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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Transmembrane Helix Dynamics of Bacterial Chemoreceptors Supports a Piston Model of Signalling
|
|---|---|
| Published in |
PLoS Computational Biology, October 2011
|
| DOI | 10.1371/journal.pcbi.1002204 |
| Pubmed ID | |
| Authors |
Benjamin A. Hall, Judith P. Armitage, Mark S. P. Sansom |
| Abstract |
Transmembrane α-helices play a key role in many receptors, transmitting a signal from one side to the other of the lipid bilayer membrane. Bacterial chemoreceptors are one of the best studied such systems, with a wealth of biophysical and mutational data indicating a key role for the TM2 helix in signalling. In particular, aromatic (Trp and Tyr) and basic (Arg) residues help to lock α-helices into a membrane. Mutants in TM2 of E. coli Tar and related chemoreceptors involving these residues implicate changes in helix location and/or orientation in signalling. We have investigated the detailed structural basis of this via high throughput coarse-grained molecular dynamics (CG-MD) of Tar TM2 and its mutants in lipid bilayers. We focus on the position (shift) and orientation (tilt, rotation) of TM2 relative to the bilayer and how these are perturbed in mutants relative to the wildtype. The simulations reveal a clear correlation between small (ca. 1.5 Å) shift in position of TM2 along the bilayer normal and downstream changes in signalling activity. Weaker correlations are seen with helix tilt, and little/none between signalling and helix twist. This analysis of relatively subtle changes was only possible because the high throughput simulation method allowed us to run large (n = 100) ensembles for substantial numbers of different helix sequences, amounting to ca. 2000 simulations in total. Overall, this analysis supports a swinging-piston model of transmembrane signalling by Tar and related chemoreceptors. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 4 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 6 | 8% |
| United States | 4 | 5% |
| Germany | 2 | 3% |
| Sweden | 1 | 1% |
| Denmark | 1 | 1% |
| Vietnam | 1 | 1% |
| Unknown | 61 | 80% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 27 | 36% |
| Researcher | 17 | 22% |
| Student > Master | 7 | 9% |
| Student > Doctoral Student | 4 | 5% |
| Professor > Associate Professor | 4 | 5% |
| Other | 10 | 13% |
| Unknown | 7 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 27 | 36% |
| Chemistry | 15 | 20% |
| Biochemistry, Genetics and Molecular Biology | 13 | 17% |
| Physics and Astronomy | 6 | 8% |
| Engineering | 2 | 3% |
| Other | 8 | 11% |
| Unknown | 5 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 13 August 2022.
All research outputs
#14,276,973
of 25,371,288 outputs
Outputs from PLoS Computational Biology
#5,933
of 8,958 outputs
Outputs of similar age
#91,676
of 151,221 outputs
Outputs of similar age from PLoS Computational Biology
#61
of 130 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% 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 33rd percentile – i.e., 33% 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 151,221 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 130 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.