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功能至关重要。 在细菌毒力、细胞调节和其他生理作用中起作用，但大多数 高分辨率的结构方法成功地将这些蛋白质捕获在一个或多个细胞中， 两种构象状态光谱技术，如DEER和单分子 FRET可以产生构象系综的群体分布，但对构象系综这样做， 标签-标签距离的小集合。这个项目的驱动假设是， 计算模型不仅可以用来帮助改进蛋白质的构象， 集成使用DEER或smFRET数据，而且还通过预测 标签的最佳位置。因此，我们开发了新的方法1）预测 给定一个初始的、不完整的估计， 构象系综和2）更好地完善给定的构象系综 异构构象群体数据，可能来自多个实验 在不同条件下获得的资源。我们应用这些方法来深入了解 柔性分子识别和细胞入侵的奈瑟氏菌和进入细胞 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.