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

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射线晶体学光束线和NMR可以容易且快速地揭示结构。然而 大约三分之一的人类蛋白质组由内在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，因此仍然被传统结构“看不见”， 生物学方法无序原子结构描述的实验和计算方法 蛋白质，虽然往往有价值，仍然缺乏预测能力，特别是对动态复合物的国内流离失所者，以及 缺乏对IDP结构集合与功能之间关系的深入了解。我们做出了重大的 （a）在最后一个赠款周期中，在以下四个方面取得了进展： IDP单体集成的实验数据类型;（2）应用原子和粗粒度的物理 用于生成单体系综的模型和机器学习采样方法;（3）提出新的 用于IDP单体系综选择的贝叶斯模型;（4）高度新颖的机器学习（ML）的发展 （5）创建软件和单体集合数据， 把它们放在实践者手中。这些结果中的许多作为这种更新的初步研究 并在建议的研究中进行了更详细的描述。但是，为了充分解决国内流离失所者的生物活动问题， 我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复合体中的国内流离失所者的动态关联更具选择性 缩合物和翻译后修饰（PTM），产生相关的IDP功能状态。建筑 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4 E结合蛋白2（np-4 E-BP 2，5 p-4 E-BP 2）的荧光数据 及其与真核翻译起始因子（eIF 4 E）的动态复合物;弹性蛋白原和混合的， 凝聚相弹性蛋白片段;以及混合和凝聚相CAPRIN 1 C-末端IDR，包括新的 探测静电势（ESP）、3色smFRET和荧光相关性的NMR实验 光谱（FCS）。这些研究将阐明翻译调节和弹性的机制， 提供对病理状态的深入了解，包括自闭症谱系障碍和心血管疾病。

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.