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Modeling the functional state of the reverse transcriptase of hepatitis B virus and its application to probing drug-protein interaction

Overview of attention for article published in BMC Bioinformatics, August 2016
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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 (80th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

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1 blog
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Citations

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Title
Modeling the functional state of the reverse transcriptase of hepatitis B virus and its application to probing drug-protein interaction
Published in
BMC Bioinformatics, August 2016
DOI 10.1186/s12859-016-1116-4
Pubmed ID
Authors

Xiaojun Xu, Hong Thai, Kathryn M. Kitrinos, Guoliang Xia, Anuj Gaggar, Matthew Paulson, Lilia Ganova-Raeva, Yury Khudyakov, James Lara

Abstract

Herein, the predicted atomic structures of five representative sequence variants of the reverse transcriptase protein (RT) of hepatitis B virus (HBV), sampled from patients with rapid or slow response to tenofovir disoproxil fumarate (TDF) treatment, have been examined to identify structural variations between them in order to assess structural and functional properties of HBV-RT variants associated with the differential responses to TDF treatment. We utilized a hybrid computational approach to model the atomistic structures of HBV-RT/DNA-RNA/dATP and HBV-RT/DNA-RNA/TFV-DP (tenofovir diphosphate) complexes with the native hybrid DNA-RNA substrate in place. Multi-nanosecond molecular dynamics (MD) simulations of HBV-RT/DNA-RNA/dATP complexes revealed strong coupling of the natural nucleotide substrate, dATP, to the active site of the RT, and the differential involvement of the two putative magnesium cations (Mg(2+)) at the active site, whereby one Mg(2+) directly bridges the interaction between dATP and HBV-RT and the other serves as a coordinator to maintain an optimal configuration of the active site. Solvated interaction energy (SIE) calculated in MD simulations of HBV-RT/DNA-RNA/TFV-DP complexes indicate no differential binding affinity between TFV-DP and HBV-RT variants identified in patients with slow or rapid response to TDF treatment. The predicted atomic structures accurately represent functional states of HBV-RT. The equivalent interaction between TFV-DP and each examined HBV-RT variants suggests that binding affinity of TFV-DP to HBV-RT is not associated with delayed viral clearance.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 33%
Researcher 3 17%
Student > Master 2 11%
Student > Doctoral Student 1 6%
Other 1 6%
Other 1 6%
Unknown 4 22%
Readers by discipline Count As %
Medicine and Dentistry 3 17%
Biochemistry, Genetics and Molecular Biology 2 11%
Agricultural and Biological Sciences 2 11%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Computer Science 1 6%
Other 3 17%
Unknown 6 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 18 October 2021.
All research outputs
#3,749,738
of 22,884,315 outputs
Outputs from BMC Bioinformatics
#1,425
of 7,298 outputs
Outputs of similar age
#64,336
of 337,459 outputs
Outputs of similar age from BMC Bioinformatics
#29
of 136 outputs
Altmetric has tracked 22,884,315 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,298 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 337,459 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 80% of its contemporaries.
We're also able to compare this research output to 136 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.