Department of Computer Sciences, National Tsing Hua University, Hsinchu 300, Taiwan.
Biomed Res Int. 2013;2013:140237. doi: 10.1155/2013/140237. Epub 2013 Sep 26.
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.
对酶活性的了解有助于阐明在生命系统中由酶催化的数千种化学反应的代谢途径。复杂的应用，如药物设计和代谢重建，可以使用准确的酶反应注释来开发。由于准确的酶反应注释方法为这些应用创造了提高生产能力的潜力，因此它们在全球市场上受到了更多关注。我们提出了酶反应预测 (ERP) 方法，作为一种从结构域架构推断酶反应的新工具。我们使用了架构和反应之间的几种频率关系来提高单催化和多催化反应的注释率。后基因组时代高通量技术产生的大量信息提高了我们对生物数据的理解，尽管它在数据处理阶段带来了障碍。云计算提供的高计算能力导致传入数据量呈指数级增长。云服务还减轻了分析酶动力学数据所需的大规模内存空间的要求。我们的工具设计为单个执行文件；因此，它可以应用于支持多个查询的任何云平台。
