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FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants

Overview of attention for article published in PLoS Computational Biology, November 2015
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)

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Citations

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Title
FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants
Published in
PLoS Computational Biology, November 2015
DOI 10.1371/journal.pcbi.1004556
Pubmed ID
Authors

David Bednar, Koen Beerens, Eva Sebestova, Jaroslav Bendl, Sagar Khare, Radka Chaloupkova, Zbynek Prokop, Jan Brezovsky, David Baker, Jiri Damborsky

Abstract

There is great interest in increasing proteins' stability to enhance their utility as biocatalysts, therapeutics, diagnostics and nanomaterials. Directed evolution is a powerful, but experimentally strenuous approach. Computational methods offer attractive alternatives. However, due to the limited reliability of predictions and potentially antagonistic effects of substitutions, only single-point mutations are usually predicted in silico, experimentally verified and then recombined in multiple-point mutants. Thus, substantial screening is still required. Here we present FireProt, a robust computational strategy for predicting highly stable multiple-point mutants that combines energy- and evolution-based approaches with smart filtering to identify additive stabilizing mutations. FireProt's reliability and applicability was demonstrated by validating its predictions against 656 mutations from the ProTherm database. We demonstrate that thermostability of the model enzymes haloalkane dehalogenase DhaA and γ-hexachlorocyclohexane dehydrochlorinase LinA can be substantially increased (ΔTm = 24°C and 21°C) by constructing and characterizing only a handful of multiple-point mutants. FireProt can be applied to any protein for which a tertiary structure and homologous sequences are available, and will facilitate the rapid development of robust proteins for biomedical and biotechnological applications.

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X Demographics

The data shown below were collected from the profiles of 4 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 202 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Canada 2 <1%
Italy 1 <1%
India 1 <1%
Japan 1 <1%
Argentina 1 <1%
Unknown 194 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 24%
Researcher 34 17%
Student > Bachelor 23 11%
Student > Master 23 11%
Student > Doctoral Student 10 5%
Other 28 14%
Unknown 36 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 69 34%
Agricultural and Biological Sciences 43 21%
Chemistry 20 10%
Computer Science 8 4%
Engineering 5 2%
Other 19 9%
Unknown 38 19%
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 09 September 2020.
All research outputs
#14,915,476
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#6,346
of 8,960 outputs
Outputs of similar age
#141,198
of 296,418 outputs
Outputs of similar age from PLoS Computational Biology
#133
of 194 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 27th percentile – i.e., 27% 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 296,418 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 194 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.