Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States.
Chemical Sciences and Engineering Division, Argonne National Laboratory, Argonne, Illinois 60439, United States.
J Phys Chem B. 2021 Nov 18;125(45):12401-12412. doi: 10.1021/acs.jpcb.1c05820. Epub 2021 Nov 8.
Proteins have been found to inhabit a diverse set of three-dimensional structures. The dynamics that govern protein interconversion between structures happen over a wide range of time scales─picoseconds to seconds. Our understanding of protein functions and dynamics is largely reliant upon our ability to elucidate physically populated structures. From an experimental structural characterization perspective, we are often limited to measuring the ensemble-averaged structure both in the steady-state and time-resolved regimes. Generating kinetic models and understanding protein structure-function relationships require atomistic knowledge of the populated states in the ensemble. In this Perspective, we present ensemble refinement methodologies that integrate time-resolved experimental signals with molecular dynamics models. We first discuss integration of experimental structural restraints to molecular models in disordered protein systems that adhere to the principle of maximum entropy for creating a complete set of ensemble structures. We then propose strategies to find kinetic pathways between the refined structures, using time-resolved inputs to guide molecular dynamics trajectories and the use of inference to generate tailored stimuli to prepare a desired ensemble of protein states.
已经发现蛋白质存在于多种三维结构中。蛋白质结构之间相互转化的动力学过程发生在很宽的时间尺度范围内——从皮秒到秒。我们对蛋白质功能和动力学的理解在很大程度上依赖于我们阐明物理上占据的结构的能力。从实验结构特征化的角度来看,我们通常仅限于测量在稳态和时间分辨的体系中蛋白质的集合平均结构。生成动力学模型和理解蛋白质结构-功能关系需要在集合中占据状态的原子知识。在本观点中,我们提出了将时间分辨的实验信号与分子动力学模型集成的集合精修方法。我们首先讨论了将实验结构约束整合到遵循最大熵原理的无序蛋白质系统中的分子模型中,以创建完整的集合结构。然后,我们提出了在精修结构之间寻找动力学途径的策略,使用时间分辨的输入来指导分子动力学轨迹,并使用推断来生成定制的刺激来准备期望的蛋白质状态集合。