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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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Separating entries into certain types of an architecture with one EC number.
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fig3: Separating entries into certain types of an architecture with one EC number.

Mentions: (3) Multiplicity of Enzyme Reactions from One Type of Domain Architecture. An enzyme can catalyze different reactions; alternatively, different enzymes may share the same domain architecture. Considering a domain subset domain_arch, we collected all entries that have domain architectures containing domains of domain_arch. Among these entries, the number of involved reactions is defined similarly to the definition of /ECsincluded/ in the previous paragraph, but we denoted it as k for simplicity. To clearly observe the expression of one specific reaction among various architectures, we separated an entry with multiple reactions into several entries with a single reaction, and the number of entries with a single reaction is counted as N (Figure 3). Furthermore, we also mark the number of entries associated with each reaction ECi as ni (i = 1, …, k), such that N = ∑i=1kni. The mean value is calculated as the average number of entries, and the difference is estimated for each reaction ECi. Because k and Entriesdomain_arch are variables dependent on the set of domains in domain_arch, we provided Consistencydomain_arch (2), which summarizes the different terms and is normalized by N and weighted with (ni/N) for each reaction for comparison with other architecture candidates.


Enzyme reaction annotation using cloud techniques.

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

Separating entries into certain types of an architecture with one EC number.
© Copyright Policy
Related In: Results  -  Collection

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

fig3: Separating entries into certain types of an architecture with one EC number.
Mentions: (3) Multiplicity of Enzyme Reactions from One Type of Domain Architecture. An enzyme can catalyze different reactions; alternatively, different enzymes may share the same domain architecture. Considering a domain subset domain_arch, we collected all entries that have domain architectures containing domains of domain_arch. Among these entries, the number of involved reactions is defined similarly to the definition of /ECsincluded/ in the previous paragraph, but we denoted it as k for simplicity. To clearly observe the expression of one specific reaction among various architectures, we separated an entry with multiple reactions into several entries with a single reaction, and the number of entries with a single reaction is counted as N (Figure 3). Furthermore, we also mark the number of entries associated with each reaction ECi as ni (i = 1, …, k), such that N = ∑i=1kni. The mean value is calculated as the average number of entries, and the difference is estimated for each reaction ECi. Because k and Entriesdomain_arch are variables dependent on the set of domains in domain_arch, we provided Consistencydomain_arch (2), which summarizes the different terms and is normalized by N and weighted with (ni/N) for each reaction for comparison with other architecture candidates.

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