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，其提供了对生物机制的基本见解。使用强大的交叉验证统计量R-free来监控优化结构模型的进度。然而，最近的进展，在细化技术已经创建了新的模型，模型的构象异质性使用合奏或多个构象。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，因为可以从这些数据集中得出对生物学重要运动的截然不同的解释。贝叶斯模型选择提供了纪律严明的方法来确定适合给定数据集的建模细节级别。我们将开发比较技术，以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X射线衍射数据集，使用标准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，我们将使用信息准则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。该软件将被集成到领先的结构细化和综合建模套件。这些统计方法将提供一个普遍的和显着的改进，从不同的结构数据的蛋白质系综的推断。通过我们的研究计划，我们将为结构生物学社区提供统计上严格的，计算上易于处理的模型比较技术，这些技术集成到现有的流行软件套件中，并为其实用性提供证据。这些进展将使利用构象异质性，以确定新的抑制剂使用在计算机对接，并指导新的蛋白质功能的工程，同时避免徒劳的探索不精确的模型所造成的数据质量差。

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.