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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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Workflow of querying a domain architecture in the ERP model.
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fig7: Workflow of querying a domain architecture in the ERP model.

Mentions: A substantial demand exists for enzymes for industrial and medical applications in the global market; thus, enzyme function annotation is receiving considerable attention because it offers reductions in the cost of chemical processes. In this study, we proposed the ERP tool for annotating enzyme reactions based on the query domain architecture (Figure 7). After providing the domain architecture of a protein, the tool is used to determine whether available enzyme reactions exist; if not, an absence message is displayed. If enzyme reactions are available, the ERP tool is used to locate one type of the same domain architecture such that the corresponding enzyme reactions could be obtained with confidence. If the same architecture is not found, the next most promising subset is chosen from the given domain architecture, and its corresponding enzyme reactions are provided. If a similar domain architecture or a domain subset exists, proteins consisting of this architecture are displayed.


Enzyme reaction annotation using cloud techniques.

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

Workflow of querying a domain architecture in the ERP model.
© Copyright Policy
Related In: Results  -  Collection

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

fig7: Workflow of querying a domain architecture in the ERP model.
Mentions: A substantial demand exists for enzymes for industrial and medical applications in the global market; thus, enzyme function annotation is receiving considerable attention because it offers reductions in the cost of chemical processes. In this study, we proposed the ERP tool for annotating enzyme reactions based on the query domain architecture (Figure 7). After providing the domain architecture of a protein, the tool is used to determine whether available enzyme reactions exist; if not, an absence message is displayed. If enzyme reactions are available, the ERP tool is used to locate one type of the same domain architecture such that the corresponding enzyme reactions could be obtained with confidence. If the same architecture is not found, the next most promising subset is chosen from the given domain architecture, and its corresponding enzyme reactions are provided. If a similar domain architecture or a domain subset exists, proteins consisting of this architecture are displayed.

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