drugs and the proteins of M. tuberculosis. This approach has facilitated the identification of several polypharmacological drugs that could potentially target unexploited M. tuberculosis proteins. A total of 130 FDA-approved drugs, originally intended against other diseases, could be repurposed against 78 potential targets in M. tuberculosis. Additionally, we have also made an attempt to augment the chemical space by recognizing compounds structurally similar to FDA-approved drugs. For three of the attractive cases we have investigated the probable binding modes of the drugs in their corresponding M. tuberculosis targets by means of structural modelling. Such prospective targets and small molecules could be prioritized for experimental endeavours, and could significantly influence drug-discovery and drug-development programmes for tuberculosis.

Probing the Druggability Limits for Enzymes of the NAD Biosynthetic Network in Glioma

The biosynthesis of NAD constitutes an important metabolic module in the cell, since NAD is an essential cofactor involved in several metabolic reactions. NAD concentrations are known to be significantly increased in several cancers, particularly in glioma, consistent with the observation of up-regulation of several enzymes of the network. Modulating NAD biosynthesis in glioma is therefore an attractive therapeutic strategy. Here we report reconstruction of a biochemical network of NAD biosynthesis consisting of 22 proteins, 36 metabolites, and 86 parameters, tuned to mimic the conditions in glioma. Kinetic simulations of the network provide comprehensive insights about the role of individual enzymes. Further, quantitative changes in the same network between different states of health and disease enable identification of drug targets, based on specific alterations in the given disease. Through simulations of enzyme inhibition titrations, we identify NMPRTase as a potential drug target, while eliminating other possible candidates NMNAT, NAPRTase, and NRK. We have also simulated titrations of both binding affinities as well as inhibitor concentrations, which provide insights into the druggability limits of the target, a novel aspect that can provide useful guidelines for designing inhibitors with optimal affinities. Our simulations suggest that an inhibitor affinity of 10 nM used in a concentration range of 0.1 to 10 μM achieves a near maximal inhibition response for NMPRTase and that increasing the affinity any further is not likely to have a significant advantage. Thus, the quantitative appreciation defines a maximal extent of inhibition possible for a chosen enzyme in the context of its network. Knowledge of this type enables an upper affinity threshold to be defined as a goal in lead screening and refinement stages in drug discovery.

Genetic signatures of Mycobacterium tuberculosis Nonthaburi genotype revealed by whole genome analysis of isolates from tuberculous meningitis patients in Thailand

Genome sequencing plays a key role in understanding the genetic diversity of Mycobacterium tuberculosis (M.tb). The genotype-specific character of M. tb contributes to tuberculosis severity and emergence of drug resistance. Strains of M. tb complex can be classified into seven lineages. The Nonthaburi (NB) genotype, belonging to the Indo-Oceanic lineage (lineage 1), has a unique spoligotype and IS6110-RFLP pattern but has not previously undergone a detailed whole genome analysis. In addition, there is not much information available on the whole genome analysis of M. tb isolates from tuberculous meningitis (TBM) patients in public databases. Isolates CSF3053, 46-5069 and 43-13838 of NB genotype were obtained from the cerebrospinal fluids of TBM Thai patients in Siriraj Hospital, Bangkok. The whole genomes were subjected to high throughput sequencing. The sequence data of each isolate were assembled into draft genome. The sequences were also aligned to reference genome, to determine genomic variations. Single nucleotide polymorphisms (SNPs) were obtained and grouped according to the functions of the genes containing them. They were compared with SNPs from 1,601 genomes, representing the seven lineages of M. tb complex, to determine the uniqueness of NB genotype. Susceptibility to first-line, second-line and other antituberculosis drugs were determined and related to the SNPs previously reported in drug-resistant related genes. The assembled genomes have an average size of 4,364,461 bp, 4,154 genes, 48 RNAs and 64 pseudogenes. A 500 base pairs deletion, which includes ppe50, was found in all isolates. RD239, specific for members of Indo Oceanic lineage, and RD147c were identified. A total of 2,202 SNPs were common to the isolates and used to classify the NB strains as members of sublineage 1.2.1. Compared with 1,601 genomes from the seven lineages of M. tb complex, mutation G2342203C was found novel to the isolates in this study. Three mutations (T28910C, C1180580T and C152178T) were found only in Thai NB isolates, including isolates from previous study. Although drug susceptibility tests indicated pan-susceptibility, non-synonymous SNPs previously reported to be associated with resistance to anti-tuberculous drugs; isoniazid, ethambutol, and ethionamide were identified in all the isolates. Non-synonymous SNPs were found in virulence genes such as the genes playing roles in apoptosis inhibition and phagosome arrest. We also report polymorphisms in essential genes, efflux pumps associated genes and genes with known epitopes. The analysis of the TBM isolates and the availability of the variations obtained will provide additional resources for global comparison of isolates from pulmonary tuberculosis and TBM. It will also contribute to the richness of genomic databases towards the prediction of antibiotic resistance, level of virulence and of origin of infection.

Viability, morphology, and proteome of Mycobacterium smegmatis MSMEG_0319 knockout strain

Mycobacterium tuberculosis Rv0228, a membrane protein, is predicted as a drug target through computational methods. MSMEG_0319 (MS0319) in Mycobacterium smegmatis mc(2) 155 is the ortholog of Rv0228. To study the effect of MS0319 protein on M. smegmatis, an MS0319 gene knockout strain (ΔMS0319) was generated via a homologous recombination technique in this study. The results showed that the lack of MS0319 protein in mc(2) 155 cells led to the loss of viability at nonpermissive temperature. Scanning electron microscopy and transmission electron microscopy observations showed drastic changes in cellular shape especially cell wall disruption in ΔMS0319 cells. Proteomic analysis of ΔMS0319 cells through LC-MS/MS revealed that 462 proteins had changes in their expressions by lacking MS0319 protein. The M. tuberculosis orthologs of these 462 proteins were found through BLASTp search and functional clustering and metabolic pathway enrichment were performed on the orthologs. The results revealed that most of them were enzymes involved in metabolism of carbohydrates and amino acids, indicating that Rv0228 played an important role in cellular metabolism. All these results suggested Rv0228 as a potential target for development of antituberculosis drugs.

Cloning, expression and characterization of histidine-tagged biotin synthase of Mycobacterium tuberculosis

The emergence of Mycobacterium tuberculosis strains that are resistant to the current anti-tuberculosis (TB) drugs necessitates a need to develop a new class of drugs whose targets are different from the current ones. M. tuberculosis biotin synthase (MtbBS) is one such target that is essential for the survival of the bacteria. In this study, MtbBS was cloned, overexpressed and purified to homogeneity for biochemical characterization. It is likely to be a dimer in its native form. Its pH and temperature optima are 8.0 and 37 °C, respectively. Km for DTB and SAM was 2.81 ± 0.35 and 9.95 ± 0.98 μM, respectively. The enzyme had a maximum velocity of 0.575 ± 0.015 μM min(-1), and a turn-over of 0.0935 min(-1). 5'-deoxyadenosine (dAH), S-(5'-Adenosyl)-l-cysteine (AdoCy) and S-(5'-Adenosyl)-l-homocysteine (AdoHcy) were competitive inhibitors of MtbBS with the following inactivation parameters: Ki = 24.2 μM, IC50 = 267.4 μM; Ki = 0.84 μM, IC50 = 9.28 μM; and Ki = 0.592 μM, IC50 = 6.54 μM for dAH, AdoCy and AdoHcy respectively. dAH could inhibit the growth of M. tuberculosis H37Ra with an MIC of 392.6 μg/ml. This information should be useful for the discovery of inhibitors of MtbBS.

Comparative analyses of the proteins from Mycobacterium tuberculosis and human genomes: Identification of potential tuberculosis drug targets

Tuberculosis, one of the major infectious diseases affecting human beings is caused by the bacillus Mycobacterium tuberculosis. Increased resistance to known drugs commonly used for the treatment of tuberculosis has created an urgent need to identify new targets for validation and to develop drugs. In this study, we have used various bioinformatics tools, to compare the protein sequences from twenty-three M. tuberculosis genome strains along with the known human protein sequences, in order to identify the 'conserved' M. tuberculosis proteins absent in human. Further, based on the analysis of protein interaction networks, we selected one-hundred and forty proteins that were predicted as potential M. tuberculosis drug targets and prioritized according to the ranking of 'clusters' of interacting proteins. Comparison of the predicted 140 TB targets with literature indicated that 46 of them were previously reported, thereby increasing the confidence in our predictions of the remaining 94 targets too. The analyses of the structures and functions corresponding to the predicted potential TB drug targets indicated a diverse range of proteins that included ten 'druggable' targets with some of the known drugs.

A whole genome bioinformatic approach to determine potential latent phase specific targets in Mycobacterium tuberculosis

Current Tuberculosis treatment is long and expensive, faces the increasing burden of MDR/XDR strains and lack of effective treatment against latent form, resulting in an urgent need of new anti-TB drugs. Key to TB biology is its capacity to fight the host's RNOS mediated attack. RNOS are known to display a concentration dependent mycobactericidal activity, which leads to the following hypothesis "if we know which proteins are targeted by RNOS and kill TB, we we might be able to inhibit them with drugs resulting in a synergistic bactericidal effect". Based on this idea, we performed an Mtb metabolic network whole proteome analysis of potential RNOS sensitive and relevant targets which includes target druggability and essentiality criteria. Our results, available at http://tuberq.proteinq.com.ar yield new potential TB targets, like I3PS, while also providing and updated view of previous proposals becoming an important tool for researchers looking for new ways of killing TB.

Comparative Genome and Network Centrality Analysis to Identify Drug Targets of Mycobacterium tuberculosis H37Rv

Potential drug targets of Mycobacterium tuberculosis H37Rv were identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome of Mycobacterium tuberculosis H37Rv against Database of Essential Genes (DEG) yields a list of proteins which are essential for the growth and survival of the pathogen. Those proteins which are nonhomologous with human were selected. The resulting proteins were then prioritized by using the four network centrality measures: degree, closeness, betweenness, and eigenvector. Proteins whose centrality value is close to the centre of gravity of the interactome network were proposed as a final list of potential drug targets for the pathogen. The use of an integrated approach is believed to increase the success of the drug target identification process. For the purpose of validation, selective comparisons have been made among the proposed targets and previously identified drug targets by various other methods. About half of these proteins have been already reported as potential drug targets. We believe that the identified proteins will be an important input to experimental study which in the way could save considerable amount of time and cost of drug target discovery.

Computational analysis of protein interaction networks for infectious diseases

Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host–pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.

SInCRe-structural interactome computational resource for Mycobacterium tuberculosis

We have developed an integrated database for Mycobacterium tuberculosis H37Rv (Mtb) that collates information on protein sequences, domain assignments, functional annotation and 3D structural information along with protein–protein and protein–small molecule interactions. SInCRe (Structural Interactome Computational Resource) is developed out of CamBan (Cambridge and Bangalore) collaboration. The motivation for development of this database is to provide an integrated platform to allow easily access and interpretation of data and results obtained by all the groups in CamBan in the field of Mtb informatics. In-house algorithms and databases developed independently by various academic groups in CamBan are used to generate Mtb-specific datasets and are integrated in this database to provide a structural dimension to studies on tuberculosis. The SInCRe database readily provides information on identification of functional domains, genome-scale modelling of structures of Mtb proteins and characterization of the small-molecule binding sites within Mtb. The resource also provides structure-based function annotation, information on small-molecule binders including FDA (Food and Drug Administration)-approved drugs, protein–protein interactions (PPIs) and natural compounds that bind to pathogen proteins potentially and result in weakening or elimination of host–pathogen protein–protein interactions. Together they provide prerequisites for identification of off-target binding.

Database URL:http://proline.biochem.iisc.ernet.in/sincre

Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein-protein interaction network

Background

In spite of the implementations of several strategies, tuberculosis (TB) is overwhelmingly a serious global public health problem causing millions of infections and deaths every year. This is mainly due to the emergence of drug-resistance varieties of TB. The current treatment strategies for the drug-resistance TB are of longer duration, more expensive and have side effects. This highlights the importance of identification and prioritization of targets for new drugs. This study has been carried out to prioritize potential drug targets of Mycobacteriumtuberculosis H37Rv based on their flow to resistance genes.

Methods

The weighted proteome interaction network of the pathogen was constructed using a dataset from STRING database. Only a subset of the dataset with interactions that have a combined score value ≥770 was considered. Maximum flow approach has been used to prioritize potential drug targets. The potential drug targets were obtained through comparative genome and network centrality analysis. The curated set of resistance genes was retrieved from literatures. Detail literature review and additional assessment of the method were also carried out for validation.

Results

A list of 537 proteins which are essential to the pathogen and non-homologous with human was obtained from the comparative genome analysis. Through network centrality measures, 131 of them were found within the close neighborhood of the centre of gravity of the proteome network. These proteins were further prioritized based on their maximum flow value to resistance genes and they are proposed as reliable drug targets of the pathogen. Proteins which interact with the host were also identified in order to understand the infection mechanism.

Conclusion

Potential drug targets of Mycobacteriumtuberculosis H37Rv were successfully prioritized based on their flow to resistance genes of existing drugs which is believed to increase the druggability of the targets since inhibition of a protein that has a maximum flow to resistance genes is more likely to disrupt the communication to these genes. Purposely selected literature review of the top 14 proteins showed that many of them in this list were proposed as drug targets of the pathogen.

Electronic supplementary material

The online version of this article (doi:10.1186/s40169-015-0061-6) contains supplementary material, which is available to authorized users.

Identification of pneumococcal colonization determinants in the stringent response pathway facilitated by genomic diversity

BACKGROUND

Understanding genetic determinants of a microbial phenotype generally involves creating and comparing isogenic strains differing at the locus of interest, but the naturally existing genomic and phenotypic diversity of microbial populations has rarely been exploited. Here we report use of a diverse collection of 616 carriage isolates of Streptococcus pneumoniae and their genome sequences to help identify a novel determinant of pneumococcal colonization.

RESULTS

A spontaneously arising laboratory variant (SpnYL101) of a capsule-switched TIGR4 strain (TIGR4:19F) showed reduced ability to establish mouse nasal colonization and lower resistance to non-opsonic neutrophil-mediated killing in vitro, a phenotype correlated with in vivo success. Whole genome sequencing revealed 5 single nucleotide polymorphisms (SNPs) affecting 4 genes in SpnYL101 relative to its ancestor. To evaluate the effect of variation in each gene, we performed an in silico screen of 616 previously published genome sequences to identify pairs of closely-related, serotype-matched isolates that differ at the gene of interest, and compared their resistance to neutrophil-killing. This method allowed rapid examination of multiple candidate genes and found phenotypic differences apparently associated with variation in SP_1645, a RelA/ SpoT homolog (RSH) involved in the stringent response. To establish causality, the alleles corresponding to SP_1645 were switched between the TIGR4:19F and SpnYL101. The wild-type SP_1645 conferred higher resistance to neutrophil-killing and competitiveness in mouse colonization. Using a similar strategy, variation in another RSH gene (TIGR4 locus tag SP_1097) was found to alter resistance to neutrophil-killing.

CONCLUSIONS

These results indicate that analysis of naturally existing genomic diversity complements traditional genetics approaches to accelerate genotype-phenotype analysis.

Purification and functional characterization of diadenosine 5',5‴-P(1),P(4)-tetraphosphate phosphorylases from Mycobacterium smegmatis and Mycobacterium avium

We recently demonstrated that the Rv2613c protein from Mycobacterium tuberculosis H37Rv is a novel diadenosine 5',5‴-P(1),P(4)-tetraphosphate (Ap4A) phosphorylase (MtAPA) that forms a tetramer. Mycobacterium avium and Mycobacterium smegmatis express proteins named MAV_3489 and MSMEG_2932, respectively, that are homologous to MtAPA. Here we showed that the MAV_3489 and MSMEG_2932 proteins possess Ap4A phosphorylase activity and enzymatic properties similar to those of MtAPA. Furthermore, gel-filtration column chromatography revealed that MAV_3489 and MSMEG_2932 assembled into homotetramers in solution, indicating that they may also form unique Ap4A-binding sites composed of tetramers.

Comparative whole-genome analysis of clinical isolates reveals characteristic architecture of Mycobacterium tuberculosis pangenome

The tubercle complex consists of closely related mycobacterium species which appear to be variants of a single species. Comparative genome analysis of different strains could provide useful clues and insights into the genetic diversity of the species. We integrated genome assemblies of 96 strains from Mycobacterium tuberculosis complex (MTBC), which included 8 Indian clinical isolates sequenced and assembled in this study, to understand its pangenome architecture. We predicted genes for all the 96 strains and clustered their respective CDSs into homologous gene clusters (HGCs) to reveal a hard-core, soft-core and accessory genome component of MTBC. The hard-core (HGCs shared amongst 100% of the strains) was comprised of 2,066 gene clusters whereas the soft-core (HGCs shared amongst at least 95% of the strains) comprised of 3,374 gene clusters. The change in the core and accessory genome components when observed as a function of their size revealed that MTBC has an open pangenome. We identified 74 HGCs that were absent from reference strains H37Rv and H37Ra but were present in most of clinical isolates. We report PCR validation on 9 candidate genes depicting 7 genes completely absent from H37Rv and H37Ra whereas 2 genes shared partial homology with them accounting to probable insertion and deletion events. The pangenome approach is a promising tool for studying strain specific genetic differences occurring within species. We also suggest that since selecting appropriate target genes for typing purposes requires the expected target gene be present in all isolates being typed, therefore estimating the core-component of the species becomes a subject of prime importance.

Prediction of host - pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs

Background

Emergence of multiple drug resistant strains of M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease.

Results

We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by ‘interolog’ method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins.

Conclusions

This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how M. tuberculosis interacts with its host.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0535-y) contains supplementary material, which is available to authorized users.

Mycobacterium tuberculosis effectors involved in host-pathogen interaction revealed by a multiple scales integrative pipeline

BACKGROUND

Mycobacterium tuberculosis (Mtb) has evolved multiple strategies to counter host immunity. Proteins are one important player in the host-pathogen interaction. A comprehensive list of such proteins will benefit our understanding of pathogenesis of Mtb.

METHODS

A genome-scale dataset was created from different sources of published data: global gene expression studies in disease models; genome-wide insertional mutagenesis defining gene essentiality under different conditions; genes lost in clinical isolates; subcellular localization analysis and non-homology analysis. Using data mining and meta-analysis, expressed proteins critical for intracellular survival of Mtb are first identified, followed by subcellular localization analysis, finally filtering a series of subtractive channel of analysis to find out promising drug target candidates.

RESULTS

The analysis found 54 potential candidates essential for the intracellular survival of the pathogen and non-homologous to host or gut flora, and might be promising drug targets.

CONCLUSION

Based on our meta-analysis and bioinformatics analysis, 54 hits were found from Mtb around 4000 open reading frames. These hits can be good candidates for further experimental investigation.

Bacterial histidine kinases as novel antibacterial drug targets

Bacterial histidine kinases (HKs) are promising targets for novel antibacterials. Bacterial HKs are part of bacterial two-component systems (TCSs), the main signal transduction pathways in bacteria, regulating various processes including virulence, secretion systems and antibiotic resistance. In this review, we discuss the biological importance of TCSs and bacterial HKs for the discovery of novel antibacterials, as well as published TCS and HK inhibitors that can be used as a starting point for structure-based approaches to develop novel antibacterials.

In-silico Metabolome Target Analysis Towards PanC-based Antimycobacterial Agent Discovery

Mycobacterium tuberculosis, the main cause of tuberculosis (TB), has still remained a global health crisis especially in developing countries. Tuberculosis treatment is a laborious and lengthy process with high risk of noncompliance, cytotoxicity adverse events and drug resistance in patient. Recently, there has been an alarming rise of drug resistant in TB. In this regard, it is an unmet need to develop novel antitubercular medicines that target new or more effective biochemical pathways to prevent drug resistant Mycobacterium. Integrated study of metabolic pathways through in-silico approach played a key role in antimycobacterial design process in this study. Our results suggest that pantothenate synthetase (PanC), anthranilate phosphoribosyl transferase (TrpD) and 3-isopropylmalate dehydratase (LeuD) might be appropriate drug targets. In the next step, in-silico ligand analysis was used for more detailed study of chemical tractability of targets. This was helpful to identify pantothenate synthetase (PanC, Rv3602c) as the best target for antimycobacterial design procedure. Virtual library screening on the best ligand of PanC was then performed for inhibitory ligand design. At the end, five chemical intermediates showed significant inhibition of Mycobacterium bovis with good selectivity indices (SI) ≥10 according to Tuberculosis Antimicrobial Acquisition & Coordinating Facility of US criteria for antimycobacterial screening programs.

Using a genome-scale metabolic model of Enterococcus faecalis V583 to assess amino acid uptake and its impact on central metabolism

Increasing antibiotic resistance in pathogenic bacteria necessitates the development of new medication strategies. Interfering with the metabolic network of the pathogen can provide novel drug targets but simultaneously requires a deeper and more detailed organism-specific understanding of the metabolism, which is often surprisingly sparse. In light of this, we reconstructed a genome-scale metabolic model of the pathogen Enterococcus faecalis V583. The manually curated metabolic network comprises 642 metabolites and 706 reactions. We experimentally determined metabolic profiles of E. faecalis grown in chemically defined medium in an anaerobic chemostat setup at different dilution rates and calculated the net uptake and product fluxes to constrain the model. We computed growth-associated energy and maintenance parameters and studied flux distributions through the metabolic network. Amino acid auxotrophies were identified experimentally for model validation and revealed seven essential amino acids. In addition, the important metabolic hub of glutamine/glutamate was altered by constructing a glutamine synthetase knockout mutant. The metabolic profile showed a slight shift in the fermentation pattern toward ethanol production and increased uptake rates of multiple amino acids, especially l-glutamine and l-glutamate. The model was used to understand the altered flux distributions in the mutant and provided an explanation for the experimentally observed redirection of the metabolic flux. We further highlighted the importance of gene-regulatory effects on the redirection of the metabolic fluxes upon perturbation. The genome-scale metabolic model presented here includes gene-protein-reaction associations, allowing a further use for biotechnological applications, for studying essential genes, proteins, or reactions, and the search for novel drug targets.

Genome-wide Analysis of Mycoplasma hominis for the Identification of Putative Therapeutic Targets

Ever increasing propensity of antibiotic resistance among pathogenic bacteria raises the demand for the development of novel therapeutic agents to control this grave problem. Advances in the field of bioinformatics, genomics, and proteomics have greatly facilitated the discovery of alternative drugs by swift identification of new drug targets. In the present study, we employed comparative genomics and metabolic pathway analysis with an aim of identifying therapeutic targets in Mycoplasma hominis. Our study has revealed 40 annotated metabolic pathways, including five unique pathways of M. hominis. Our study also identified 179 essential proteins, including 59 proteins having no similarity with human proteins. Further filtering by molecular weight, subcellular localization, functional analysis, and protein network interaction, we identified 57 putative candidates for which new drugs can be developed. Druggability analysis for each of the identified targets has prioritized 16 proteins as suitable for potential drug development.

Pyridoxal-phosphate dependent mycobacterial cysteine synthases: Structure, mechanism and potential as drug targets

The alarming increase of drug resistance in Mycobacterium tuberculosis strains poses a severe threat to human health. Chemotherapy is particularly challenging because M. tuberculosis can persist in the lungs of infected individuals; estimates of the WHO indicate that about 1/3 of the world population is infected with latent tuberculosis providing a large reservoir for relapse and subsequent spread of the disease. Persistent M. tuberculosis shows considerable tolerance towards conventional antibiotics making treatment particularly difficult. In this phase the bacilli are exposed to oxygen and nitrogen radicals generated as part of the host response and redox-defense mechanisms are thus vital for the survival of the pathogen. Sulfur metabolism and de novo cysteine biosynthesis have been shown to be important for the redox homeostasis in persistent M. tuberculosis and these pathways could provide promising targets for novel antibiotics for the treatment of the latent form of the disease. Recent research has provided evidence for three de novo metabolic routes of cysteine biosynthesis in M. tuberculosis, each with a specific PLP dependent cysteine synthase with distinct substrate specificities. In this review we summarize our present understanding of these pathways, with a focus on the advances on functional and mechanistic characterization of mycobacterial PLP dependent cysteine synthases, their role in the various pathways to cysteine, and first attempts to develop specific inhibitors of mycobacterial cysteine biosynthesis. This article is part of a Special Issue entitled: Cofactor-dependent proteins: evolution, chemical diversity and bio-applications.

In silico analysis of Acinetobacter baumannii phospholipase D as a subunit vaccine candidate

The rate of human health care-associated infections caused by Acinetobacter baumannii has increased significantly in recent years for its remarkable resistance to desiccation and most antibiotics. Phospholipases, capable of destroying a phospholipid substrate, are heterologous group of enzymes which are believed to be the bacterial virulence determinants. There is a need for in silico studies to identify potential vaccine candidates. A. baumannii phospholipase D (PLD) role has been proved in increasing organism's resistance to human serum, destruction of host epithelial cell and pathogenesis in murine model. In this in silico study high potentials of A. baumannii PLD in elicitation of humoral and cellular immunities were elucidated. Thermal stability, long half-life, non-similarity to human and gut flora proteome and non-allergenicity were in a list of A. baumannii PLD positive properties. Potential epitopic sequences were also identified that could be used as peptide vaccines against A. baumannii and various other human bacterial pathogens.

Identifying feasible metabolic routes in Mycobacterium smegmatis and possible alterations under diverse nutrient conditions

Background

Many studies on M. tuberculosis have emerged from using M. smegmatis MC²155 (Msm), since they share significant similarities and yet Msm is non-pathogenic and faster growing. Although several individual molecules have been studied from Msm, many questions remain open about its metabolism as a whole and its capability to be versatile. Adaptability and versatility are emergent properties of a system, warranting a molecular systems perspective to understand them.

Results

We identify feasible metabolic pathways in Msm in reference condition with transcriptome, phenotypic microarray, along with functional annotation of the genome. Together with transcriptome data, specific genes from a set of alternatives have been mapped onto different pathways. About 257 metabolic pathways can be considered to be feasible in Msm. Next, we probe cellular metabolism with an array of alternative carbon and nitrogen sources and identify those that are utilized and favour growth as well as those that do not support growth. In all, about 135 points in the entire metabolic map are probed. Analyzing growth patterns under these conditions, lead us to hypothesize different pathways that can become active in various conditions and possible alternate routes that may be induced, thus explaining the observed physiological adaptations.

Conclusions

The study provides the first detailed analysis of feasible pathways towards adaptability. We obtain mechanistic insights that explain observed phenotypic behaviour by studying gene-expression profiles and pathways inferred from the genome sequence. Comparison of transcriptome and phenome analysis of Msm and Mtb provides a rationale for understanding commonalities in metabolic adaptability.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-014-0276-5) contains supplementary material, which is available to authorized users.

Comparative analyses of nonpathogenic, opportunistic, and totally pathogenic mycobacteria reveal genomic and biochemical variabilities and highlight the survival attributes of Mycobacterium tuberculosis

Mycobacterial evolution involves various processes, such as genome reduction, gene cooption, and critical gene acquisition. Our comparative genome size analysis of 44 mycobacterial genomes revealed that the nonpathogenic (NP) genomes were bigger than those of opportunistic (OP) or totally pathogenic (TP) mycobacteria, with the TP genomes being smaller yet variable in size—their genomic plasticity reflected their ability to evolve and survive under various environmental conditions. From the 44 mycobacterial species, 13 species, representing TP, OP, and NP, were selected for genomic-relatedness analyses. Analysis of homologous protein-coding genes shared between Mycobacterium indicus pranii (NP), Mycobacterium intracellulare ATCC 13950 (OP), and Mycobacterium tuberculosis H37Rv (TP) revealed that 4,995 (i.e., ~95%) M. indicaus pranii proteins have homology with M. intracellulare, whereas the homologies among M. indicus pranii, M. intracellulare ATCC 13950, and M. tuberculosis H37Rv were significantly lower. A total of 4,153 (~79%) M. indicus pranii proteins and 4,093 (~79%) M. intracellulare ATCC 13950 proteins exhibited homology with the M. tuberculosis H37Rv proteome, while 3,301 (~82%) and 3,295 (~82%) M. tuberculosis H37Rv proteins showed homology with M. indicus pranii and M. intracellulare ATCC 13950 proteomes, respectively. Comparative metabolic pathway analyses of TP/OP/NP mycobacteria showed enzymatic plasticity between M. indicus pranii (NP) and M. intracellulare ATCC 13950 (OP), Mycobacterium avium 104 (OP), and M. tuberculosis H37Rv (TP). Mycobacterium tuberculosis seems to have acquired novel alternate pathways with possible roles in metabolism, host-pathogen interactions, virulence, and intracellular survival, and by implication some of these could be potential drug targets.

The complete sequence analysis of Mycobacterium indicus pranii, a novel species of Mycobacterium shown earlier to have strong immunomodulatory properties and currently in use for the treatment of leprosy, places it evolutionarily at the point of transition to pathogenicity. With the purpose of establishing the importance of M. indicus pranii in providing insight into the virulence mechanism of tuberculous and nontuberculous mycobacteria, we carried out comparative genomic and proteomic analyses of 44 mycobacterial species representing nonpathogenic (NP), opportunistic (OP), and totally pathogenic (TP) mycobacteria. Our results clearly placed M. indicus pranii as an ancestor of the M. avium complex. Analyses of comparative metabolic pathways between M. indicus pranii (NP), M. tuberculosis (TP), and M. intracellulare (OP) pointed to the presence of novel alternative pathways in M. tuberculosis with implications for pathogenesis and survival in the human host and identification of new drug targets.

Roles of Ala-149 in the catalytic activity of diadenosine tetraphosphate phosphorylase from Mycobacterium tuberculosis H37Rv

Diadenosine 5',5'''-P(1),P(4)-tetraphosphate (Ap4A) phosphorylase from Mycobacterium tuberculosis H37Rv (MtAPA) belongs to the histidine triad motif (HIT) superfamily, but is the only member with an alanine residue at position 149 (Ala-149). Enzymatic analysis revealed that the Ala-149 deletion mutant displayed substrate specificity for diadenosine 5',5'''-P(1),P(5)-pentaphosphate and was inactive on Ap4A and other substrates that are utilized by the wild-type enzyme.

Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets

Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies.

Characterizing the pocketome of Mycobacterium tuberculosis and application in rationalizing polypharmacological target selection

Polypharmacology is beginning to emerge as an important concept in the field of drug discovery. However, there are no established approaches to either select appropriate target sets or design polypharmacological drugs. Here, we propose a structural-proteomics approach that utilizes the structural information of the binding sites at a genome-scale obtained through in-house algorithms to characterize the pocketome, yielding a list of ligands that can participate in various biochemical events in the mycobacterial cell. The pocket-type space is seen to be much larger than the sequence or fold-space, suggesting that variations at the site-level contribute significantly to functional repertoire of the organism. All-pair comparisons of binding sites within Mycobacterium tuberculosis (Mtb), pocket-similarity network construction and clustering result in identification of binding-site sets, each containing a group of similar binding sites, theoretically having a potential to interact with a common set of compounds. A polypharmacology index is formulated to rank targets by incorporating a measure of druggability and similarity to other pockets within the proteome. This study presents a rational approach to identify targets with polypharmacological potential along with possible drugs for repurposing, while simultaneously, obtaining clues on lead compounds for use in new drug-discovery pipelines.

Unraveling novel broad-spectrum antibacterial targets in food and waterborne pathogens using comparative genomics and protein interaction network analysis

Food and waterborne diseases are a growing concern in terms of human morbidity and mortality worldwide, even in the 21st century, emphasizing the need for new therapeutic interventions for these diseases. The current study aims at prioritizing broad-spectrum antibacterial targets, present in multiple food and waterborne bacterial pathogens, through a comparative genomics strategy coupled with a protein interaction network analysis. The pathways unique and common to all the pathogens under study (viz., methane metabolism, d-alanine metabolism, peptidoglycan biosynthesis, bacterial secretion system, two-component system, C5-branched dibasic acid metabolism), identified by comparative metabolic pathway analysis, were considered for the analysis. The proteins/enzymes involved in these pathways were prioritized following host non-homology analysis, essentiality analysis, gut flora non-homology analysis and protein interaction network analysis. The analyses revealed a set of promising broad-spectrum antibacterial targets, present in multiple food and waterborne pathogens, which are essential for bacterial survival, non-homologous to host and gut flora, and functionally important in the metabolic network. The identified broad-spectrum candidates, namely, integral membrane protein/virulence factor (MviN), preprotein translocase subunits SecB and SecG, carbon storage regulator (CsrA), and nitrogen regulatory protein P-II 1 (GlnB), contributed by the peptidoglycan pathway, bacterial secretion systems and two-component systems, were also found to be present in a wide range of other disease-causing bacteria. Cytoplasmic proteins SecG, CsrA and GlnB were considered as drug targets, while membrane proteins MviN and SecB were classified as vaccine targets. The identified broad-spectrum targets can aid in the design and development of antibacterial agents not only against food and waterborne pathogens but also against other pathogens.