Comparative genomics study for the identification of drug and vaccine targets in Staphylococcus aureus: MurA ligase enzyme as a proposed candidate

Ghosh, Soma; Prava, Jyoti; Samal, Himanshu Bhusan; Suar, Mrutyunjay; Mahapatra, Rajani Kanta

doi:10.1016/j.mimet.2014.03.009

Cited by 12 publications

(6 citation statements)

References 30 publications

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“…In the modern postgenomic period, the probabilities of selecting suitable targets through computational methods and integration of "omics" data, such as proteomics, metabolomics, and genomics have been expanding continuously. The in silico approaches such as subtractive and comparative proteomics are now being used extensively for the identification as well as the prediction of drug targets for several pathogenic bacteria [13]. Compared to traditional methods, these techniques are efficient, time saving as well as cost effective in drug designing processes [14].…”

Section: Introductionmentioning

confidence: 99%

In Silico Subtractive Proteomics Approach for Identification of Potential Drug Targets in Staphylococcus saprophyticus

Shahid

Ashfaq

Saeed

et al. 2020

IJERPH

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Staphylococcus saprophyticus is a uropathogenic bacteria responsible for acute urinary tract infections (UTIs) mainly in young female patients. Patients suffering from urinary catheterization, pregnant patients, the elderly as well as those with nosocomial UTIs are at greater risk of the colonizing S. saprophyticus infection. The causative factors include benign prostatic hyperplasia, indwelling catheter, neurogenic bladder, pregnancy, and history of frequent UTIs. Recent findings have exhibited that S. saprophyticus is resistant to several antimicrobial agents. Moreover, there is a global concern regarding the increasing level of antimicrobial resistance, which leads to treatment failure and reduced effectiveness of broad-spectrum antimicrobials. Therefore, a novel approach is being utilized to combat resistant microbes since the past few years. Subtractive proteome analysis has been performed with the entire proteome of S. saprophyticus strain American Type Culture Collection (ATCC) 15305 using several bioinformatics servers and software. The proteins that were non-homologous to humans and bacteria were identified for metabolic pathway analysis. Only four cytoplasmic proteins were found possessing the potential of novel drug target candidates. The development of innovative therapeutic agents by targeting the inhibition of any essential proteins may disrupt the metabolic pathways specific to the pathogen, thus causing destruction as well as eradication of the pathogen from a particular host. The identified targets can facilitate in designing novel and potent drugs against S. saprophyticus strain ATCC 15305.

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Section: Introductionmentioning

confidence: 99%

In Silico Subtractive Proteomics Approach for Identification of Potential Drug Targets in Staphylococcus saprophyticus

Shahid

Ashfaq

Saeed

et al. 2020

IJERPH

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“…That is the reason we set out in this study a systematic approach to identify and annotate prokaryotic ortholog gene circuits whose presence or absence are linked to phenotypes of antagonism to the host. Comparative genomics has been utilized before in the identification of drug and vaccine targets in Staphylococcus aureus [ 22 ] and mycobacterial peptidoglycan remodeling enzymes linked to pathogenicity [ 23 ]. Ours, however, takes it one step further, employing comparisons among 2527 distinct genomes.…”

Section: Introductionmentioning

confidence: 99%

Encyclopedia of bacterial gene circuits whose presence or absence correlate with pathogenicity – a large-scale system analysis of decoded bacterial genomes

2015

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BackgroundBacterial infections comprise a global health challenge as the incidences of antibiotic resistance increase. Pathogenic potential of bacteria has been shown to be context dependent, varying in response to environment and even within the strains of the same genus.ResultsWe used the KEGG repository and extensive literature searches to identify among the 2527 bacterial genomes in the literature those implicated as pathogenic to the host, including those which show pathogenicity in a context dependent manner. Using data on the gene contents of these genomes, we identified sets of genes highly abundant in pathogenic but relatively absent in commensal strains and vice versa. In addition, we carried out genome comparison within a genus for the seventeen largest genera in our genome collection. We projected the resultant lists of ortholog genes onto KEGG bacterial pathways to identify clusters and circuits, which can be linked to either pathogenicity or synergy. Gene circuits relatively abundant in nonpathogenic bacteria often mediated biosynthesis of antibiotics. Other synergy-linked circuits reduced drug-induced toxicity. Pathogen-abundant gene circuits included modules in one-carbon folate, two-component system, type-3 secretion system, and peptidoglycan biosynthesis. Antibiotics-resistant bacterial strains possessed genes modulating phagocytosis, vesicle trafficking, cytoskeletal reorganization, and regulation of the inflammatory response. Our study also identified bacterial genera containing a circuit, elements of which were previously linked to Alzheimers disease.ConclusionsPresent study produces for the first time, a signature, in the form of a robust list of gene circuitry whose presence or absence could potentially define the pathogenicity of a microbiome. Extensive literature search substantiated a bulk majority of the commensal and pathogenic circuitry in our predicted list. Scanning microbiome libraries for these circuitry motifs will provide further insights into the complex and context dependent pathogenicity of bacteria.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1957-7) contains supplementary material, which is available to authorized users.

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“…The collected protein sequences of unique pathways of the pathogen from the above step were subjected to NCBI BLASTP search with an e-value threshold set to 0.005 against human proteome. Proteins showing ‘no significant similarities’ or ‘no hits’ were considered as non-homologous proteins to the host proteome (Anishetty et al 2005; Ghosh et al 2014). Since non-homologous proteins minimize the chances of side effects and cross-reactivity caused by antibiotics, hence they can effectively serve as therapeutic candidates.…”

Section: Methodsmentioning

confidence: 99%

Identification of novel therapeutic candidates inCryptosporidium parvum: anin silicoapproach

Panda

Mahapatra

2018

Parasitology

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Unavailability of vaccines and effective drugs are primarily responsible for the growing menace of cryptosporidiosis. This study has incorporated a bioinformatics-based screening approach to explore potential vaccine candidates and novel drug targets in Cryptosporidium parvum proteome. A systematic strategy was defined for comparative genomics, orthology with related Cryptosporidium species, prioritization parameters and MHC class I and II binding promiscuity. The approach reported cytoplasmic protein cgd7_1830, a signal peptide protein, as a novel drug target. SWISS-MODEL online server was used to generate the 3D model of the protein and was validated by PROCHECK. The model has been subjected to in silico docking study with screened potent lead compounds from the ZINC database, PubChem and ChEMBL database using Flare software package of Cresset®. Furthermore, the approach reported protein cgd3_1400, as a vaccine candidate. The predicted B- and T-cell epitopes on the proposed vaccine candidate with highest scores were also subjected to docking study with MHC class I and II alleles using ClusPro web server. Results from this study could facilitate selection of proteins which could serve as drug targets and vaccine candidates to efficiently tackle the growing threat of cryptosporidiosis.

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Comparative genomics study for the identification of drug and vaccine targets in Staphylococcus aureus: MurA ligase enzyme as a proposed candidate

Cited by 12 publications

References 30 publications

In Silico Subtractive Proteomics Approach for Identification of Potential Drug Targets in Staphylococcus saprophyticus

In Silico Subtractive Proteomics Approach for Identification of Potential Drug Targets in Staphylococcus saprophyticus

Encyclopedia of bacterial gene circuits whose presence or absence correlate with pathogenicity – a large-scale system analysis of decoded bacterial genomes

Identification of novel therapeutic candidates inCryptosporidium parvum: anin silicoapproach

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