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Discovery of GPCR ligands for probing signal transduction pathways

Overview of attention for article published in Frontiers in Pharmacology, November 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

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Title
Discovery of GPCR ligands for probing signal transduction pathways
Published in
Frontiers in Pharmacology, November 2014
DOI 10.3389/fphar.2014.00255
Pubmed ID
Authors

Simone Brogi, Andrea Tafi, Laurent Désaubry, Canan G. Nebigil

Abstract

G protein-coupled receptors (GPCRs) are seven integral transmembrane proteins that are the primary targets of almost 30% of approved drugs and continue to represent a major focus of pharmaceutical research. All of GPCR targeted medicines were discovered by classical medicinal chemistry approaches. After the first GPCR crystal structures were determined, the docking screens using these structures lead to discovery of more novel and potent ligands. There are over 360 pharmaceutically relevant GPCRs in the human genome and to date about only 30 of structures have been determined. For these reasons, computational techniques such as homology modeling and molecular dynamics simulations have proven their usefulness to explore the structure and function of GPCRs. Furthermore, structure-based drug design and in silico screening (High Throughput Docking) are still the most common computational procedures in GPCRs drug discovery. Moreover, ligand-based methods such as three-dimensional quantitative structure-selectivity relationships, are the ideal molecular modeling approaches to rationalize the activity of tested GPCR ligands and identify novel GPCR ligands. In this review, we discuss the most recent advances for the computational approaches to effectively guide selectivity and affinity of ligands. We also describe novel approaches in medicinal chemistry, such as the development of biased agonists, allosteric modulators, and bivalent ligands for class A GPCRs. Furthermore, we highlight some knockout mice models in discovering biased signaling selectivity.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 1%
Switzerland 1 <1%
Germany 1 <1%
Italy 1 <1%
United States 1 <1%
Unknown 153 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 19%
Researcher 30 19%
Student > Master 24 15%
Student > Bachelor 20 13%
Other 6 4%
Other 21 13%
Unknown 27 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 39 25%
Chemistry 28 18%
Biochemistry, Genetics and Molecular Biology 25 16%
Medicine and Dentistry 12 8%
Pharmacology, Toxicology and Pharmaceutical Science 12 8%
Other 12 8%
Unknown 31 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 29 January 2015.
All research outputs
#7,204,207
of 22,772,779 outputs
Outputs from Frontiers in Pharmacology
#3,012
of 16,011 outputs
Outputs of similar age
#102,696
of 361,884 outputs
Outputs of similar age from Frontiers in Pharmacology
#12
of 59 outputs
Altmetric has tracked 22,772,779 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 16,011 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 80% 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 361,884 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.