↓ Skip to main content

Enzyme discovery beyond homology: a unique hydroxynitrile lyase in the Bet v1 superfamily

Overview of attention for article published in Scientific Reports, May 2017
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

twitter
3 X users
facebook
1 Facebook page

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
60 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Enzyme discovery beyond homology: a unique hydroxynitrile lyase in the Bet v1 superfamily
Published in
Scientific Reports, May 2017
DOI 10.1038/srep46738
Pubmed ID
Authors

Elisa Lanfranchi, Tea Pavkov-Keller, Eva-Maria Koehler, Matthias Diepold, Kerstin Steiner, Barbara Darnhofer, Jürgen Hartler, Tom Van Den Bergh, Henk-Jan Joosten, Mandana Gruber-Khadjawi, Gerhard G. Thallinger, Ruth Birner-Gruenberger, Karl Gruber, Margit Winkler, Anton Glieder

Abstract

Homology and similarity based approaches are most widely used for the identification of new enzymes for biocatalysis. However, they are not suitable to find truly novel scaffolds with a desired function and this averts options and diversity. Hydroxynitrile lyases (HNLs) are an example of non-homologous isofunctional enzymes for the synthesis of chiral cyanohydrins. Due to their convergent evolution, finding new representatives is challenging. Here we show the discovery of unique HNL enzymes from the fern Davallia tyermannii by coalescence of transcriptomics, proteomics and enzymatic screening. It is the first protein with a Bet v1-like protein fold exhibiting HNL activity, and has a new catalytic center, as shown by protein crystallography. Biochemical properties of D. tyermannii HNLs open perspectives for the development of a complementary class of biocatalysts for the stereoselective synthesis of cyanohydrins. This work shows that systematic integration of -omics data facilitates discovery of enzymes with unpredictable sequences and helps to extend our knowledge about enzyme diversity.

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 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Austria 1 2%
Brazil 1 2%
Unknown 58 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 18%
Student > Bachelor 8 13%
Student > Master 7 12%
Researcher 6 10%
Professor 3 5%
Other 11 18%
Unknown 14 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 27%
Agricultural and Biological Sciences 13 22%
Chemistry 5 8%
Engineering 2 3%
Immunology and Microbiology 1 2%
Other 3 5%
Unknown 20 33%
Attention Score in Context

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 27 June 2018.
All research outputs
#13,243,031
of 23,344,526 outputs
Outputs from Scientific Reports
#57,900
of 126,236 outputs
Outputs of similar age
#150,169
of 311,842 outputs
Outputs of similar age from Scientific Reports
#1,895
of 4,170 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 126,236 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.3. This one has gotten more attention than average, scoring higher than 52% 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 311,842 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 4,170 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 53% of its contemporaries.