代谢和蛋白质序列的共同进化。

Moritz Schütte, Niels Klitgord, Daniel Segrè, Oliver Ebenhöh
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引用次数: 0

摘要

通过代谢途径产生和使用的一系列化学物质必须与催化它们的酶同步进化。这一共同历史路径的一个含义应该是,逐渐向生物圈水平的生化工具包中添加新的代谢反应的创新步骤与必须缓慢形成相应酶结构的渐进序列变化之间的对应关系。然而,长期共同进化的全球特征尚未被确定。在这里,我们通过计算代谢网络上的相互反应距离和相应酶蛋白的序列距离之间的相关性来搜索这些特征。我们使用KEGG数据库中所有已知的代谢反应集进行计算。代谢网络上的反应-反应距离计算为代谢网络投影上最短路径的长度,其中节点是反应,边表示去除辅因子后两个反应是否具有共同的代谢物。要有意义地估计酶序列之间的距离需要特别注意:对于每个酶的委托(EC)编号,我们使用COG数据库从KEGG中选择一组一致的蛋白质序列。我们将蛋白质序列之间的进化距离定义为两种酶之间的不对称转移概率,该概率来自相应的成对BLAST得分。通过将序列之间的距离与代谢反应图上的最小距离进行比较,我们发现两者之间存在很小但具有统计学意义的相关性。这表明酶序列空间的进化行走在一定程度上局部反映了代谢的逐渐扩张。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Co-evolution of metabolism and protein sequences.

The set of chemicals producible and usable by metabolic pathways must have evolved in parallel with the enzymes that catalyze them. One implication of this common historical path should be a correspondence between the innovation steps that gradually added new metabolic reactions to the biosphere-level biochemical toolkit, and the gradual sequence changes that must have slowly shaped the corresponding enzyme structures. However, global signatures of a long-term co-evolution have not been identified. Here we search for such signatures by computing correlations between inter-reaction distances on a metabolic network, and sequence distances of the corresponding enzyme proteins. We perform our calculations using the set of all known metabolic reactions, available from the KEGG database. Reaction-reaction distance on the metabolic network is computed as the length of the shortest path on a projection of the metabolic network, in which nodes are reactions and edges indicate whether two reactions share a common metabolite, after removal of cofactors. Estimating the distance between enzyme sequences in a meaningful way requires some special care: for each enzyme commission (EC) number, we select from KEGG a consensus set of protein sequences using the cluster of orthologous groups of proteins (COG) database. We define the evolutionary distance between protein sequences as an asymmetric transition probability between two enzymes, derived from the corresponding pair-wise BLAST scores. By comparing the distances between sequences to the minimal distances on the metabolic reaction graph, we find a small but statistically significant correlation between the two measures. This suggests that the evolutionary walk in enzyme sequence space has locally mirrored, to some extent, the gradual expansion of metabolism.

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