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 X射线结晶学、光束线和核磁共振可以方便、快速地揭示其结构。然而， 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。 没有采用主导的良好折叠结构，因此仍然是传统结构所看不到的 生物学方法。当前无序结构描述的实验和计算方法 蛋白质虽然往往很有价值，但仍然缺乏预测能力，特别是对国内流离失所者的动态复合体以及 缺乏对国内流离失所者结构集合和职能之间关系的洞察。我们取得了重大进展 上一个赠款周期在以下四个方向取得的进展：(1)产生和量化 IDP单体系综的实验数据类型；(2)应用原子化和粗粒度物理 用于产生单体系综的模型和机器学习的抽样方法；(3)提出新的 IDP单体集成选择的贝叶斯模型；(4)高度新颖的机器学习(ML)的发展 生成和选择合奏的方法；(5)创建软件和单体合奏数据和 把它们交到从业者手中。这些结果中的许多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。但为了充分解决境内流离失所者的生物学活动，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， 对离散的动态复合体和生物体内的国内流离失所者的动态关联具有更高的选择性 用于凝析油和翻译后修饰(PTM)，从而产生相关的IDP功能状态。建房 根据我们的实验合作者提供的强大的初步数据，我们将记录核磁共振、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 及其与真核细胞翻译起始因子(EIF4E)的动态复合体；原弹性蛋白和混合的 浓缩相弹性蛋白片段；以及混合相和浓缩相CAPRIN1 C-末端IDR，包括新的 探测静电势(ESPs)、三色smFRET和荧光关联的核磁共振实验 光谱学(FCS)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。

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.