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Engineering an allosteric transcription factor to respond to new ligands

Overview of attention for article published in Nature Methods, December 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

news
5 news outlets
blogs
4 blogs
twitter
37 tweeters
patent
2 patents
facebook
1 Facebook page
googleplus
4 Google+ users

Citations

dimensions_citation
86 Dimensions

Readers on

mendeley
398 Mendeley
citeulike
2 CiteULike
Title
Engineering an allosteric transcription factor to respond to new ligands
Published in
Nature Methods, December 2015
DOI 10.1038/nmeth.3696
Pubmed ID
Authors

Noah D Taylor, Alexander S Garruss, Rocco Moretti, Sum Chan, Mark A Arbing, Duilio Cascio, Jameson K Rogers, Farren J Isaacs, Sriram Kosuri, David Baker, Stanley Fields, George M Church, Srivatsan Raman

Abstract

Genetic regulatory proteins inducible by small molecules are useful synthetic biology tools as sensors and switches. Bacterial allosteric transcription factors (aTFs) are a major class of regulatory proteins, but few aTFs have been redesigned to respond to new effectors beyond natural aTF-inducer pairs. Altering inducer specificity in these proteins is difficult because substitutions that affect inducer binding may also disrupt allostery. We engineered an aTF, the Escherichia coli lac repressor, LacI, to respond to one of four new inducer molecules: fucose, gentiobiose, lactitol and sucralose. Using computational protein design, single-residue saturation mutagenesis or random mutagenesis, along with multiplex assembly, we identified new variants comparable in specificity and induction to wild-type LacI with its inducer, isopropyl β-D-1-thiogalactopyranoside (IPTG). The ability to create designer aTFs will enable applications including dynamic control of cell metabolism, cell biology and synthetic gene circuits.

Twitter Demographics

The data shown below were collected from the profiles of 37 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 398 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 2%
United Kingdom 3 <1%
Belgium 2 <1%
Denmark 2 <1%
Germany 1 <1%
China 1 <1%
Brazil 1 <1%
Korea, Republic of 1 <1%
Switzerland 1 <1%
Other 4 1%
Unknown 376 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 143 36%
Researcher 89 22%
Student > Master 45 11%
Student > Bachelor 41 10%
Student > Postgraduate 19 5%
Other 56 14%
Unknown 5 1%
Readers by discipline Count As %
Agricultural and Biological Sciences 142 36%
Biochemistry, Genetics and Molecular Biology 131 33%
Chemistry 33 8%
Unspecified 31 8%
Engineering 25 6%
Other 31 8%
Unknown 5 1%

Attention Score in Context

This research output has an Altmetric Attention Score of 91. 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 09 May 2019.
All research outputs
#172,414
of 13,237,867 outputs
Outputs from Nature Methods
#250
of 3,758 outputs
Outputs of similar age
#6,208
of 357,628 outputs
Outputs of similar age from Nature Methods
#7
of 113 outputs
Altmetric has tracked 13,237,867 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,758 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.9. 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 357,628 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 98% of its contemporaries.
We're also able to compare this research output to 113 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.