Model Comparison in Structural Biology

描述(申请人提供)：X射线结晶学传统上被用来生成生物分子的三维结构模型，这提供了对生物机制的基本见解。使用一个强大的交叉验证统计量R-Free来监控改进结构模型的进度。然而，最近精化技术的进步创造了新的模型类，这些模型使用系综或多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上重要运动的巨大不同的解释。贝叶斯模型选择提供了有纪律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，严格权衡不同型号类型的适合性和简洁性。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构象的异质性和噪声。在模型精化之后，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精化。我们将严格探索全局参数网格搜索对结果模型的影响。最后，我们将实施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优化和一体化建模套件中。这些统计方法将为从不同的结构数据中推断蛋白质系综提供普遍和显着的改进。通过我们的研究计划，我们将为结构生物学社区提供集成到现有流行软件套件中的统计上严格的、计算上容易处理的模型比较技术，并为其实用性提供证据。这些进展将使人们能够利用构象异质性来确定用于电子对接的新的抑制剂，并指导新的蛋白质功能的工程设计，同时避免因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.