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Exploring the Composition of Protein-Ligand Binding Sites on a Large Scale

Overview of attention for article published in PLoS Computational Biology, November 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

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2 X users
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2 patents

Citations

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69 Dimensions

Readers on

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119 Mendeley
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7 CiteULike
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Title
Exploring the Composition of Protein-Ligand Binding Sites on a Large Scale
Published in
PLoS Computational Biology, November 2013
DOI 10.1371/journal.pcbi.1003321
Pubmed ID
Authors

Nickolay A. Khazanov, Heather A. Carlson

Abstract

The residue composition of a ligand binding site determines the interactions available for diffusion-mediated ligand binding, and understanding general composition of these sites is of great importance if we are to gain insight into the functional diversity of the proteome. Many structure-based drug design methods utilize such heuristic information for improving prediction or characterization of ligand-binding sites in proteins of unknown function. The Binding MOAD database if one of the largest curated sets of protein-ligand complexes, and provides a source of diverse, high-quality data for establishing general trends of residue composition from currently available protein structures. We present an analysis of 3,295 non-redundant proteins with 9,114 non-redundant binding sites to identify residues over-represented in binding regions versus the rest of the protein surface. The Binding MOAD database delineates biologically-relevant "valid" ligands from "invalid" small-molecule ligands bound to the protein. Invalids are present in the crystallization medium and serve no known biological function. Contacts are found to differ between these classes of ligands, indicating that residue composition of biologically relevant binding sites is distinct not only from the rest of the protein surface, but also from surface regions capable of opportunistic binding of non-functional small molecules. To confirm these trends, we perform a rigorous analysis of the variation of residue propensity with respect to the size of the dataset and the content bias inherent in structure sets obtained from a large protein structure database. The optimal size of the dataset for establishing general trends of residue propensities, as well as strategies for assessing the significance of such trends, are suggested for future studies of binding-site composition.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Germany 2 2%
Italy 1 <1%
Korea, Republic of 1 <1%
Argentina 1 <1%
United Kingdom 1 <1%
Unknown 110 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 29%
Student > Master 17 14%
Researcher 15 13%
Student > Bachelor 10 8%
Professor 6 5%
Other 22 18%
Unknown 15 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 25%
Biochemistry, Genetics and Molecular Biology 24 20%
Chemistry 19 16%
Pharmacology, Toxicology and Pharmaceutical Science 8 7%
Computer Science 8 7%
Other 10 8%
Unknown 20 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 24 January 2022.
All research outputs
#5,187,899
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#3,941
of 8,964 outputs
Outputs of similar age
#55,918
of 315,555 outputs
Outputs of similar age from PLoS Computational Biology
#61
of 146 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 55% 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 315,555 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 82% of its contemporaries.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.