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Protease-based synthetic sensing and signal amplification

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, October 2014
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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 (92nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

blogs
1 blog
twitter
3 X users
patent
17 patents

Citations

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71 Dimensions

Readers on

mendeley
185 Mendeley
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Title
Protease-based synthetic sensing and signal amplification
Published in
Proceedings of the National Academy of Sciences of the United States of America, October 2014
DOI 10.1073/pnas.1405220111
Pubmed ID
Authors

Viktor Stein, Kirill Alexandrov

Abstract

The bottom-up design of protein-based signaling networks is a key goal of synthetic biology; yet, it remains elusive due to our inability to tailor-make signal transducers and receptors that can be readily compiled into defined signaling networks. Here, we report a generic approach for the construction of protein-based molecular switches based on artficially autoinhibited proteases. Using structure-guided design and directed protein evolution, we created signal transducers based on artificially autoinhibited proteases that can be activated following site-specific proteolysis and also demonstrate the modular design of an allosterically regulated protease receptor following recombination with an affinity clamp peptide receptor. Notably, the receptor's mode of action can be varied from >5-fold switch-OFF to >30-fold switch-ON solely by changing the length of the connecting linkers, demonstrating a high functional plasticity not previously observed in naturally occurring receptor systems. We also create an integrated signaling circuit based on two orthogonal autoinhibited protease units that can propagate and amplify molecular queues generated by the protease receptor. Finally, we present a generic two-component receptor architecture based on proximity-based activation of two autoinhibited proteases. Overall, the approach allows the design of protease-based signaling networks that, in principle, can be connected to any biological process.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 185 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Australia 1 <1%
United Kingdom 1 <1%
Slovenia 1 <1%
Uruguay 1 <1%
Denmark 1 <1%
Mexico 1 <1%
Greece 1 <1%
China 1 <1%
Other 0 0%
Unknown 174 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 30%
Researcher 36 19%
Student > Bachelor 18 10%
Student > Master 15 8%
Student > Doctoral Student 10 5%
Other 30 16%
Unknown 21 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 65 35%
Biochemistry, Genetics and Molecular Biology 50 27%
Engineering 11 6%
Chemistry 8 4%
Medicine and Dentistry 7 4%
Other 11 6%
Unknown 33 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 17 October 2023.
All research outputs
#1,657,014
of 24,625,114 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#21,782
of 101,438 outputs
Outputs of similar age
#19,118
of 266,001 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#376
of 966 outputs
Altmetric has tracked 24,625,114 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 101,438 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 38.8. This one has done well, scoring higher than 78% 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 266,001 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 966 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 61% of its contemporaries.