BEES: Bayesian Ensemble Estimation from SAS
BEES: Bayesian Ensemble Estimation from SAS
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DOI：
10.1016/j.bpj.2019.06.024
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发表时间：
2019-08-06
影响因子：
3.4
通讯作者：
Wereszczynski, Jeff
中科院分区：
文献类型：
--
作者：
Bowerman, Samuel;Curtis, Joseph E.;Wereszczynski, Jeff
许多生物分子复合物在溶液中以灵活的状态系综存在，这是执行其生物功能所必需的。小角散射（SAS）测量是表征这些柔性分子的流行方法，因为它们相对易于使用并且能够同时探测全部状态。然而，SAS数据通常是低维的，如果没有额外的结构模型的帮助，很难解释。理论上，实验SAS曲线可以从理论模型的线性组合中重构，尽管该过程具有过拟合固有低维SAS数据的显著风险。在此之前，我们开发了一种基于贝叶斯的方法，用于将模型结构的集合拟合到实验SAS数据，该方法严格避免了过拟合。然而，我们发现，这些方法可能很难纳入典型的SAS建模工作流，特别是对于那些不是计算建模专家的用户。为此，我们提出了贝叶斯Enhancement估计从SAS（BEES）程序。BEES有两个分支，主要的一个作为SASSIE Web服务器的模块存在，另一个是独立的Python程序的开发版本。BEES允许用户对从理论状态库构建的集成模型进行详尽的采样，并交互式地分析和比较每个模型的性能。拟合程序还允许提供辅助数据集，从而同时将模型拟合到SAS数据以及正交信息。K63连接的泛素三聚体的灵活的合奏作为BEES的能力的一个例子。
Many biomolecular complexes exist in a flexible ensemble of states in solution that is necessary to perform their biological function. Small-angle scattering (SAS) measurements are a popular method for characterizing these flexible molecules because of their relative ease of use and their ability to simultaneously probe the full ensemble of states. However, SAS data is typically low dimensional and difficult to interpret without the assistance of additional structural models. In theory, experimental SAS curves can be reconstituted from a linear combination of theoretical models, although this procedure carries a significant risk of overfitting the inherently low-dimensional SAS data. Previously, we developed a Bayesian-based method for fitting ensembles of model structures to experimental SAS data that rigorously avoids overfitting. However, we have found that these methods can be difficult to incorporate into typical SAS modeling workflows, especially for users that are not experts in computational modeling. To this end, we present the Bayesian Ensemble Estimation from SAS (BEES) program. Two forks of BEES are available, the primary one existing as a module for the SASSIE web server and a developmental version that is a stand-alone Python program. BEES allows users to exhaustively sample ensemble models constructed from a library of theoretical states and to interactively analyze and compare each model's performance. The fitting routine also allows for secondary data sets to be supplied, thereby simultaneously fitting models to both SAS data as well as orthogonal information. The flexible ensemble of K63-linked ubiquitin trimers is presented as an example of BEES' capabilities.