Calculating Ensembles of Discrete Dynamic Complexes and Condensed States of Intrinsically Disordered Proteins

计算离散动态复合体和本质无序蛋白质的凝聚态的系综

基本信息

批准号：
10607371
负责人：
Teresa L. Head-Gordon
金额：
$ 32.59万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-05-01 至 2027-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10607371
关键词：
Address Adopted Back Bayesian Modeling Binding Proteins Biological Biology C-terminal Cardiovascular Diseases Chemicals Collaborations Color Complex Computer software Computing Methodologies Data Development Disease Elasticity Elastin Electrostatics Eukaryotic Initiation Factors Fluorescence Fluorescence Resonance Energy Transfer Generations Grain Grant Human IGFBP2 gene Inosine Diphosphate Letters Machine Learning Malignant Neoplasms Measures Methods Modeling Pathologic Phase Phosphorylation Physical condensation Post-Translational Protein Processing Prevalence Proteins Proteome RNA Regulation Relaxation Research Residual state Roentgen Rays Role Sampling Spectrum Analysis Structure Surface Translational Regulation Translations Tropoelastin Update Validation X-Ray Crystallography autism spectrum disorder beamline computerized tools experimental study frontier insight link protein machine learning method molecular dynamics monomer novel physical model protein folding protein structure function single molecule single-molecule FRET structural biology three dimensional structure tool

项目摘要

The traditional structure-function paradigm has provided significant insights for well-folded proteins in which structures can be easily and rapidly revealed by X-ray crystallography beamlines and NMR. However approximately one third of the human proteome are comprised of intrinsically disordered proteins and regions that do not adopt a dominant well-folded structure, and therefore remain “unseen” by traditional structural biology methods. Current experimental and computational approaches to structural descriptions of disordered proteins, while often valuable, still lack predictive power, particularly for dynamic complexes of IDPs, as well as lack of insight into the relationships between IDP structural ensembles and function. We made significant progress in the last grant cycle in the following four directions: (1) Generating and quantifying the utility of experimental data types for IDP monomer ensembles; (2) Applying atomistic and coarse-grained physical models and machine learned sampling methods for generating monomer ensembles; (3) Advancing new Bayesian models for IDP monomer ensemble selection; (4) development of highly novel machine learning (ML) methodology for ensemble generation and selection; (5) Creating software and monomer ensemble data and placing them in the hands of practitioners. Many of these results serve as preliminary studies for this renewal and are described in more detail in proposed research. But to fully address the biological activity of IDPs we propose to adapt these computational methods further and develop new integrative biology tools that will be more selective for dynamic associations of IDPs within both discrete dynamic complexes and biological condensates and for post-translational modifications (PTMs) that create relevant IDP functional states. Building on strong preliminary data from our experimental collaborators, we will record NMR, SAXS and single molecule fluorescence data on phosphorylated and non-phosphorylated 4E-binding protein 2 (np-4E-BP2, 5p-4E-BP2) and its dynamic complex with the eukaryotic translation initiation factor (eIF4E); tropoelastin and mixed and condensed-phase elastin fragments; and mixed and condensed-phase CAPRIN1 C-terminal IDR, including novel NMR experiments that probe electrostatic potentials (ESPs), 3-color smFRET, and fluorescence correlation spectroscopy (FCS). These studies will illuminate mechanisms of translational regulation and elasticity, and provide insights into pathological states, including autism spectrum disorder and cardiovascular disease.

传统的结构-功能范式为折叠良好的蛋白质提供了重要的见解，其中通过 X 射线晶体学束线和 NMR 可以轻松快速地揭示结构。大约三分之一的人类蛋白质组由本质上无序的蛋白质和区域组成不采用占主导地位的良好折叠结构，因此传统结构“看不到” 生物学方法。目前对无序结构描述的实验和计算方法。蛋白质虽然通常很有价值，但仍然缺乏预测能力，特别是对于国内流离失所者的动态复合物，以及我们缺乏对 IDP 结构整体和功能之间关系的深入了解。上一个资助周期在以下四个方向取得的进展： (1) 生成和量化 IDP单体系综的实验数据类型；（2）应用原子和粗粒度物理用于生成单体集成的模型和机器学习采样方法；（3）推进新的用于 IDP 单体集成选择的贝叶斯模型；（4）开发高度新颖的机器学习（ML）集成生成和选择的方法；（5）创建软件和单体集成数据和将它们交给实践者，其中许多结果可以作为这次更新的初步研究。并在拟议的研究中进行了更详细的描述，但为了充分解决国内流离失所者的生物活性，我们建议进一步调整这些计算方法并开发新的综合生物学工具对离散动态复合体和生物体内 IDP 的动态关联更具选择性凝结物和翻译后修饰（PTM），以创建相关的 IDP 功能状态。根据我们的实验合作者提供的强有力的初步数据，我们将记录 NMR、SAXS 和单分子磷酸化和非磷酸化 4E 结合蛋白 2（np-4E-BP2、5p-4E-BP2）的荧光数据及其与真核翻译起始因子（eIF4E）的动态复合物以及混合和凝聚相弹性蛋白片段；以及混合和凝聚相 CAPRIN1 C 端 IDR，包括新型探测静电势 (ESP)、三色 smFRET 和荧光相关性的 NMR 实验这些研究将阐明翻译调节和弹性的机制，以及提供对病理状态的见解，包括自闭症谱系障碍和心血管疾病。