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MUCHA: multiple chemical alignment algorithm to identify building block substructures of orphan secondary metabolites

机译:MUCHA:多种化学比对算法,用于鉴定孤儿次级代谢产物的结构单元亚结构

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BackgroundIn 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.ResultsWe 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.ConclusionsMUCHA successfully find building blocks of secondary metabolites, and has a potential to complement to other existing methods to reconstruct metabolic networks using reaction patterns.
机译:背景与成功的基因组计划数目不断增加相反,仍然有许多孤儿代谢产物的合成过程尚不清楚。代谢物,包括这些孤儿代谢物,可以分为源自相同生物合成途径的具有相同核心亚结构的组。已知许多代谢物是通过将结构单元加到现有代谢物上来合成的。因此,建议对于任何给定的代谢物组,找到核心亚结构和分支亚结构都可以帮助预测它们的生物合成途径。关于多重图对齐技术的报道已经很多,以发现相对较小分子中的保守化学亚结构。但是,它们针对配体结合进行了优化,不适合代谢组学研究。结果我们开发了一种有效的多图比对方法,称为MUCHA(多重化学比对),专门用于寻找代谢构件。该方法显示了在保留子结构之间的相对位置的情况下找到代谢构件的优势,而仅通过应用频繁的图挖掘技术就无法实现。与结合的成对比对相比,这种提议的MUCHA方法通常可以通过提高比对质量来降低计算成本。结论MUCHA成功地找到了次级代谢产物的构成部分,并有可能补充其他现有方法来利用反应模式重建代谢网络。

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