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Building a toolbox of protein scaffolds for future immobilization of biocatalysts

Overview of attention for article published in Applied Microbiology and Biotechnology, July 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

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3 X users
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1 Facebook page

Citations

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33 Dimensions

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69 Mendeley
Title
Building a toolbox of protein scaffolds for future immobilization of biocatalysts
Published in
Applied Microbiology and Biotechnology, July 2018
DOI 10.1007/s00253-018-9252-6
Pubmed ID
Authors

Sarah Schmidt-Dannert, Guoqiang Zhang, Timothy Johnston, Maureen B. Quin, Claudia Schmidt-Dannert

Abstract

Biological materials that are genetically encoded and can self-assemble offer great potential as immobilization platforms in industrial biocatalysis. Protein-based scaffolds can be used for the spatial organization of enzymes, to stabilize the catalysts and provide optimal microenvironments for reaction sequences. In our previous work, we created a protein scaffold for enzyme localization by engineering the bacterial microcompartment shell protein EutM from Salmonella enterica. Here, we sought to expand this work by developing a toolbox of EutM proteins with different properties, with the potential to be used for future immobilization of enzymes. We describe the bioinformatic identification of hundreds of homologs of EutM from diverse microorganisms. We specifically select 13 EutM homologs from extremophiles for characterization, based on phylogenetic analyses. We synthesize genes encoding the novel proteins, clone and express them in E. coli, and purify the proteins. In vitro characterization shows that the proteins self-assemble into robust nano- and micron-scale architectures including protein nanotubes, filaments, and scaffolds. We explore the self-assembly characteristics from a sequence-based approach and create a synthetic biology platform for the coexpression of different EutM homologs as hybrid scaffolds with integrated enzyme attachment points. This work represents a step towards our goal of generating a modular toolbox for the rapid production of self-assembling protein-based materials for enzyme immobilization.

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

Geographical breakdown

Country Count As %
Unknown 69 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 23%
Student > Ph. D. Student 12 17%
Student > Bachelor 12 17%
Student > Doctoral Student 4 6%
Other 4 6%
Other 7 10%
Unknown 14 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 26 38%
Agricultural and Biological Sciences 11 16%
Chemical Engineering 6 9%
Engineering 4 6%
Computer Science 2 3%
Other 5 7%
Unknown 15 22%
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 07 August 2018.
All research outputs
#13,838,821
of 24,493,651 outputs
Outputs from Applied Microbiology and Biotechnology
#5,209
of 8,124 outputs
Outputs of similar age
#159,004
of 334,136 outputs
Outputs of similar age from Applied Microbiology and Biotechnology
#62
of 143 outputs
Altmetric has tracked 24,493,651 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 8,124 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 35th percentile – i.e., 35% 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 334,136 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 51% of its contemporaries.
We're also able to compare this research output to 143 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 57% of its contemporaries.