Löhr Thomas, Kohlhoff Kai, Heller Gabriella T, Camilloni Carlo, Vendruscolo Michele
Department of Chemistry, University of Cambridge, Cambridge, UK.
Google Research, Mountain View, CA, USA.
Nat Comput Sci. 2021 Jan;1(1):71-78. doi: 10.1038/s43588-020-00003-w. Epub 2021 Jan 14.
The conformational and thermodynamic properties of disordered proteins are commonly described in terms of structural ensembles and free energy landscapes. To provide information on the transition rates between the different states populated by these proteins, it would be desirable to generalize this description to kinetic ensembles. Approaches based on the theory of stochastic processes can be particularly suitable for this purpose. Here, we develop a Markov state model and apply it to determine a kinetic ensemble of Aβ42, a disordered peptide associated with Alzheimer's disease. Through the Google Compute Engine, we generated 315-µs all-atom molecular dynamics trajectories. Using a probabilistic-based definition of conformational states in a neural network approach, we found that Aβ42 is characterized by inter-state transitions on the microsecond timescale, exhibiting only fully unfolded or short-lived, partially folded states. Our results illustrate how kinetic ensembles provide effective information about the structure, thermodynamics and kinetics of disordered proteins.
无序蛋白质的构象和热力学性质通常根据结构系综和自由能景观来描述。为了提供有关这些蛋白质所占据的不同状态之间转变速率的信息，将这种描述推广到动力学系综是很有必要的。基于随机过程理论的方法可能特别适合此目的。在这里，我们开发了一个马尔可夫状态模型，并将其应用于确定与阿尔茨海默病相关的无序肽Aβ42的动力学系综。通过谷歌计算引擎，我们生成了315微秒的全原子分子动力学轨迹。在神经网络方法中使用基于概率的构象状态定义，我们发现Aβ42的特征是在微秒时间尺度上的状态间转变，仅表现出完全展开或短暂的、部分折叠的状态。我们的结果说明了动力学系综如何提供有关无序蛋白质的结构、热力学和动力学的有效信息。
