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Computational Approaches to Toll-Like Receptor 4 Modulation

Overview of attention for article published in Molecules, July 2016
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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5 tweeters

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1 Mendeley
Title
Computational Approaches to Toll-Like Receptor 4 Modulation
Published in
Molecules, July 2016
DOI 10.3390/molecules21080994
Pubmed ID
Authors

Jean-Marc Billod, Alessandra Lacetera, Joan Guzmán-Caldentey, Sonsoles Martín-Santamaría

Abstract

Toll-like receptor 4 (TLR4), along with its accessory protein myeloid differentiation factor 2 (MD-2), builds a heterodimeric complex that specifically recognizes lipopolysaccharides (LPS), which are present on the cell wall of Gram-negative bacteria, activating the innate immune response. Some TLR4 modulators are undergoing preclinical and clinical evaluation for the treatment of sepsis, inflammatory diseases, cancer and rheumatoid arthritis. Since the relatively recent elucidation of the X-ray crystallographic structure of the extracellular domain of TLR4, research around this fascinating receptor has risen to a new level, and thus, new perspectives have been opened. In particular, diverse computational techniques have been applied to decipher some of the basis at the atomic level regarding the mechanism of functioning and the ligand recognition processes involving the TLR4/MD-2 system at the atomic level. This review summarizes the reported molecular modeling and computational studies that have recently provided insights into the mechanism regulating the activation/inactivation of the TLR4/MD-2 system receptor and the key interactions modulating the molecular recognition process by agonist and antagonist ligands. These studies have contributed to the design and the discovery of novel small molecules with promising activity as TLR4 modulators.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 100%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 100%

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 28 June 2017.
All research outputs
#2,035,163
of 8,148,623 outputs
Outputs from Molecules
#366
of 3,800 outputs
Outputs of similar age
#82,240
of 257,175 outputs
Outputs of similar age from Molecules
#40
of 228 outputs
Altmetric has tracked 8,148,623 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 3,800 research outputs from this source. They receive a mean Attention Score of 2.2. This one has done particularly well, scoring higher than 90% 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 257,175 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 67% of its contemporaries.
We're also able to compare this research output to 228 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.