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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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The case of existence of the same architecture protein for the domain architecture {SSF51110, SSF55486}.
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fig9: The case of existence of the same architecture protein for the domain architecture {SSF51110, SSF55486}.

Mentions: In the event that the same architecture protein (Figure 9) is found, a confirmation message is displayed and the domain architecture (Figure 9, {SSF51110, SSF55486}) is identified. The succinctness value of 1 indicates that an enzyme with this type of domain architecture is capable of catalyzing the reaction denoted as 3.4.24.21 without any auxiliary domains. The consistency value of 0 indicates that a strong relationship between the domain architecture {SSF51110, SSF55486} and enzyme reaction 3.4.24.21 exists and that an association with other enzyme reactions does not exist. Because only one associated enzyme reaction exists, the strength measurement Intensity3.4.24.21 is calculated as 1. The protein consisting of the architecture {SSF51110, SSF55486} is shown in Figure 9 as accession number F4KTN6 and UniProt ID F4KTN6_9SPHI.


Enzyme reaction annotation using cloud techniques.

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

The case of existence of the same architecture protein for the domain architecture {SSF51110, SSF55486}.
© Copyright Policy
Related In: Results  -  Collection

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

fig9: The case of existence of the same architecture protein for the domain architecture {SSF51110, SSF55486}.
Mentions: In the event that the same architecture protein (Figure 9) is found, a confirmation message is displayed and the domain architecture (Figure 9, {SSF51110, SSF55486}) is identified. The succinctness value of 1 indicates that an enzyme with this type of domain architecture is capable of catalyzing the reaction denoted as 3.4.24.21 without any auxiliary domains. The consistency value of 0 indicates that a strong relationship between the domain architecture {SSF51110, SSF55486} and enzyme reaction 3.4.24.21 exists and that an association with other enzyme reactions does not exist. Because only one associated enzyme reaction exists, the strength measurement Intensity3.4.24.21 is calculated as 1. The protein consisting of the architecture {SSF51110, SSF55486} is shown in Figure 9 as accession number F4KTN6 and UniProt ID F4KTN6_9SPHI.

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