ArticleScientific reports2017
Data Intensive Genome Level Analysis for Identifying Novel, Non-Toxic Drug Targets for Multi Drug Resistant Mycobacterium tuberculosis.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- Telacebec Interferes with Virulence Lipid Biosynthesis Protein Expression and Sensitizes to Other Antibiotics.Microorganisms · 2023Article
- From Genome to Drugs: New Approaches in Antimicrobial Discovery.Frontiers in pharmacology · 2021Review
- Fluoroquinolone resistance and mutational profile of gyrA in pulmonary MDR tuberculosis patients.BMC pulmonary medicine · 2020Article
- A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis.Frontiers in chemistry · 2020Article
- Mycobacterial genomics and structural bioinformatics: opportunities and challenges in drug discovery.Emerging microbes & infections · 2019Review
- An integrative, multi-omics approach towards the prioritization of Klebsiella pneumoniae drug targets.Scientific reports · 2018Article
- Structure based drug discovery for designing leads for the non-toxic metabolic targets in multi drug resistant Mycobacterium tuberculosis.Journal of translational medicine · 2017Article
- Erratum: Data Intensive Genome Level Analysis for Identifying Novel, Non-Toxic Drug Targets for Multi Drug Resistant Mycobacterium tuberculosis.Scientific reports · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.