利用粗粒度正态分析研究参与代谢途径的酶的内在动力学

Sarah M. Meeuwsen, An Nam Hodac, Lauren M. Adams, Ryan D. McMunn, Maxwell S. Anschutz, Kari J. Carothers, Rachel E. Egdorf, Peter M. Hanneman, Jonathan P. Kitzrow, Cynthia K. Keonigsberg, Óscar López-Martínez, P. A. Matthew, Ethan H. Richter, Jonathan E. Schenk, Heidi L. Schmit, M. Scott, Eva M. Volenec, S. Hati
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引用次数: 3

摘要

摘要蛋白质的内在动力学在其功能中起着重要作用。特别是,由蛋白质的整体结构定义的蛋白质的集体动力学在促进有利于底物结合和有效催化的活性位点构象方面很重要。蛋白质的一级序列决定其三维结构,编码独特的动力学。蛋白质的内在动力学实际上将蛋白质结构与其功能联系起来。在本研究中,进行了粗粒度正态模式分析,以检查参与初级代谢途径的24种不同酶的内在动力学模式。我们观察到,每种代谢酶都表现出独特的运动模式,这些模式在多个物种中是保守的,并且在功能上是相关的。动态互相关矩阵(DCCM)对于给定的酶家族明显相同,但与其他蛋白质家族的DCCM显著不同,这加强了具有相似功能的蛋白质表现出相似的运动模式。目前的工作还重申,基于未知蛋白质固有的迁移模式,正确识别未知蛋白质是可能的。
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Investigation of intrinsic dynamics of enzymes involved in metabolic pathways using coarse-grained normal mode analysis
Abstract Intrinsic dynamics of proteins are known to play important roles in their function. In particular, collective dynamics of a protein, which are defined by the protein’s overall architecture, are important in promoting the active site conformation that favors substrate binding and effective catalysis. The primary sequence of a protein, which determines its three-dimensional structure, encodes unique dynamics. The intrinsic dynamics of a protein actually link protein structure to its function. In the present study, coarse-grained normal mode analysis was performed to examine the intrinsic dynamic patterns of 24 different enzymes involved in primary metabolic pathways. We observed that each metabolic enzyme exhibits unique patterns of motions, which are conserved across multiple species and functionally relevant. Dynamic cross-correlation matrices (DCCMs) are visibly identical for a given enzyme family but significantly different from DCCMs of other protein families, reinforcing that proteins with similar function exhibit a similar pattern of motions. The present work also reasserted that correct identification of unknown proteins is possible based on their intrinsic mobility patterns.
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Cogent Biology
Cogent Biology MULTIDISCIPLINARY SCIENCES-
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