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Theoretical Analysis of Competing Conformational Transitions in Superhelical DNA

Overview of attention for article published in PLoS Computational Biology, April 2012
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Title
Theoretical Analysis of Competing Conformational Transitions in Superhelical DNA
Published in
PLoS Computational Biology, April 2012
DOI 10.1371/journal.pcbi.1002484
Pubmed ID
Authors

Dina Zhabinskaya, Craig J. Benham

Abstract

We develop a statistical mechanical model to analyze the competitive behavior of transitions to multiple alternate conformations in a negatively supercoiled DNA molecule of kilobase length and specified base sequence. Since DNA superhelicity topologically couples together the transition behaviors of all base pairs, a unified model is required to analyze all the transitions to which the DNA sequence is susceptible. Here we present a first model of this type. Our numerical approach generalizes the strategy of previously developed algorithms, which studied superhelical transitions to a single alternate conformation. We apply our multi-state model to study the competition between strand separation and B-Z transitions in superhelical DNA. We show this competition to be highly sensitive to temperature and to the imposed level of supercoiling. Comparison of our results with experimental data shows that, when the energetics appropriate to the experimental conditions are used, the competition between these two transitions is accurately captured by our algorithm. We analyze the superhelical competition between B-Z transitions and denaturation around the c-myc oncogene, where both transitions are known to occur when this gene is transcribing. We apply our model to explore the correlation between stress-induced transitions and transcriptional activity in various organisms. In higher eukaryotes we find a strong enhancement of Z-forming regions immediately 5' to their transcription start sites (TSS), and a depletion of strand separating sites in a broad region around the TSS. The opposite patterns occur around transcript end locations. We also show that susceptibility to each type of transition is different in eukaryotes and prokaryotes. By analyzing a set of untranscribed pseudogenes we show that the Z-susceptibility just downstream of the TSS is not preserved, suggesting it may be under selection pressure.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 36%
Researcher 6 21%
Student > Bachelor 4 14%
Professor 3 11%
Student > Postgraduate 2 7%
Other 1 4%
Unknown 2 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 36%
Physics and Astronomy 5 18%
Computer Science 4 14%
Agricultural and Biological Sciences 4 14%
Chemistry 3 11%
Other 1 4%
Unknown 1 4%
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 15 May 2012.
All research outputs
#16,063,069
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#6,970
of 8,964 outputs
Outputs of similar age
#107,027
of 175,705 outputs
Outputs of similar age from PLoS Computational Biology
#67
of 100 outputs
Altmetric has tracked 25,394,764 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 8,964 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 175,705 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.