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Simulation-guided spectroscopy and refinement of heterogenous conformational ensembles

模拟引导光谱学和异质构象系综的细化

基本信息

批准号：
10668473
负责人：
Peter M Kasson
金额：
$ 37.31万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-25 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10668473
关键词：
Address Affect Affinity Area Automobile Driving Binding Binding Proteins Biological Complex Computer Analysis Computer Models Data Development Drug Delivery Systems Equilibrium Event Exocytosis Fluorescence Resonance Energy Transfer Hybrids Invaded Joints Knowledge Label Measurement Measures Membrane Methodological Studies Methodology Methods Modeling Molecular Conformation Neisseria Neurons Pathogenesis Physiological Play Population Population Distributions Process Protein Conformation Proteins Regulation Relaxation Resolution Role SNAP receptor Sampling Site Source Spectrum Analysis Structure Synapses Techniques Testing Time Virulence Work arm cell growth regulation design experimental study flexibility improved insight molecular dynamics molecular recognition nanomolar neurotransmission new therapeutic target pathogen receptor restraint simulation single-molecule FRET syntaxin

项目摘要

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.

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。