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Multiple conformational states in retrospective virtual screening – homology models vs. crystal structures: beta-2 adrenergic receptor case study

Overview of attention for article published in Journal of Cheminformatics, April 2015
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  • Good Attention Score compared to outputs of the same age (69th percentile)

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7 tweeters
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1 Facebook page

Citations

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6 Dimensions

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25 Mendeley
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Title
Multiple conformational states in retrospective virtual screening – homology models vs. crystal structures: beta-2 adrenergic receptor case study
Published in
Journal of Cheminformatics, April 2015
DOI 10.1186/s13321-015-0062-x
Pubmed ID
Authors

Stefan Mordalski, Jagna Witek, Sabina Smusz, Krzysztof Rataj, Andrzej J Bojarski

Abstract

Distinguishing active from inactive compounds is one of the crucial problems of molecular docking, especially in the context of virtual screening experiments. The randomization of poses and the natural flexibility of the protein make this discrimination even harder. Some of the recent approaches to post-docking analysis use an ensemble of receptor models to mimic this naturally occurring conformational diversity. However, the optimal number of receptor conformations is yet to be determined. In this study, we compare the results of a retrospective screening of beta-2 adrenergic receptor ligands performed on both the ensemble of receptor conformations extracted from ten available crystal structures and an equal number of homology models. Additional analysis was also performed for homology models with up to 20 receptor conformations considered. The docking results were encoded into the Structural Interaction Fingerprints and were automatically analyzed by support vector machine. The use of homology models in such virtual screening application was proved to be superior in comparison to crystal structures. Additionally, increasing the number of receptor conformational states led to enhanced effectiveness of active vs. inactive compounds discrimination. For virtual screening purposes, the use of homology models was found to be most beneficial, even in the presence of crystallographic data regarding the conformational space of the receptor. The results also showed that increasing the number of receptors considered improves the effectiveness of identifying active compounds by machine learning methods. Graphical abstractComparison of machine learning results obtained for various number of beta-2 AR homology models and crystal structures.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 4%
Denmark 1 4%
Germany 1 4%
Unknown 22 88%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 24%
Student > Ph. D. Student 5 20%
Researcher 4 16%
Student > Bachelor 2 8%
Professor 2 8%
Other 3 12%
Unknown 3 12%
Readers by discipline Count As %
Chemistry 8 32%
Agricultural and Biological Sciences 6 24%
Computer Science 3 12%
Biochemistry, Genetics and Molecular Biology 1 4%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 0 0%
Unknown 6 24%

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 21 April 2015.
All research outputs
#3,904,351
of 13,526,991 outputs
Outputs from Journal of Cheminformatics
#330
of 546 outputs
Outputs of similar age
#67,154
of 227,109 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 1 outputs
Altmetric has tracked 13,526,991 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 546 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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 227,109 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 69% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them