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Structural and inhibition analysis of novel sulfur-rich 2-mercaptobenzothiazole and 1,2,3-triazole ligands against Mycobacterium tuberculosis DprE1 enzyme

Overview of attention for article published in Journal of Molecular Modeling, July 2017
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Title
Structural and inhibition analysis of novel sulfur-rich 2-mercaptobenzothiazole and 1,2,3-triazole ligands against Mycobacterium tuberculosis DprE1 enzyme
Published in
Journal of Molecular Modeling, July 2017
DOI 10.1007/s00894-017-3403-z
Pubmed ID
Authors

Sumita Karan, Vipin K. Kashyap, Syed Shafi, Ajay K. Saxena

Abstract

Mycobacterium tuberculosis decaprenylphosphoryl-β-D-ribose oxidase (MtbDprE1) acts in concert with decaprenylphosphoryl-β-D-ribose 2-epimerase (MtbDprE2) and catalyzes the epimerization of DPR into DPA. DPA is the sole precursor for synthesis of arabinogalactan and lipoarabinomannan in the mycobacterial cell wall. MtbDprE1 is a unique antimalarial drug target and many covalent and non-covalent inhibitors against MtbDprE1 have been studied for their antituberculosis activities. In the current study, we have purified MtbDprE1 enzyme and synthesized six sulfur-rich 2-mercaptobenzothiazole and 1, 2, 3-triazole conjugated ligands and performed binding analysis with MtbDprE1. All ligands have shown competitive binding, as observed for other covalently and noncovalently bound MtbDprE1 inhibitors. Molecular docking analysis of six ligands with MtbDprE1 shows that they occupy the substrate binding pocket of MtbDprE1 and are stabilized by hydrogen bonds and van der Waals interactions. Our study shows that sulfur-rich 2-mercaptobenzothiazole ligands act as specific inhibitors against MtbDprE1 and could be used as antituberculosis agents.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 18%
Student > Ph. D. Student 3 18%
Student > Master 2 12%
Lecturer 1 6%
Professor 1 6%
Other 3 18%
Unknown 4 24%
Readers by discipline Count As %
Chemistry 4 24%
Biochemistry, Genetics and Molecular Biology 3 18%
Chemical Engineering 1 6%
Computer Science 1 6%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Other 2 12%
Unknown 5 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 July 2017.
All research outputs
#18,565,641
of 22,994,508 outputs
Outputs from Journal of Molecular Modeling
#529
of 822 outputs
Outputs of similar age
#242,997
of 316,999 outputs
Outputs of similar age from Journal of Molecular Modeling
#11
of 17 outputs
Altmetric has tracked 22,994,508 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 316,999 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.