Model Comparison in Structural Biology

描述（由申请人提供）：x射线晶体学传统上用于生成生物分子的三维结构模型，这为生物机制提供了基本的见解。使用强大的交叉验证统计（R-free）来监测结构模型的改进过程。然而，最近改进技术的进步创造了新的模型类别，这些模型使用集成或多个构象来模拟构象异质性。目前迫切需要创建新的模型选择标准来评估不同类别的模型，因为从这些数据集中可以得出对生物学重要运动的截然不同的解释。贝叶斯模型选择提出了有纪律的方法来确定适合给定数据集的建模细节水平。我们将开发比较技术，严格权衡不同模型类型的拟合质量和简约性。首先，我们将创建合成的x射线衍射数据集，使用标准的数据集成管道进行处理。合成数据集为我们提供了“正确”答案的知识，并允许我们改变输入的构象异质性和噪声。在模型细化之后，我们将使用信息标准来评估模型复杂性和简约性之间的权衡。接下来，我们将评估真实的数据集，重点是高分辨率酶和低分辨率膜蛋白数据集的细化。我们将严格探索全局参数网格搜索对所得模型的影响。最后，我们将实现和分发自动化模型比较的软件。该软件将集成到领先的结构细化和集成建模套件中。这些统计方法将为从不同结构数据推断蛋白质集合提供一个普遍而重要的改进。通过我们的研究计划，我们将为结构生物学社区提供统计严谨，计算易于处理的模型比较技术，并将其集成到现有的流行软件套件中，并证明其实用性。这些进展将使利用构象异质性来识别用于硅对接的新抑制剂，并指导新蛋白质功能的工程设计，同时避免由于数据质量差而导致的不精确模型的徒劳探索。

DESCRIPTION (provided by applicant): X-ray crystallography has traditionally been used to generate three-dimensional structural models of biological molecules, which provide fundamental insights into biological mechanisms. The progress of refining a structural model is monitored using a powerful cross-validation statistic, R-free. However, recent advances in refinement techniques have created new classes of models that model conformational heterogeneity using ensembles or multiple conformations. There is currently a critical need to create new model selection criteria to evaluate different classes of models, as vastly different interpretations of biologically important motions can be drawn from these datasets. Bayesian model selection presents disciplined methods to determine the level of modeling detail appropriate for a given dataset. We will develop comparison techniques to rigorously trade off the quality of fit and parsimony of distinct model types. First, we will create synthetic X-ray diffraction datasets to be processed using standard data integration pipelines. Synthetic datasets afford us knowledge of the "correct" answer and allow us to vary the input conformational heterogeneity and noise. After model refinement, we will use information criteria to evaluate the tradeoffs between model complexity and parsimony. Next, we will evaluate real datasets, focusing on the refinement of high-resolution enzyme and low-resolution membrane protein data sets. We will rigorously explore the effect of global parameter grid searches on the resulting models. Finally, we will implement and distribute software that automates model comparisons. This software will be integrated into leading structure refinement and integrative modeling suites. These statistical methods will provide a general and significant improvement to the inference of protein ensembles from diverse structural data. With our research program, we will provide the structural biology community with statistically rigorous, computationally tractabl model comparison techniques integrated into existing popular software suites, and evidence for their utility. These advances will enable the exploitation of conformational heterogeneity to identify new inhibitors using in silico docking and to guide engineering of new protein functions, while avoiding futile explorations of imprecise models caused by poor data quality.