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Study of intra–inter species protein–protein interactions for potential drug targets identification and subsequent drug design for Escherichia coli O104:H4 C277-11

Overview of attention for article published in In Silico Pharmacology, April 2017
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Title
Study of intra–inter species protein–protein interactions for potential drug targets identification and subsequent drug design for Escherichia coli O104:H4 C277-11
Published in
In Silico Pharmacology, April 2017
DOI 10.1007/s40203-017-0021-5
Pubmed ID
Authors

Shakhinur Islam Mondal, Zabed Mahmud, Montasir Elahi, Arzuba Akter, Nurnabi Azad Jewel, Md. Muzahidul Islam, Sabiha Ferdous, Taisei Kikuchi

Abstract

Protein-protein interaction (PPI) and host-pathogen interactions (HPI) proteomic analysis has been successfully practiced for potential drug target identification in pathogenic infections. In this research, we attempted to identify new drug target based on PPI and HPI computation approaches and subsequently design new drug against devastating enterohemorrhagic Escherichia coli O104:H4 C277-11 (Broad), which causes life-threatening food borne disease outbreak in Germany and other countries in Europe in 2011. Our systematic in silico analysis on PPI and HPI of E. coli O104:H4 was able to identify bacterial D-galactose-binding periplasmic and UDP-N-acetylglucosamine 1-carboxyvinyltransferase as attractive candidates for new drug targets. Furthermore, computational three-dimensional structure modeling and subsequent molecular docking finally proposed [3-(5-Amino-7-Hydroxy-[1,2,3]Triazolo[4,5-D]Pyrimidin-2-Yl)-N-(3,5-Dichlorobenzyl)-Benzamide)] and (6-amino-2-[(1-naphthylmethyl)amino]-3,7-dihydro-8H-imidazo[4,5-g]quinazolin-8-one) as promising candidate drugs for further evaluation and development for E. coli O104:H4 mediated diseases. Identification of new drug target would be of great utility for humanity as the demand for designing new drugs to fight infections is increasing due to the developing resistance and side effects of current treatments. This research provided the basis for computer aided drug design which might be useful for new drug target identification and subsequent drug design for other infectious organisms.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 39%
Student > Doctoral Student 2 11%
Student > Master 2 11%
Student > Ph. D. Student 1 6%
Lecturer 1 6%
Other 1 6%
Unknown 4 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 28%
Biochemistry, Genetics and Molecular Biology 2 11%
Immunology and Microbiology 2 11%
Medicine and Dentistry 2 11%
Computer Science 1 6%
Other 3 17%
Unknown 3 17%
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 14 April 2017.
All research outputs
#20,413,129
of 22,963,381 outputs
Outputs from In Silico Pharmacology
#57
of 75 outputs
Outputs of similar age
#270,218
of 310,118 outputs
Outputs of similar age from In Silico Pharmacology
#1
of 1 outputs
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So far Altmetric has tracked 75 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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