Elucidating Angular Protein Motion using Kinetic Ensemble Refinement

项目概要/摘要 推进对关键蛋白质功能（如酶）背后的原子水平机制的理解 催化和变构调节，重要的是首先阐明蛋白质的真实表达， 溶液为了实现这一长期目标，我们将使用最近开发的动力学增强器 一种变换核磁共振（NMR）数据计算建模方式的方法 来解决蛋白质结构和测量蛋白质运动。核磁共振是最强大的技术之一， 阐明蛋白质的结构和动力学。它可以在溶液中进行研究（与X射线不同 晶体学），并可以捕捉关键的结构重排，因为它们发生在室温下（不像 冷冻电子显微术）。然而，尽管有这些优点， 对蛋白质结构解决方式背后的一个基本方面的改进，即 根据核奥弗豪泽效应（NOE）实验计算原子间距离。这些方法具有 仍然主要是定性的，导致大部分NMR结构的原子位置存在很大的不确定性。 此外，该领域几乎完全忽略了角运动和动力学如何影响NOE，导致 原子之间的距离比实际上要远得多。为了克服这些重大 缺陷，我们将实施和测试新的动力学集成为基础的细化算法， 比以前的方法更准确和物理现实，占两个角度 运动和动力学。为了消除大部分系统和随机结构误差， 从量化差的NMR谱，我们还将整合FitNMR峰量化所取得的进展 我们实验室最近开发的软件。这些方法将被用来创建更好的实验核磁共振 结构，侧链动力学的更详尽的模型，并确定解决方案和 以前所未有的细节呈现晶体状态。这项工作将允许更准确地确定 蛋白质部分的结构动力学表现出显著的波动，包括蛋白质活性位点， 调节区域和隐藏的结合位点。这些知识将促进我们对 蛋白质生物物理学和促进新疗法的合理设计。

PROJECT SUMMARY/ABSTRACT To advance the understanding of atomic-level mechanisms behind critical protein functions like enzyme catalysis and allosteric regulation, it is important to first elucidate a true representation of the protein in solution. In an effort to achieve this long term goal, we will use the recently developed Kinetic Ensemble approach to transform the way in which nuclear magnetic resonance (NMR) data is computationally modeled to solve protein structures and measure protein motions. NMR is one of the most powerful techniques for elucidating the structure and dynamics of proteins. It enables their study in solution (unlike x-ray crystallography) and can capture critical structural rearrangements as they happen at room temperature (unlike cryo-electron microscopy). However, despite these advantages, there have been relatively few practical improvements to one of the foundational aspects behind the way protein structures are solved, namely the calculation of interatomic distances from nuclear Overhauser effect (NOE) experiments. Such methods have remained largely qualitative, resulting in large uncertainties in the atomic positions for most NMR structures. Also, the field has almost completely ignored how angular motion and kinetics affect the NOE, resulting in atoms appearing much further away from one another than they actually are. To overcome these significant deficiencies, we will implement and test new Kinetic Ensemble-based refinement algorithms that are considerably more accurate and physically realistic than previous approaches, accounting for both angular motion and kinetics. To eliminate a significant fraction of the systematic and random structural errors resulting from poorly quantified NMR spectra, we will also integrate advances made by the FitNMR peak quantification software recently developed by our lab. These methods will be used to create better experimental NMR structures, more exhaustive models of side chain dynamics, and determine differences between solution and crystal states with unprecedented detail. This work will allow much more accurate determination of the structural dynamics in parts of the protein exhibiting significant fluctuations, including protein active sites, regulatory regions, and hidden binding sites. Such knowledge will advance our fundamental understanding of protein biophysics and facilitate rational design of new therapeutics.