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Catalytic mechanism of type C sialidase from Streptococcus pneumoniae: from covalent intermediate to final product

Overview of attention for article published in Journal of Molecular Modeling, September 2018
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Title
Catalytic mechanism of type C sialidase from Streptococcus pneumoniae: from covalent intermediate to final product
Published in
Journal of Molecular Modeling, September 2018
DOI 10.1007/s00894-018-3822-5
Pubmed ID
Authors

Jing Xiong, Chunchun Zhang, Dingguo Xu

Abstract

Streptococcus pneumoniae is a Gram-positive human pathogenic bacterium, which is the main cause of pneumonia and meningitis in children and the elderly. Three sialidases (or neuraminidases) encoded from Streptococcus pneumoniae could catalyze the cleavage of sialic acid linkages. This mechanism is directly connected with infection, apoptosis, and signaling, and usually considered to be one of the critical virulence factors. Type C neuraminidase (NanC) is unique because its primary product of Neu5Ac2en is considered to be an inhibitor to the other two sialidases. Experimentally, there are two different pathways for the formation mechanism of Neu5Ac2en catalyzed by NanC. In this work, a combined quantum mechanical and molecular mechanical approach was employed in all calculations. Starting from the covalent sialylated intermediate, we first examined the reaction to Neu5Ac2en and found the reaction prefers a direct proton abstraction mechanism rather than the water mediated proton abstraction mechanism. Free energy profiles can confirm that Neu5Ac2en is the major product of NanC. Functional roles of some important residues were also investigated, e.g., D315 acts as the proton acceptor during the formation of Neu5Ac2en, while the general base for the hydrolytic reaction to Neu5Ac. This study can facilitate the understanding of the catalytic mechanism of NanC and has the potential to aid in future inhibitor design studies.

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Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Student > Doctoral Student 1 11%
Student > Bachelor 1 11%
Student > Master 1 11%
Researcher 1 11%
Other 1 11%
Unknown 1 11%
Readers by discipline Count As %
Medicine and Dentistry 2 22%
Chemistry 2 22%
Computer Science 1 11%
Pharmacology, Toxicology and Pharmaceutical Science 1 11%
Biochemistry, Genetics and Molecular Biology 1 11%
Other 1 11%
Unknown 1 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 September 2018.
All research outputs
#20,535,139
of 23,105,443 outputs
Outputs from Journal of Molecular Modeling
#642
of 827 outputs
Outputs of similar age
#296,864
of 341,556 outputs
Outputs of similar age from Journal of Molecular Modeling
#13
of 23 outputs
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So far Altmetric has tracked 827 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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