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：
10494226
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/10494226
关键词：
Address Affect Affinity Area Automobile Driving Binding Binding Proteins Biological Complex Computer Analysis Computer Models Data Development Drug Delivery Systems Entropy Equilibrium Event Exocytosis Fluorescence Resonance Energy Transfer Hybrids 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 Site Source Spectrum Analysis Structure Synapses Techniques Testing Time Virulence Work arm base cell growth regulation experimental study flexibility improved insight molecular dynamics molecular recognition nanomolar neurotransmission new therapeutic target pathogen receptor 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.

柔性蛋白的异质构象合奏对于它们的细菌毒力，细胞调节和其他生理作用中的功能，但大多数高分辨率结构方法通过将这种蛋白质捕获到一个或两个构象状态。光谱技术，例如鹿和单分子 FRET可以为构象合奏产生人口分布，但为此做到这一点少数标签标签距离。该项目的驾驶假设是计算模型不仅可以帮助提炼蛋白质构象使用鹿或SMFRET数据的合奏，但也通过预测来指导此类实验标签的最佳位置。因此，我们开发新方法1）给出了一组标签的最有用的放置构象合奏和2）更好地完善给定的构象合奏异质构象种群数据，可能来自多个实验来源在不同条件下获取。我们采用这些方法来洞悉奈瑟氏菌并进入细胞的柔性分子识别和细胞侵袭控制神经传递的突触融合机械的调节。