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In silico work flow for scaffold hopping in Leishmania

Overview of attention for article published in BMC Research Notes, November 2014
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Title
In silico work flow for scaffold hopping in Leishmania
Published in
BMC Research Notes, November 2014
DOI 10.1186/1756-0500-7-802
Pubmed ID
Authors

Barnali Waugh, Ambarnil Ghosh, Dhananjay Bhattacharyya, Nanda Ghoshal, Rahul Banerjee

Abstract

Leishmaniasis,a broad spectrum of diseases caused by several sister species of protozoa belonging to family trypanosomatidae and genus leishmania , generally affects poorer sections of the populace in third world countries. With the emergence of strains resistant to traditional therapies and the high cost of second line drugs which generally have severe side effects, it becomes imperative to continue the search for alternative drugs to combat the disease. In this work, the leishmanial genomes and the human genome have been compared to identify proteins unique to the parasite and whose structures (or those of close homologues) are available in the Protein Data Bank. Subsequent to the prioritization of these proteins (based on their essentiality, virulence factor etc.), inhibitors have been identified for a subset of these prospective drug targets by means of an exhaustive literature survey. A set of three dimensional protein-ligand complexes have been assembled from the list of leishmanial drug targets by culling structures from the Protein Data Bank or by means of template based homology modeling followed by ligand docking with the GOLD software. Based on these complexes several structure based pharmacophores have been designed and used to search for alternative inhibitors in the ZINC database.Result: This process led to a list of prospective compounds which could serve as potential antileishmanials. These small molecules were also used to search the Drug Bank to identify prospective lead compounds already in use as approved drugs. Interestingly, paromomycin which is currently being used as an antileishmanial drug spontaneously appeared in the list, probably giving added confidence to the 'scaffold hopping' computational procedures adopted in this work.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 2%
Unknown 58 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 17%
Student > Bachelor 10 17%
Researcher 6 10%
Student > Master 6 10%
Student > Postgraduate 4 7%
Other 11 19%
Unknown 12 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 20%
Chemistry 11 19%
Biochemistry, Genetics and Molecular Biology 7 12%
Pharmacology, Toxicology and Pharmaceutical Science 7 12%
Medicine and Dentistry 5 8%
Other 6 10%
Unknown 11 19%
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 10 December 2014.
All research outputs
#17,731,702
of 22,770,070 outputs
Outputs from BMC Research Notes
#2,827
of 4,263 outputs
Outputs of similar age
#246,080
of 360,537 outputs
Outputs of similar age from BMC Research Notes
#69
of 123 outputs
Altmetric has tracked 22,770,070 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,263 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 29th percentile – i.e., 29% 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 360,537 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 123 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.