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

Potential drug targets ofMycobacterium tuberculosis H37Rvwere identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome ofMycobacterium tuberculosis H37Rvagainst 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.

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

Clinical and Translational Medicine ◽

10.1186/s40169-015-0061-6 ◽

2015 ◽

Vol 4 (1) ◽

Cited By ~ 6

Author(s):

Tilahun Melak ◽

Sunita Gakkhar

Keyword(s):

Mycobacterium Tuberculosis ◽

Protein Interaction Network ◽

Drug Targets ◽

Interaction Network ◽

Maximum Flow ◽

Potential Drug ◽

Protein Protein Interaction ◽

Mycobacterium Tuberculosis H37rv ◽

Potential Drug Targets ◽

Protein Protein Interaction Network

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Identification of Missing Carbon Fixation Enzymes as Potential Drug Targets in Mycobacterium Tuberculosis

Journal of Integrative Bioinformatics ◽

10.1515/jib-2017-0041 ◽

2018 ◽

Vol 15 (3) ◽

Cited By ~ 1

Author(s):

Amit Katiyar ◽

Harpreet Singh ◽

Krishna Kant Azad

Keyword(s):

Mycobacterium Tuberculosis ◽

Drug Target ◽

Drug Targets ◽

Metabolic Networks ◽

Carbon Fixation ◽

New Drugs ◽

Metabolic Adaptation ◽

Potential Drug ◽

Genes Encoding ◽

Potential Drug Targets

Abstract Metabolic adaptation to the host environment has been recognized as an essential mechanism of pathogenicity and the growth of Mycobacterium tuberculosis (Mtb) in the lungs for decades. The Mtb uses CO2 as a source of carbon during the dormant or non-replicative state. However, there is a lack of biochemical knowledge of its metabolic networks. In this study, we investigated the CO2 fixation pathways (such as ko00710 and ko00720) most likely involved in the energy production and conversion of CO2 in Mtb. Extensive pathway evaluation of 23 completely sequenced strains of Mtb confirmed the existence of a complete list of genes encoding the relevant enzymes of the reductive tricarboxylic acid (rTCA) cycle. This provides the evidence that an rTCA cycle may function to fix CO2 in this bacterium. We also proposed that as CO2 is plentiful in the lungs, inhibition of CO2 fixation pathways (by targeting the relevant CO2 fixation enzymes) could be used in the expansion of new drugs against the dormant Mtb. In support of the suggested hypothesis, the CO2 fixation enzymes were confirmed as a potential drug target by analyzing a number of attributes necessary to be a good bacterial target.

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Drug Target Prediction Based on the Herbs Components: The Study on the Multitargets Pharmacological Mechanism of Qishenkeli Acting on the Coronary Heart Disease

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2012/698531 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10 ◽

Cited By ~ 20

Author(s):

Yong Wang ◽

Zhongyang Liu ◽

Chun Li ◽

Dong Li ◽

Yulin Ouyang ◽

...

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Angiotensin Ii ◽

Drug Target ◽

Drug Targets ◽

Target Prediction ◽

Coronary Artery Ligation ◽

Potential Drug ◽

Drug Target Prediction ◽

Potential Drug Targets

In this paper, we present a case study of Qishenkeli (QSKL) to research TCM’s underlying molecular mechanism, based on drug target prediction and analyses of TCM chemical components and following experimental validation. First, after determining the compositive compounds of QSKL, we use drugCIPHER-CS to predict their potential drug targets. These potential targets are significantly enriched with known cardiovascular disease-related drug targets. Then we find these potential drug targets are significantly enriched in the biological processes of neuroactive ligand-receptor interaction, aminoacyl-tRNA biosynthesis, calcium signaling pathway, glycine, serine and threonine metabolism, and renin-angiotensin system (RAAS), and so on. Then, animal model of coronary heart disease (CHD) induced by left anterior descending coronary artery ligation is applied to validate predicted pathway. RAAS pathway is selected as an example, and the results show that QSKL has effect on both rennin and angiotensin II receptor (AT1R), which eventually down regulates the angiotensin II (AngII). Bioinformatics combing with experiment verification can provide a credible and objective method to understand the complicated multitargets mechanism for Chinese herbal formula.

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Computational drug target and toxicity analysis among hypothetical proteins of mycobacterium tuberculosis H37RV strain

International Journal of Medical Toxicology & Legal Medicine ◽

10.5958/0974-4614.2020.00062.5 ◽

2020 ◽

Vol 23 (3and4) ◽

pp. 157-163

Author(s):

Suresh Kumar ◽

Aisha >Mariam-Fatima

Keyword(s):

Mycobacterium Tuberculosis ◽

Drug Target ◽

Tuberculosis H37rv ◽

Hypothetical Proteins ◽

Mycobacterium Tuberculosis H37rv ◽

H37rv Strain ◽

Toxicity Analysis

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Generation and Analysis of Large-Scale Data-Driven Mycobacterium tuberculosis Functional Networks for Drug Target Identification

Advances in Bioinformatics ◽

10.1155/2011/801478 ◽

2011 ◽

Vol 2011 ◽

pp. 1-14 ◽

Cited By ~ 15

Author(s):

Gaston K. Mazandu ◽

Nicola J. Mulder

Keyword(s):

Mycobacterium Tuberculosis ◽

Drug Targets ◽

Large Scale ◽

Global Analysis ◽

Interaction Network ◽

Interaction Networks ◽

Microbial Pathogens ◽

Potential Drug ◽

Functional Interaction ◽

Potential Drug Targets

Technological developments in large-scale biological experiments, coupled with bioinformatics tools, have opened the doors to computational approaches for the global analysis of whole genomes. This has provided the opportunity to look at genes within their context in the cell. The integration of vast amounts of data generated by these technologies provides a strategy for identifying potential drug targets within microbial pathogens, the causative agents of infectious diseases. As proteins are druggable targets, functional interaction networks between proteins are used to identify proteins essential to the survival, growth, and virulence of these microbial pathogens. Here we have integrated functional genomics data to generate functional interaction networks between Mycobacterium tuberculosis proteins and carried out computational analyses to dissect the functional interaction network produced for identifying drug targets using network topological properties. This study has provided the opportunity to expand the range of potential drug targets and to move towards optimal target-based strategies.

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