Molecular simulations of enzymatic phosphorylation of disordered proteins and their condensates

Emanuele Zippo, Dorothee Dormann, Thomas Speck, Lukas Stelzl
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Abstract

Understanding the condensation and aggregation of intrinsically disordered proteins in a non-equilibrium environment is crucial for unraveling many biological processes. Active enzymes catalyse many processes by consuming chemical fuels such as ATP. Enzymes called kinases phosphorylate disordered regions of proteins and thus profoundly affect their properties and interactions. Protein phosphorylation is implicated in neurodegenerative diseases and may modulate pathogenesis. However, how protein sequence and molecular recognition of a disordered protein by kinases determine phosphorylation patterns is not understood. In principle, molecular dynamics simulations hold the promise to resolve how phosphorylation affects disordered proteins and their assemblies. In practice, chemically-detailed simulations of enzymatic reactions and the dynamics of enzymes are highly challenging, in particular it is difficult to verify whether implementations of driven simulations are thermodynamically consistent. We can now address this problem with residue-level coarse-grained molecular dynamics simulations, integrating Metropolis Monte Carlo steps to model chemical reactions. Importantly, we show how to verify by Markov-state modeling that the realisation of a non-equilibrium steady state satisfies local-detailed balance. We investigate TDP-43 phosphorylation by the kinase CK1δ in simulations, examining patterns of phosphorylation and assessing its preventive role in chain aggregation, which may be a cytoprotective mechanism in neurodegenerative diseases. We find that the degree of residue phosphorylation is determined by sequence preference and charges, rather than by the position in the chain. The phosphorylation frequency is also affected by the phosphorylation patterns, since the interactions between CK1δ and TDP-43 actively change after each reaction. For TDP-43, our simulations show condensates dissolution through phosphorylation with kinases binding to the condensates and phosphorylating TDP-43 in the condensates.
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无序蛋白质及其凝聚物酶磷酸化的分子模拟
了解内在无序蛋白质在非平衡环境中的凝结和聚集对于揭示许多生物过程至关重要。活性酶通过消耗 ATP 等化学燃料催化许多过程。被称为激酶的酶对蛋白质的无序区域进行磷酸化,从而对其特性和相互作用产生深远影响。蛋白质磷酸化与神经退行性疾病有关,并可能调节发病机制。然而,蛋白质序列和激酶对无序蛋白质的分子识别如何决定磷酸化模式尚不清楚。原则上,分子动力学模拟有望解决磷酸化如何影响紊乱蛋白质及其组装的问题。在实践中,对酶反应和酶的动力学进行化学详细模拟极具挑战性,特别是很难验证驱动模拟的实现是否符合热力学。现在,我们可以通过残基级粗粒度分子动力学模拟来解决这个问题,整合 Metropolis 蒙特卡洛步骤来模拟化学反应。重要的是,我们展示了如何通过马尔可夫状态建模来验证非平衡稳态的实现是否满足局部细节平衡。我们在模拟中研究了激酶 CK1δ 对 TDP-43 的磷酸化,考察了磷酸化的模式并评估了其在链聚集中的预防作用,这可能是神经退行性疾病中的一种细胞保护机制。我们发现,残基的磷酸化程度是由序列偏好和电荷决定的,而不是由链中的位置决定的。磷酸化频率还受到磷酸化模式的影响,因为每次反应后,CK1δ 和 TDP-43 之间的相互作用都会发生积极变化。就 TDP-43 而言,我们的模拟显示,凝集物通过磷酸化溶解,激酶与凝集物结合,并在凝集物中对 TDP-43 进行磷酸化。
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