Calculating Dynamic Ensembles 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。这是因为境内流离失所者 需要以前所未有的水平集成多个基于解决方案的互补性实验， 最先进的分子模拟为IDP合奏提供真实和相关的模型，选择 通过贝叶斯概率方法的最佳集成给出了问题的欠确定性质， 以及综合分析，将观察到的动态结构与与生物相关的功能联系起来 正在回答问题。我们建议开发IDP计算器，它将(1)量化 大量实验数据类型的有用性和信息内容，如化学位移、标量 联轴器、RDC、NOES、PRES和FRET/FCS；(2)使用各种高级原子化和粗粒度 生成IdP及其复合体候选系综的模型和采样方法；(3)应用新的 用于IDP集成选择的贝叶斯模型，用于评估和优化候选集成 最佳实验数据类型；以及(4)创建一个软件套件，将这些方法与 广泛执行结构、序列、结合和其他功能数据的关联分析的工具 身份识别问题。有待开发的计算方法将推进结构的表征 具有内在无序性的蛋白质的系综，不仅针对游离单体，而且侧重于IDP 复合体。我们将在广泛的系统上开发和验证我们的方法： SIC1：调节酵母细胞周期的CdC4，蛋白磷酸酶1与其形成的复合体 控制各种细胞过程的无序调节器，以及单体和相分离的FU和FU TDP-43 IDPs对理解导致生物相的动态分子间接触很重要 分离和ALS相关的聚集。

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. This is because IDPs require an unprecedented level of integration of multiple and complementary solution-based experiments, state-of-the art molecular simulations to provide realistic and relevant models for IDP ensembles, selection of the best ensembles via Bayesian probabilistic approaches given the underdetermined nature of the problem, and comprehensive analysis to connect observed dynamic structure with function relevant to the biological questions being addressed. We propose the development of IDP Calculator, which will (1) quantify the usefulness and information content of a large set of experimental data types such as chemical shifts, scalar couplings, RDCs, NOEs, PREs, and FRET/FCS; (2) use a variety of advanced atomistic and coarse-grained models and sampling methods for generating candidate ensembles of IDP and their complexes; (3) apply new Bayesian models for IDP ensemble selection that both evaluates and optimizes the candidate ensembles with the best experimental data types; and (4) create a software suite that will integrate these methods along with tools to perform correlative analysis of structural, sequence, binding and other functional data on a wide range of IDP problems. The computational approaches to be developed will advance the characterization of structural ensembles for proteins with intrinsic disorder, not only for the free monomer, but with emphasis on IDP complexes. We will develop and validate our approaches on a wide range of systems: the dynamic complex of Sic1:Cdc4 that regulates the yeast cell cycle, complexes formed between protein phosphatase 1 and its disordered regulators that control diverse cellular processes, and monomeric and phase-separated FUS and TDP-43 IDPs important for understanding the dynamic intermolecular contacts leading to biological phase separation and ALS-associated aggregation.