Hadadi Noushin, Hafner Jasmin, Soh Keng Cher, Hatzimanikatis Vassily
Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
Biotechnol J. 2017 Jan;12(1). doi: 10.1002/biot.201600464.
Reaction atom mappings track the positional changes of all of the atoms between the substrates and the products as they undergo the biochemical transformation. However, information on atom transitions in the context of metabolic pathways is not widely available in the literature. The understanding of metabolic pathways at the atomic level is of great importance as it can deconvolute the overlapping catabolic/anabolic pathways resulting in the observed metabolic phenotype. The automated identification of atom transitions within a metabolic network is a very challenging task since the degree of complexity of metabolic networks dramatically increases when we transit from metabolite-level studies to atom-level studies. Despite being studied extensively in various approaches, the field of atom mapping of metabolic networks is lacking an automated approach, which (i) accounts for the information of reaction mechanism for atom mapping and (ii) is extendable from individual atom-mapped reactions to atom-mapped reaction networks. Hereby, we introduce a computational framework, iAM.NICE (in silico Atom Mapped Network Integrated Computational Explorer), for the systematic atom-level reconstruction of metabolic networks from in silico labelled substrates. iAM.NICE is to our knowledge the first automated atom-mapping algorithm that is based on the underlying enzymatic biotransformation mechanisms, and its application goes beyond individual reactions and it can be used for the reconstruction of atom-mapped metabolic networks. We illustrate the applicability of our method through the reconstruction of atom-mapped reactions of the KEGG database and we provide an example of an atom-level representation of the core metabolic network of E. coli.
反应原子映射追踪底物和产物之间所有原子在经历生化转化时的位置变化。然而，关于代谢途径背景下原子转变的信息在文献中并不广泛可得。在原子水平上理解代谢途径非常重要，因为它可以解析导致观察到的代谢表型的重叠分解代谢/合成代谢途径。在代谢网络中自动识别原子转变是一项极具挑战性的任务，因为当我们从代谢物水平研究过渡到原子水平研究时，代谢网络的复杂程度会急剧增加。尽管已经用各种方法进行了广泛研究，但代谢网络的原子映射领域仍缺乏一种自动化方法，该方法（i）在原子映射中考虑反应机制信息，并且（ii）可从单个原子映射反应扩展到原子映射反应网络。在此，我们引入一个计算框架iAM.NICE（计算机模拟原子映射网络综合计算探索器），用于从计算机模拟标记的底物中系统地在原子水平上重建代谢网络。据我们所知，iAM.NICE是第一个基于潜在酶促生物转化机制的自动化原子映射算法，其应用不仅限于单个反应，还可用于重建原子映射的代谢网络。我们通过重建KEGG数据库的原子映射反应来说明我们方法的适用性，并提供大肠杆菌核心代谢网络的原子水平表示示例。
