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Enzyme reaction annotation using cloud techniques.

Huang CC, Lin CY, Chang CW, Tang CY - Biomed Res Int (2013)

Bottom Line: The deluge of information which arose from high-throughput techniques in the postgenomic era has improved our understanding of biological data, although it presents obstacles in the data-processing stage.The high computational capacity provided by cloud computing has resulted in an exponential growth in the volume of incoming data.Our tool is designed as a single execution file; thus, it could be applied to any cloud platform in which multiple queries are supported.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Sciences, National Tsing Hua University, Hsinchu 300, Taiwan.

ABSTRACT
An understanding of the activities of enzymes could help to elucidate the metabolic pathways of thousands of chemical reactions that are catalyzed by enzymes in living systems. Sophisticated applications such as drug design and metabolic reconstruction could be developed using accurate enzyme reaction annotation. Because accurate enzyme reaction annotation methods create potential for enhanced production capacity in these applications, they have received greater attention in the global market. We propose the enzyme reaction prediction (ERP) method as a novel tool to deduce enzyme reactions from domain architecture. We used several frequency relationships between architectures and reactions to enhance the annotation rates for single and multiple catalyzed reactions. The deluge of information which arose from high-throughput techniques in the postgenomic era has improved our understanding of biological data, although it presents obstacles in the data-processing stage. The high computational capacity provided by cloud computing has resulted in an exponential growth in the volume of incoming data. Cloud services also relieve the requirement for large-scale memory space required by this approach to analyze enzyme kinetic data. Our tool is designed as a single execution file; thus, it could be applied to any cloud platform in which multiple queries are supported.

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Related in: MedlinePlus

The report displayed in the case of an absence of any protein for the domain architecture {SSF54211, SSF54814}.
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fig10: The report displayed in the case of an absence of any protein for the domain architecture {SSF54211, SSF54814}.

Mentions: In the absence of a protein consisting of the same architecture (Figure 10), the subsets of domain architecture {SSF54211, SSF54814} are enumerated as {SSF54211} and {SSF54814}. After evaluating the 4 measurements used for enumerating the domain architecture, the candidate with the highest priority {SSF54814} is obtained. Similarly, an enzyme with this architecture is capable of catalyzing the reaction 2.7.7.8 independently and with succinctness value of 1. The relationship between the domain set {SSF54814} and enzyme reaction 2.7.7.8 is strong according to the consistency value of 0. Only one reaction, 2.7.7.8, is related to {SSF54814}; thus, Intensity2.7.7.8 is calculated as 1. The protein with the accession number D9PMT6 and UniProt ID D9PMT6_9ZZZZ consisting of this type of domain architecture {SSF54814} is listed in Figure 10.


Enzyme reaction annotation using cloud techniques.

Huang CC, Lin CY, Chang CW, Tang CY - Biomed Res Int (2013)

The report displayed in the case of an absence of any protein for the domain architecture {SSF54211, SSF54814}.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814047&req=5

fig10: The report displayed in the case of an absence of any protein for the domain architecture {SSF54211, SSF54814}.
Mentions: In the absence of a protein consisting of the same architecture (Figure 10), the subsets of domain architecture {SSF54211, SSF54814} are enumerated as {SSF54211} and {SSF54814}. After evaluating the 4 measurements used for enumerating the domain architecture, the candidate with the highest priority {SSF54814} is obtained. Similarly, an enzyme with this architecture is capable of catalyzing the reaction 2.7.7.8 independently and with succinctness value of 1. The relationship between the domain set {SSF54814} and enzyme reaction 2.7.7.8 is strong according to the consistency value of 0. Only one reaction, 2.7.7.8, is related to {SSF54814}; thus, Intensity2.7.7.8 is calculated as 1. The protein with the accession number D9PMT6 and UniProt ID D9PMT6_9ZZZZ consisting of this type of domain architecture {SSF54814} is listed in Figure 10.

Bottom Line: The deluge of information which arose from high-throughput techniques in the postgenomic era has improved our understanding of biological data, although it presents obstacles in the data-processing stage.The high computational capacity provided by cloud computing has resulted in an exponential growth in the volume of incoming data.Our tool is designed as a single execution file; thus, it could be applied to any cloud platform in which multiple queries are supported.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Sciences, National Tsing Hua University, Hsinchu 300, Taiwan.

ABSTRACT
An understanding of the activities of enzymes could help to elucidate the metabolic pathways of thousands of chemical reactions that are catalyzed by enzymes in living systems. Sophisticated applications such as drug design and metabolic reconstruction could be developed using accurate enzyme reaction annotation. Because accurate enzyme reaction annotation methods create potential for enhanced production capacity in these applications, they have received greater attention in the global market. We propose the enzyme reaction prediction (ERP) method as a novel tool to deduce enzyme reactions from domain architecture. We used several frequency relationships between architectures and reactions to enhance the annotation rates for single and multiple catalyzed reactions. The deluge of information which arose from high-throughput techniques in the postgenomic era has improved our understanding of biological data, although it presents obstacles in the data-processing stage. The high computational capacity provided by cloud computing has resulted in an exponential growth in the volume of incoming data. Cloud services also relieve the requirement for large-scale memory space required by this approach to analyze enzyme kinetic data. Our tool is designed as a single execution file; thus, it could be applied to any cloud platform in which multiple queries are supported.

Show MeSH
Related in: MedlinePlus