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Structural analyses of 2015-updated drug-resistant mutations in HIV-1 protease: an implication of protease inhibitor cross-resistance

Overview of attention for article published in BMC Bioinformatics, December 2016
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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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

news
1 news outlet
facebook
2 Facebook pages
wikipedia
1 Wikipedia page

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
30 Mendeley
citeulike
2 CiteULike
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Title
Structural analyses of 2015-updated drug-resistant mutations in HIV-1 protease: an implication of protease inhibitor cross-resistance
Published in
BMC Bioinformatics, December 2016
DOI 10.1186/s12859-016-1372-3
Pubmed ID
Authors

Chinh Tran-To Su, Wei-Li Ling, Wai-Heng Lua, Yu-Xuan Haw, Samuel Ken-En Gan

Abstract

Strategies to control HIV for improving the quality of patient lives have been aided by the Highly Active Anti-Retroviral Therapy (HAART), which consists of a cocktail of inhibitors targeting key viral enzymes. Numerous new drugs have been developed over the past few decades but viral resistances to these drugs in the targeted viral enzymes are increasingly reported. Nonetheless the acquired mutations often reduce viral fitness and infectivity. Viral compensatory secondary-line mutations mitigate this loss of fitness, equipping the virus with a broad spectrum of resistance against these drugs. While structural understanding of the viral protease and its drug resistance mutations have been well established, the interconnectivity and development of structural cross-resistance remain unclear. This paper reports the structural analyses of recent clinical mutations on the drug cross-resistance effects from various protease and protease inhibitors (PIs) complexes. Using the 2015 updated clinical HIV protease mutations, we constructed a structure-based correlation network and a minimum-spanning tree (MST) based on the following features: (i) topology of the PI-binding pocket, (ii) allosteric effects of the mutations, and (iii) protease structural stability. Analyis of the network and the MST of dominant mutations conferring resistance to the seven PIs (Atazanavir-ATV, Darunavir-DRV, Indinavir-IDV, Lopinavir-LPV, Nelfinavir-NFV, Saquinavir-SQV, and Tipranavir-TPV) showed that cross-resistance can develop easily across NFV, SQV, LPV, IDV, and DRV, but not for ATV or TPV. Through estimation of the changes in vibrational entropies caused by each reported mutation, some secondary mutations were found to destabilize protease structure. Our findings provide an insight into the mechanism of PI cross-resistance and may also be useful in guiding the selection of PI in clinical treatment to delay the onset of cross drug resistance.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 20%
Student > Bachelor 5 17%
Researcher 3 10%
Student > Doctoral Student 2 7%
Student > Ph. D. Student 2 7%
Other 4 13%
Unknown 8 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 23%
Chemistry 6 20%
Medicine and Dentistry 3 10%
Agricultural and Biological Sciences 2 7%
Computer Science 1 3%
Other 3 10%
Unknown 8 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 01 December 2020.
All research outputs
#2,836,806
of 22,925,760 outputs
Outputs from BMC Bioinformatics
#965
of 7,306 outputs
Outputs of similar age
#59,196
of 420,669 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 133 outputs
Altmetric has tracked 22,925,760 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,306 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 86% 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 420,669 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 133 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.