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BCL::Conf: small molecule conformational sampling using a knowledge based rotamer library

Overview of attention for article published in Journal of Cheminformatics, September 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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1 blog
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7 X users
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1 Facebook page

Citations

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Title
BCL::Conf: small molecule conformational sampling using a knowledge based rotamer library
Published in
Journal of Cheminformatics, September 2015
DOI 10.1186/s13321-015-0095-1
Pubmed ID
Authors

Sandeepkumar Kothiwale, Jeffrey L. Mendenhall, Jens Meiler

Abstract

The interaction of a small molecule with a protein target depends on its ability to adopt a three-dimensional structure that is complementary. Therefore, complete and rapid prediction of the conformational space a small molecule can sample is critical for both structure- and ligand-based drug discovery algorithms such as small molecule docking or three-dimensional quantitative structure-activity relationships. Here we have derived a database of small molecule fragments frequently sampled in experimental structures within the Cambridge Structure Database and the Protein Data Bank. Likely conformations of these fragments are stored as 'rotamers' in analogy to amino acid side chain rotamer libraries used for rapid sampling of protein conformational space. Explicit fragments take into account correlations between multiple torsion bonds and effect of substituents on torsional profiles. A conformational ensemble for small molecules can then be generated by recombining fragment rotamers with a Monte Carlo search strategy. BCL::Conf was benchmarked against other conformer generator methods including Confgen, Moe, Omega and RDKit in its ability to recover experimentally determined protein bound conformations of small molecules, diversity of conformational ensembles, and sampling rate. BCL::Conf recovers at least one conformation with a root mean square deviation of 2 Å or better to the experimental structure for 99 % of the small molecules in the Vernalis benchmark dataset. The 'rotamer' approach will allow integration of BCL::Conf into respective computational biology programs such as Rosetta.Graphical abstract:Conformation sampling is carried out using explicit fragment conformations derived from crystallographic structure databases. Molecules from the database are decomposed into fragments and most likely conformations/rotamers are used to sample correspondng sub-structure of a molecule of interest.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Czechia 1 <1%
Canada 1 <1%
Unknown 98 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 24%
Researcher 16 16%
Student > Master 12 12%
Student > Doctoral Student 9 9%
Student > Bachelor 6 6%
Other 16 16%
Unknown 18 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 27 27%
Chemistry 26 26%
Computer Science 9 9%
Agricultural and Biological Sciences 6 6%
Pharmacology, Toxicology and Pharmaceutical Science 4 4%
Other 7 7%
Unknown 22 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 22 May 2019.
All research outputs
#2,691,225
of 22,829,683 outputs
Outputs from Journal of Cheminformatics
#268
of 833 outputs
Outputs of similar age
#38,495
of 274,274 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 14 outputs
Altmetric has tracked 22,829,683 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 833 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one has gotten more attention than average, scoring higher than 67% 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 274,274 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 85% of its contemporaries.
We're also able to compare this research output to 14 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 71% of its contemporaries.