Simulation-guided spectroscopy and refinement of heterogenous conformational ensembles

柔性蛋白质的异质构象集合对于它们的 在细菌毒力、细胞调节和其他生理作用中的作用，但大多数 高分辨率结构方法正是通过将这些蛋白质捕获在一个或多个蛋白质中而成功的 两种构象状态。光谱技术，如鹿和单分子 FRET可以产生构象系综的种群分布，但对 一组较小的标签-标签距离。这个项目的驱动假设是先进的 计算模型不仅可以用来帮助精炼蛋白质构象 使用鹿或smFRET数据的组合，也通过预测来指导这样的实验 标签的最佳位置。因此，我们开发了新的方法1)来预测 在给定初始的、不完整的估计的情况下，一组标签的最具信息量的放置 给出了构象系综和2)更好地提炼构象系综 异质构象种群数据，可能来自多个实验 在不同条件下获得的来源。我们应用这些方法来洞察 奈瑟氏菌的柔性分子识别和细胞入侵及进入细胞 控制神经传递的突触融合机制的调节。

The heterogeneous conformational ensembles of flexible proteins are critical to their function in bacterial virulence, cellular regulation, and other physiological roles, yet most high-resolution structural methods succeed precisely by trapping such proteins in one or two conformational states. Spectroscopic techniques such as DEER and single-molecule FRET can yield population distributions for conformational ensembles but do this for a small set of label-label distances. The driving hypothesis of this project is that advanced computational models can be used not only to help refine protein conformational ensembles using DEER or smFRET data but also to direct such experiments by predicting the optimal placement of labels. We therefore develop new methods 1) to predict the most informative placement for a set of labels given an initial, incomplete estimate of a conformational ensemble and 2) to better refine conformational ensembles given heterogeneous conformational population data, potentially from multiple experimental sources acquired under different conditions. We apply these methods to gain insight into flexible molecular recognition and cellular invasion by Neisseria and into cellular regulation of the synaptic fusion machinery that controls neurotransmission.