Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.
BMC Bioinformatics. 2011 Dec 14;12 Suppl 14(Suppl 14):S1. doi: 10.1186/1471-2105-12-S14-S1.
In contrast to the increasing number of the successful genome projects, there still remain many orphan metabolites for which their synthesis processes are unknown. Metabolites, including these orphan metabolites, can be classified into groups that share the same core substructures, originated from the same biosynthetic pathways. It is known that many metabolites are synthesized by adding up building blocks to existing metabolites. Therefore, it is proposed that, for any given group of metabolites, finding the core substructure and the branched substructures can help predict their biosynthetic pathway. There already have been many reports on the multiple graph alignment techniques to find the conserved chemical substructures in relatively small molecules. However, they are optimized for ligand binding and are not suitable for metabolomic studies.
We developed an efficient multiple graph alignment method named as MUCHA (Multiple Chemical Alignment), specialized for finding metabolic building blocks. This method showed the strength in finding metabolic building blocks with preserving the relative positions among the substructures, which is not achieved by simply applying the frequent graph mining techniques. Compared with the combined pairwise alignments, this proposed MUCHA method generally reduced computational costs with improving the quality of the alignment.
MUCHA successfully find building blocks of secondary metabolites, and has a potential to complement to other existing methods to reconstruct metabolic networks using reaction patterns.
与越来越多成功的基因组项目相比,仍有许多孤儿代谢物的合成过程未知。代谢物,包括这些孤儿代谢物,可以分为具有相同核心子结构的组,这些核心子结构来源于相同的生物合成途径。已知许多代谢物是通过向现有代谢物添加构建块来合成的。因此,有人提出,对于任何给定的代谢物组,找到核心子结构和分支子结构可以帮助预测其生物合成途径。已经有许多关于多图谱对齐技术的报告,用于在相对较小的分子中找到保守的化学子结构。然而,它们是针对配体结合进行优化的,不适合代谢组学研究。
我们开发了一种名为 MUCHA(多化学对齐)的高效多图谱对齐方法,专门用于寻找代谢构建块。该方法在保留子结构之间相对位置的同时,表现出了寻找代谢构建块的优势,这是简单应用频繁图挖掘技术所无法实现的。与组合成对对齐相比,这种新提出的 MUCHA 方法通常可以降低计算成本,同时提高对齐的质量。
MUCHA 成功地找到了次生代谢物的构建块,并有潜力补充其他现有的方法,以利用反应模式重建代谢网络。