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Modulation of Coiled-Coil Dimer Stability through Surface Residues while Preserving Pairing Specificity

Overview of attention for article published in Journal of the American Chemical Society, June 2017
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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 (88th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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Title
Modulation of Coiled-Coil Dimer Stability through Surface Residues while Preserving Pairing Specificity
Published in
Journal of the American Chemical Society, June 2017
DOI 10.1021/jacs.7b01690
Pubmed ID
Authors

Igor Drobnak, Helena Gradišar, Ajasja Ljubetič, Estera Merljak, Roman Jerala

Abstract

The coiled-coil dimer is a widespread protein structural motif and due to its designability represents an attractive building block for assembling modular nanostructures. The specificity of coiled-coil dimer pairing is mainly based on the hydro-phobic and electrostatic interactions between residues at positions a and d, and e and g of the heptad repeat, respectively. Binding affinity, on the other hand, can also be affected by surface residues that face away from the dimerization interface. Here we show how design of the local helical propensity of interacting peptides can be used to tune the stabilities of coiled-coil dimers over a wide range. By designing intramolecular charge pairs, regions of high local helical propensity can be engineered to form trigger sequences and dimer stability is adjusted without changing the peptide length or any of the directly interacting residues. This general principle is demonstrated by a change in thermal stability by more than 30 °C as a result of only two mutations outside the binding interface. The same approach was successfully used to modulate the stabilities in an orthogonal set of coiled-coils without affecting their binding preferences. The stability effects of local helical propensity and peptide charge are well described by a simple linear model, which should help improve current coiled-coil stability prediction algorithms. Our findings enable tuning the stabilities of coiled-coil-based building modules match a diverse range of applications in synthetic biology and nanomaterials.

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The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 <1%
Unknown 138 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 27%
Student > Bachelor 19 14%
Researcher 18 13%
Student > Master 13 9%
Student > Doctoral Student 5 4%
Other 13 9%
Unknown 33 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 47 34%
Chemistry 31 22%
Agricultural and Biological Sciences 16 12%
Chemical Engineering 2 1%
Materials Science 2 1%
Other 6 4%
Unknown 35 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 22 June 2023.
All research outputs
#1,913,431
of 24,081,774 outputs
Outputs from Journal of the American Chemical Society
#3,954
of 64,096 outputs
Outputs of similar age
#37,573
of 320,802 outputs
Outputs of similar age from Journal of the American Chemical Society
#63
of 487 outputs
Altmetric has tracked 24,081,774 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 64,096 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has done particularly well, scoring higher than 93% 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 320,802 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 88% of its contemporaries.
We're also able to compare this research output to 487 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.