Max Planck Institute for Informatics, Campus E1 4, 66123 Saarbrucken, Germany.
BMC Bioinformatics. 2010 Jan 18;11 Suppl 1(Suppl 1):S31. doi: 10.1186/1471-2105-11-S1-S31.
Understanding of secondary metabolic pathway in plant is essential for finding druggable candidate enzymes. However, there are many enzymes whose functions are not yet discovered in organism-specific metabolic pathways. Towards identifying the functions of those enzymes, assignment of EC numbers to the enzymatic reactions they catalyze plays a key role, since EC numbers represent the categorization of enzymes on one hand, and the categorization of enzymatic reactions on the other hand.
We propose reaction graph kernels for automatically assigning EC numbers to unknown enzymatic reactions in a metabolic network. Reaction graph kernels compute similarity between two chemical reactions considering the similarity of chemical compounds in reaction and their relationships. In computational experiments based on the KEGG/REACTION database, our method successfully predicted the first three digits of the EC number with 83% accuracy. We also exhaustively predicted missing EC numbers in plant's secondary metabolism pathway. The prediction results of reaction graph kernels on 36 unknown enzymatic reactions are compared with an expert's knowledge. Using the same data for evaluation, we compared our method with E-zyme, and showed its ability to assign more number of accurate EC numbers.
Reaction graph kernels are a new metric for comparing enzymatic reactions.
理解植物的次生代谢途径对于寻找可成药的候选酶至关重要。然而，有许多酶在特定于生物体的代谢途径中其功能尚未被发现。为了确定这些酶的功能，对它们催化的酶促反应进行 EC 编号的分配起着关键作用，因为 EC 编号一方面代表了酶的分类，另一方面也代表了酶促反应的分类。
我们提出了反应图核函数，用于自动为代谢网络中的未知酶促反应分配 EC 编号。反应图核函数考虑反应中化合物的相似性及其关系，计算两个化学反应之间的相似性。在基于 KEGG/REACTION 数据库的计算实验中，我们的方法成功地以 83%的准确率预测了 EC 编号的前三位。我们还详尽地预测了植物次生代谢途径中缺失的 EC 编号。反应图核函数对 36 个未知酶促反应的预测结果与专家知识进行了比较。使用相同的数据进行评估，我们将我们的方法与 E-zyme 进行了比较，并展示了它分配更多准确 EC 编号的能力。
反应图核函数是比较酶促反应的一种新指标。
