Nature of the Pre-Chemistry Ensemble in Mitogen-Activated Protein Kinases
Nature of the Pre-Chemistry Ensemble in Mitogen-Activated Protein Kinases
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DOI：
10.1016/j.jmb.2018.12.007
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发表时间：
2019-01-18
影响因子：
5.6
通讯作者：
Ghose, Ranajeet
中科院分区：
文献类型：
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作者：
Ghose, Ranajeet
尽管有大量的结构细节的对接相互作用，涉及促分裂原活化蛋白激酶（MAPK）和它们的基板，无序的磷酸受体的基板上的激酶催化元件瞬时相互作用的机制，并磷酸化，往往以高效率，仍然知之甚少。在这里，这种动态的相互作用进行了分析，在现有的生物物理和生化数据的背景下，ERK 2，一个原型MAPK。提出了一个关于涉及MAPK，其底物，和ATP的三元复合物的性质的假设，就在化学步骤之前（预化学复合物）。据推测，代表预化学复合物的溶液系综（预化学系综）包括由多个时间尺度上的动力学链接的几种构象。这些单独的构象具有不同的内在能力，通过化学步骤进行。因此，化学反应的总体速率与化学反应前集合的微观性质、其组成构象微观状态及其产生磷酸化产物的内在能力有关。虽然在原子或近原子细节中表征化学前系综内的这些微观状态是一个极具挑战性的命题，但采用实验数据驱动的计算方法的混合方法的最新发展似乎为实现这一目标提供了最有希望的途径。(C)2018爱思唯尔有限公司版权所有。
In spite of the availability of a significant amount of structural detail on docking interactions involving mitogen-activated protein kinases (MAPKs) and their substrates, the mechanism by which the disordered phospho-acceptor on the substrate transiently interacts with the kinase catalytic elements and is phosphorylated, often with high efficiency, remains poorly understood. Here, this dynamic interaction is analyzed in the context of available biophysical and biochemical data for ERK2, an archetypal MAPK. A hypothesis about the nature of the ternary complex involving a MAPK, its substrate, and ATP immediately prior to the chemical step (the pre-chemistry complex) is proposed. It is postulated that the solution ensemble (the pre-chemistry ensemble) representing the pre-chemistry complex comprises several conformations that are linked by dynamics on multiple timescales. These individual conformations possess different intrinsic abilities to proceed through the chemical step. The overall rate of chemistry is therefore related to the microscopic nature of the pre-chemistry ensemble, its constituent conformational microstates, and their intrinsic abilities to yield a phosphorylated product. While characterizing these microstates within the pre-chemistry ensemble in atomic or near-atomic detail is an extremely challenging proposition, recent developments in hybrid methodologies that employ computational approaches driven by experimental data appear to provide the most promising path forward toward achieving this goal. (C) 2018 Elsevier Ltd. All rights reserved.