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BioMed Research International
Volume 2013 (2013), Article ID 140237, 11 pages
http://dx.doi.org/10.1155/2013/140237
Research Article

Enzyme Reaction Annotation Using Cloud Techniques

1Department of Computer Sciences, National Tsing Hua University, Hsinchu 300, Taiwan
2Department of Computer Sciences and Information Engineering, Chang Gung University, Taoyuan 333, Taiwan
3Department of Applied Chemistry, National Chiao Tung University, Hsinchu 300, Taiwan
4Department of Computer Sciences and Information Engineering, Providence University, Taichung 433, Taiwan

Received 7 December 2012; Revised 12 July 2013; Accepted 19 July 2013

Academic Editor: Ching-Hsien (Robert) Hsu

Copyright © 2013 Chuan-Ching Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.