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Computational Design of a pH Stable Enzyme: Understanding Molecular Mechanism of Penicillin Acylase's Adaptation to Alkaline Conditions

Overview of attention for article published in PLOS ONE, June 2014
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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Title
Computational Design of a pH Stable Enzyme: Understanding Molecular Mechanism of Penicillin Acylase's Adaptation to Alkaline Conditions
Published in
PLOS ONE, June 2014
DOI 10.1371/journal.pone.0100643
Pubmed ID
Authors

Dmitry Suplatov, Nikolay Panin, Evgeny Kirilin, Tatyana Shcherbakova, Pavel Kudryavtsev, Vytas Švedas

Abstract

Protein stability provides advantageous development of novel properties and can be crucial in affording tolerance to mutations that introduce functionally preferential phenotypes. Consequently, understanding the determining factors for protein stability is important for the study of structure-function relationship and design of novel protein functions. Thermal stability has been extensively studied in connection with practical application of biocatalysts. However, little work has been done to explore the mechanism of pH-dependent inactivation. In this study, bioinformatic analysis of the Ntn-hydrolase superfamily was performed to identify functionally important subfamily-specific positions in protein structures. Furthermore, the involvement of these positions in pH-induced inactivation was studied. The conformational mobility of penicillin acylase in Escherichia coli was analyzed through molecular modeling in neutral and alkaline conditions. Two functionally important subfamily-specific residues, Gluβ482 and Aspβ484, were found. Ionization of these residues at alkaline pH promoted the collapse of a buried network of stabilizing interactions that consequently disrupted the functional protein conformation. The subfamily-specific position Aspβ484 was selected as a hotspot for mutation to engineer enzyme variant tolerant to alkaline medium. The corresponding Dβ484N mutant was produced and showed 9-fold increase in stability at alkaline conditions. Bioinformatic analysis of subfamily-specific positions can be further explored to study mechanisms of protein inactivation and to design more stable variants for the engineering of homologous Ntn-hydrolases with improved catalytic properties.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Spain 1 1%
Brazil 1 1%
Unknown 84 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 25%
Researcher 12 14%
Student > Master 10 11%
Student > Bachelor 10 11%
Student > Doctoral Student 6 7%
Other 11 13%
Unknown 17 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 27 31%
Agricultural and Biological Sciences 21 24%
Chemistry 8 9%
Engineering 3 3%
Unspecified 2 2%
Other 8 9%
Unknown 19 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 30 June 2014.
All research outputs
#13,060,986
of 22,757,541 outputs
Outputs from PLOS ONE
#102,965
of 194,187 outputs
Outputs of similar age
#107,548
of 228,106 outputs
Outputs of similar age from PLOS ONE
#2,053
of 4,423 outputs
Altmetric has tracked 22,757,541 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 194,187 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one is in the 46th percentile – i.e., 46% 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 228,106 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 52% of its contemporaries.
We're also able to compare this research output to 4,423 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.