Limits...
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.

Show MeSH
Entries containing domains SSF56112 and SSF57889.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3814047&req=5

fig2: Entries containing domains SSF56112 and SSF57889.

Mentions: (2) Succinctness Measurement of the Domain Architecture of Enzymes. One reaction can be catalyzed by various enzymes that can comprise a variety of domain architectures. Among them, each subset of one type of domain architecture could also include another type of an enzyme. The Succinctnessdomain_arch equation (1) is designed to identify the most relevant architecture. Given an enumerated domain subset called domain_arch, we collected a set of entries, Entriesdomain_arch, that have domain architectures containing domains in domain_arch. The number of reactions associated with the entries which have domain architecture that exactly match domain_arch is denoted as /ECsexact/. The number of reactions associated with the entries that have architectures containing domains in domain_arch is denoted as /ECsincluded/. The Succinctnessdomain_arch measurement is calculated as the ratio of /ECsexact/ to /ECsincluded/. The type of domain subset with a greater Succinctnessdomain_arch value is assigned higher priority among a set of architecture candidates for the query domain architecture. For example, a query architecture domain_arch consisting of domains SSF56112 and SSF57889 is involved in 10 entries involving 5 types of enzyme reactions, comprising 2.7.10.2, 2.7.11.1, 2.7.11.13, 2.7.1.107, and 1.3.1.74 (/ECsincluded/ = 5) in Figure 2. The exactly matched architecture {SSF56112, SSF57889} is associated with 3 reactions, 2.7.10.2, 2.7.11.1, and 2.7.11.13, such that the Succinctness{SSF56112, SSF57889} is estimated as 0.6. We assign priority to the candidate with the greatest succinctness value because the corresponding chemical reactions proceed without requiring auxiliary domains as follows:(1)Succinctnessdomain_arch=/ECsexact//ECsincluded/.


Enzyme reaction annotation using cloud techniques.

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

Entries containing domains SSF56112 and SSF57889.
© Copyright Policy
Related In: Results  -  Collection

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

fig2: Entries containing domains SSF56112 and SSF57889.
Mentions: (2) Succinctness Measurement of the Domain Architecture of Enzymes. One reaction can be catalyzed by various enzymes that can comprise a variety of domain architectures. Among them, each subset of one type of domain architecture could also include another type of an enzyme. The Succinctnessdomain_arch equation (1) is designed to identify the most relevant architecture. Given an enumerated domain subset called domain_arch, we collected a set of entries, Entriesdomain_arch, that have domain architectures containing domains in domain_arch. The number of reactions associated with the entries which have domain architecture that exactly match domain_arch is denoted as /ECsexact/. The number of reactions associated with the entries that have architectures containing domains in domain_arch is denoted as /ECsincluded/. The Succinctnessdomain_arch measurement is calculated as the ratio of /ECsexact/ to /ECsincluded/. The type of domain subset with a greater Succinctnessdomain_arch value is assigned higher priority among a set of architecture candidates for the query domain architecture. For example, a query architecture domain_arch consisting of domains SSF56112 and SSF57889 is involved in 10 entries involving 5 types of enzyme reactions, comprising 2.7.10.2, 2.7.11.1, 2.7.11.13, 2.7.1.107, and 1.3.1.74 (/ECsincluded/ = 5) in Figure 2. The exactly matched architecture {SSF56112, SSF57889} is associated with 3 reactions, 2.7.10.2, 2.7.11.1, and 2.7.11.13, such that the Succinctness{SSF56112, SSF57889} is estimated as 0.6. We assign priority to the candidate with the greatest succinctness value because the corresponding chemical reactions proceed without requiring auxiliary domains as follows:(1)Succinctnessdomain_arch=/ECsexact//ECsincluded/.

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