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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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Population of EC numbers in the universe data set according to the six NC-IUBMB classes.
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fig5: Population of EC numbers in the universe data set according to the six NC-IUBMB classes.

Mentions: To examine the feasibility of our method, we compiled data from the UniProtKB and SUPERFAMILY domains of the InterPro database. A total of 1,664,839 proteins are associated with 1,218 SUPERFAMILY domains and 3,306 4-numerical-block EC numbers. The population of the 6 NC-IUBMB classes is shown in Figure 5. If one type of domain architecture was only associated with one enzyme reaction, then we collect these entries as a single-EC set. Entries associated with more than one enzyme-catalyzed reaction were assigned to a multiple-EC set. There were single-EC entries and multiple-EC entries in both the training set and the testing set. The ratio of the number of single-EC entries to the number of the multiple-EC entries in the testing set was approximately 6 : 4. Detailed information is shown in the “Testing set” column in Table 3.


Enzyme reaction annotation using cloud techniques.

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

Population of EC numbers in the universe data set according to the six NC-IUBMB classes.
© Copyright Policy
Related In: Results  -  Collection

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

fig5: Population of EC numbers in the universe data set according to the six NC-IUBMB classes.
Mentions: To examine the feasibility of our method, we compiled data from the UniProtKB and SUPERFAMILY domains of the InterPro database. A total of 1,664,839 proteins are associated with 1,218 SUPERFAMILY domains and 3,306 4-numerical-block EC numbers. The population of the 6 NC-IUBMB classes is shown in Figure 5. If one type of domain architecture was only associated with one enzyme reaction, then we collect these entries as a single-EC set. Entries associated with more than one enzyme-catalyzed reaction were assigned to a multiple-EC set. There were single-EC entries and multiple-EC entries in both the training set and the testing set. The ratio of the number of single-EC entries to the number of the multiple-EC entries in the testing set was approximately 6 : 4. Detailed information is shown in the “Testing set” column in Table 3.

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