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Molecular modeling and structure-based drug discovery approach reveals protein kinases as off-targets for novel anticancer drug RH1

Overview of attention for article published in Medical Oncology, September 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

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6 X users
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1 Facebook page
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2 Google+ users

Citations

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17 Mendeley
Title
Molecular modeling and structure-based drug discovery approach reveals protein kinases as off-targets for novel anticancer drug RH1
Published in
Medical Oncology, September 2017
DOI 10.1007/s12032-017-1011-5
Pubmed ID
Authors

Pramodkumar P. Gupta, Virupaksha A. Bastikar, Dalius Kuciauskas, Shanker Lal Kothari, Jonas Cicenas, Mindaugas Valius

Abstract

Potential drug target identification and mechanism of action is an important step in drug discovery process, which can be achieved by biochemical methods, genetic interactions or computational conjectures. Sometimes more than one approach is implemented to mine out the potential drug target and characterize the on-target or off-target effects. A novel anticancer agent RH1 is designed as pro-drug to be activated by NQO1, an enzyme overexpressed in many types of tumors. However, increasing data show that RH1 can affect cells in NQO1-independent fashion. Here, we implemented the bioinformatics approach of modeling and molecular docking for search of RH1 targets among protein kinase species. We have examined 129 protein kinases in total where 96 protein kinases are in complexes with their inhibitor, 11 kinases were in the unbound state with any ligand and for 22 protein kinases 3D structure were modeled. Comparison of calculated free energy of binding of RH1 with indigenous kinase inhibitors binding efficiency as well as alignment of their pharmacophoric maps let us predict and ranked protein kinases such as KIT, CDK2, CDK6, MAPK1, NEK2 and others as the most prominent off-targets of RH1. Our finding opens new avenues in search of protein targets that might be responsible for curing cancer by new promising drug RH1 in NQO1-independent way.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 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 > Master 4 24%
Researcher 3 18%
Student > Ph. D. Student 3 18%
Professor > Associate Professor 2 12%
Student > Bachelor 2 12%
Other 1 6%
Unknown 2 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 29%
Biochemistry, Genetics and Molecular Biology 5 29%
Chemistry 2 12%
Computer Science 1 6%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Other 0 0%
Unknown 3 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 September 2017.
All research outputs
#3,931,409
of 23,001,641 outputs
Outputs from Medical Oncology
#85
of 1,301 outputs
Outputs of similar age
#69,280
of 315,600 outputs
Outputs of similar age from Medical Oncology
#4
of 17 outputs
Altmetric has tracked 23,001,641 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,301 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done particularly well, scoring higher than 93% of its peers.
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 315,600 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
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 has done well, scoring higher than 76% of its contemporaries.