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Identification of reaction organization patterns that naturally cluster enzymatic transformations

Overview of attention for article published in BMC Systems Biology, May 2018
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Title
Identification of reaction organization patterns that naturally cluster enzymatic transformations
Published in
BMC Systems Biology, May 2018
DOI 10.1186/s12918-018-0583-9
Pubmed ID
Authors

Carlos Vazquez-Hernandez, Antonio Loza, Esteban Peguero-Sanchez, Lorenzo Segovia, Rosa-Maria Gutierrez-Rios

Abstract

Metabolic reactions are chemical transformations commonly catalyzed by enzymes. In recent years, the explosion of genomic data and individual experimental characterizations have contributed to the construction of databases and methodologies for the analysis of metabolic networks. Some methodologies based on graph theory organize compound networks into metabolic functional categories without preserving biochemical pathways. Other methods based on chemical group exchange and atom flow trace the conversion of substrates into products in detail, which is useful for inferring metabolic pathways. Here, we present a novel rule-based approach incorporating both methods that decomposes each reaction into architectures of compound pairs and loner compounds that can be organized into tree structures. We compared the tree structure-compound pairs to those reported in the KEGG-RPAIR dataset and obtained a match precision of 81%. The generated tree structures naturally clustered all reactions into general reaction patterns of compounds with similar chemical transformations. The match precision of each cluster was calculated and used to suggest reactant-pairs for which manual curation can be avoided because this is the main goal of the method. We evaluated catalytic processes in the clusters based on Enzyme Commission categories that revealed preferential use of enzyme classes. We demonstrate that the application of simple rules can enable the identification of reaction patterns reflecting metabolic reactions that transform substrates into products and the types of catalysis involved in these transformations. Our rule-based approach can be incorporated as the input in pathfinders or as a tool for the construction of reaction classifiers, indicating its usefulness for predicting enzyme catalysis.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 50%
Student > Bachelor 1 8%
Student > Ph. D. Student 1 8%
Unknown 4 33%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 25%
Biochemistry, Genetics and Molecular Biology 3 25%
Chemical Engineering 1 8%
Unknown 5 42%
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 06 June 2018.
All research outputs
#18,836,331
of 23,344,526 outputs
Outputs from BMC Systems Biology
#838
of 1,143 outputs
Outputs of similar age
#256,879
of 331,835 outputs
Outputs of similar age from BMC Systems Biology
#21
of 31 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,143 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.