Enzyme Reaction Annotation Using Cloud Techniques

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 reacti...

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Bibliographic Details
Published in:BioMed research international 2013-01, Vol.2013 (2013), p.1-11
Main Authors: Tang, Chuan Yi, Chang, Cheng-Wen, Lin, Chun-Yuan, Huang, Chuan-Ching
Format: Article
Language:English
Subjects:
Catalysis
Cloud computing
Computational Biology
Enzymes
Enzymes - chemistry
Enzymes - metabolism
Kinetics
Methods
Molecular Sequence Annotation
Software
Technology application
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Summary: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.
ISSN:2314-6133
2314-6141
DOI:10.1155/2013/140237