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A Two-State Model for the Dynamics of the Pyrophosphate Ion Release in Bacterial RNA Polymerase

Overview of attention for article published in PLoS Computational Biology, April 2013
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Title
A Two-State Model for the Dynamics of the Pyrophosphate Ion Release in Bacterial RNA Polymerase
Published in
PLoS Computational Biology, April 2013
DOI 10.1371/journal.pcbi.1003020
Pubmed ID
Authors

Lin-Tai Da, Fátima Pardo Avila, Dong Wang, Xuhui Huang

Abstract

The dynamics of the PPi release during the transcription elongation of bacterial RNA polymerase and its effects on the Trigger Loop (TL) opening motion are still elusive. Here, we built a Markov State Model (MSM) from extensive all-atom molecular dynamics (MD) simulations to investigate the mechanism of the PPi release. Our MSM has identified a simple two-state mechanism for the PPi release instead of a more complex four-state mechanism observed in RNA polymerase II (Pol II). We observed that the PPi release in bacterial RNA polymerase occurs at sub-microsecond timescale, which is ∼3-fold faster than that in Pol II. After escaping from the active site, the (Mg-PPi)(2-) group passes through a single elongated metastable region where several positively charged residues on the secondary channel provide favorable interactions. Surprisingly, we found that the PPi release is not coupled with the TL unfolding but correlates tightly with the side-chain rotation of the TL residue R1239. Our work sheds light on the dynamics underlying the transcription elongation of the bacterial RNA polymerase.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 1 2%
Russia 1 2%
Belgium 1 2%
Unknown 49 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 38%
Researcher 7 13%
Student > Master 5 10%
Student > Doctoral Student 3 6%
Student > Bachelor 3 6%
Other 4 8%
Unknown 10 19%
Readers by discipline Count As %
Chemistry 14 27%
Agricultural and Biological Sciences 10 19%
Biochemistry, Genetics and Molecular Biology 6 12%
Physics and Astronomy 3 6%
Chemical Engineering 2 4%
Other 5 10%
Unknown 12 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 June 2013.
All research outputs
#16,045,990
of 25,371,288 outputs
Outputs from PLoS Computational Biology
#6,967
of 8,958 outputs
Outputs of similar age
#125,250
of 212,585 outputs
Outputs of similar age from PLoS Computational Biology
#105
of 157 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,958 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 19th percentile – i.e., 19% 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 212,585 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 157 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.