Comparative Proteome Analysis of Mycobacterium Tuberculosis Strains - H37Ra, H37Rv, CCDC5180, and CAS/NITR204: A Step Forward to Identify Novel Drug Targets

2020 ◽  
Vol 17 (11) ◽  
pp. 1422-1431
Author(s):  
Shradheya R.R. Gupta ◽  
Ekta Gupta ◽  
Avnam Ohri ◽  
Sandeep Kumar Shrivastava ◽  
Sumita Kachhwaha ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Target ◽  
Drug Targets ◽  
Virulent Strain ◽  
Proteome Analysis ◽  
Target Protein ◽  
Central Asian ◽  
Virulent Strains ◽  
Novel Drug ◽  
Novel Drug Targets

Background: Mycobacterium tuberculosis is a causative agent of tuberculosis. It is a non-motile, acid-fast, obligatory aerobic bacterium. Finding novel drug targets in Mycobacterium tuberculosis has become extremely important as the bacterium is evolving into a more dangerous multi-drug resistant pathogen. The predominant strains in India belong to the Central-Asian, East- African Indian, and Beijing clad. For the same reason, the whole proteomes of a non-virulent strain (H37Ra), a virulent (H37Rv) and two clinical strains, a Central-Asian clad (CAS/NITR204) and a Beijing clad (CCDC5180) have been selected for comparative study. Selecting a phylogenetically close and majorly studied non-virulent strain is helpful in removing the common and undesired proteins from the study. Objective: The study compares the whole proteome of non-virulent strain with the other three virulent strains to find a unique protein responsible for virulence in virulent strains. It is expected that the drugs developed against identified targets will be specific to the virulent strains. Additionally, to assure minimal toxicity to the host, we also screened the human proteome. Methods: Comparative proteome analysis was used for target identification and in silico validation of identified target protein Rv2466c, identification of the respective ligand of the identified target protein and binding interaction study using Molecular docking and Molecular Dynamic Simulation study were used in this study. Results and Discussion: Finally, eleven proteins were found to be unique in virulent strain only and out of which, Rv2466c (PDB-ID: 4ZIL) was found to be an essential protein and identified as a putative drug target protein for further study. The compound glutathione was found to be a suitable inhibitor for Rv2466c. In this study, we used a comparative proteomics approach to identify novel target proteins. Conclusion: This study is unique as we are assured that the study will move forward the research in a new direction to cure the deadly disease (tuberculosis) caused by Mycobacterium tuberculosis. Rv2466c was identified as a novel drug target and glutathione as a respective ligand of Rv2466c. Discovery of the novel drug target as well as the drug will provide a solution to drug resistance as well as the infection caused by Mycobacterium tuberculosis.

scholarly journals Synthesis and anti-tubercular activity of 3-substituted benzothiophene-1,1-dioxides

2014 ◽  
Author(s):  
N. Susantha Chandrasekera ◽  
Mai A Bailey ◽  
Megan Files ◽  
Torey Alling ◽  
Stephanie K Florio ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Minimum Inhibitory Concentration ◽  
Mode Of Action ◽  
Drug Targets ◽  
Inhibitory Concentration ◽  
Eukaryotic Cells ◽  
Structure Activity ◽  
Novel Drug ◽  
Further Development ◽  
Novel Drug Targets

We demonstrated that the 3-substituted benzothiophene-1,1-dioxide class of compounds are effective inhibitors of Mycobacterium tuberculosis growth under aerobic conditions. We examined substitution at the C-3 position of the benzothiophene-1,1-dioxide series systematically to delineate structure-activity relationships influencing potency and cytotoxicity. Compounds were tested for inhibitory activity against virulent M. tuberculosis and eukaryotic cells. The tetrazole substituent was most potent, with a minimum inhibitory concentration (MIC) of 2.6 µM. However, cytotoxicity was noted with even more potency (Vero cell TC50 = 0.1 µM). Oxadiazoles had good anti-tubercular activity (MICs of 3–8 µM), but imidazoles, thiadiazoles and thiazoles had little activity. Cytotoxicity did not track with anti-tubercular activity, suggesting different targets or mode of action between bacterial and eukaryotic cells. However, we were unable to derive analogs without cytotoxicity; all compounds synthesized were cytotoxic (TC50 of 0.1–5 µM). We conclude that cytotoxicity is a liability in this series precluding it from further development. However, the series has potent anti-tubercular activity and future efforts towards identifying the mode of action could result in the identification of novel drug targets.

scholarly journals Synthesis and anti-tubercular activity of 3-substituted benzothiophene-1,1-dioxides

2014 ◽  
Author(s):  
N. Susantha Chandrasekera ◽  
Mai A Bailey ◽  
Megan Files ◽  
Torey Alling ◽  
Stephanie K Florio ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Minimum Inhibitory Concentration ◽  
Mode Of Action ◽  
Drug Targets ◽  
Inhibitory Concentration ◽  
Eukaryotic Cells ◽  
Structure Activity ◽  
Novel Drug ◽  
Further Development ◽  
Novel Drug Targets

We demonstrated that the 3-substituted benzothiophene-1,1-dioxide class of compounds are effective inhibitors of Mycobacterium tuberculosis growth under aerobic conditions. We examined substitution at the C-3 position of the benzothiophene-1,1-dioxide series systematically to delineate structure-activity relationships influencing potency and cytotoxicity. Compounds were tested for inhibitory activity against virulent M. tuberculosis and eukaryotic cells. The tetrazole substituent was most potent, with a minimum inhibitory concentration (MIC) of 2.6 µM. However, cytotoxicity was noted with even more potency (Vero cell TC50 = 0.1 µM). Oxadiazoles had good anti-tubercular activity (MICs of 3–8 µM), but imidazoles, thiadiazoles and thiazoles had little activity. Cytotoxicity did not track with anti-tubercular activity, suggesting different targets or mode of action between bacterial and eukaryotic cells. However, we were unable to derive analogs without cytotoxicity; all compounds synthesized were cytotoxic (TC50 of 0.1–5 µM). We conclude that cytotoxicity is a liability in this series precluding it from further development. However, the series has potent anti-tubercular activity and future efforts towards identifying the mode of action could result in the identification of novel drug targets.

scholarly journals Novel drug targets for Mycobacterium tuberculosis: 2-heterostyrylbenzimidazoles as inhibitors of cell wall protein synthesis

2017 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohana Rao Anguru ◽  
Ashok Kumar Taduri ◽  
Rama Devi Bhoomireddy ◽  
Malathi Jojula ◽  
Shravan Kumar Gunda
Keyword(s):  
Cell Wall ◽  
Protein Synthesis ◽  
Mycobacterium Tuberculosis ◽  
Drug Targets ◽  
Cell Wall Protein ◽  
Novel Drug ◽  
Novel Drug Targets

scholarly journals Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Jeremy J. Yang ◽  
Christopher R. Gessner ◽  
Joel L. Duerksen ◽  
Daniel Biber ◽  
Jessica L. Binder ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Human Health ◽  
Molecular Basis ◽  
Drug Target ◽  
Drug Targets ◽  
Complex Diseases ◽  
Knowledge Graph ◽  
Graph Analytics ◽  
Novel Drug ◽  
Novel Drug Targets

Abstract Background LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. Results Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. Conclusions The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

scholarly journals Insights into Novel Drug Targets in Mycobacterium tuberculosis: Where Do We Stand and Where Do We Go from Here?

2018 ◽  
Vol 2 (1) ◽  
pp. 14-21
Author(s):  
Safaa Kishk ◽  
Mohamed A. Helal ◽  
Mohamed S. Gomaa ◽  
Ismail Salama ◽  
Samia Moustafa ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Targets ◽  
Novel Drug ◽  
Novel Drug Targets

scholarly journals Metabolic pathway analysis in trypanosomes and malaria parasites

2002 ◽  
Vol 357 (1417) ◽  
pp. 101-107 ◽  
Author(s):  
Alan H. Fairlamb
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Target Identification ◽  
Potential Drug ◽  
Metabolic Pathway Analysis ◽  
Entire Genome ◽  
Microbial Responses ◽  
Novel Drug ◽  
Potential Drug Targets ◽  
Novel Drug Targets

Identification of novel drug targets is required for the development of new classes of drugs to overcome drug resistance and replace less efficacious treatments. In theory, knowledge of the entire genome of a pathogen identifies every potential drug target in any given microbe. In practice, the sheer complexity and the inadequate or inaccurate annotation of genomic information makes target identification and selection somewhat more difficult. Analysis of metabolic pathways provides a useful conceptual framework for the identification of potential drug targets and also for improving our understanding of microbial responses to nutritional, chemical and other environmental stresses. A number of metabolic databases are available as tools for such analyses. The strengths and weaknesses of this approach are discussed.

scholarly journals Knowledge graph analytics platform with LINCS and IDG for Parkinson’s disease target illumination

2021 ◽  
Author(s):  
Jeremy J Yang ◽  
Christopher R Gessner ◽  
Joel L Duerksen ◽  
Daniel Biber ◽  
Jessica L Binder ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Human Health ◽  
Molecular Basis ◽  
Drug Target ◽  
Drug Targets ◽  
Complex Diseases ◽  
Knowledge Graph ◽  
Graph Analytics ◽  
Novel Drug ◽  
Novel Drug Targets

AbstractBackgroundLINCS, “Library of Integrated Network-based Cellular Signatures”, and IDG, “Illuminating the Druggable Genome”, are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson’s disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches.ResultsIntegrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods, using Neo4j. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG’s resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD.ConclusionsThe synergy of LINCS and IDG, via KG methods, empowers graph analytic methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

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