■综合结构建模结合了实验数据,物理原理,以前结构的统计,和先前的模型来获得大分子组件的结构,这些结构在实验上具有挑战性。模型表示的选择是集成建模中的关键决策,因为它决定了评分的准确性,采样效率,和分析的分辨率。但目前,选择通常是临时的,手动。
■这里,我们报告了Nestor(用于优化R呈现的Nested采样),一个完全自动化的,基于贝叶斯模型选择的统计严格方法,以识别给定集成建模设置的最佳粗粒度表示。给定一个综合的建模设置,它根据模型证据和采样效率从给定的候选表示中确定最佳表示。在四个大分子组件的基准上评估NestOR的性能。
■NestOR在集成建模平台(https://integrativemodeling.org)中实现,可在https://github.com/isblab/nestor获得。基准测试的数据位于https://www。doi.org/10.5281/zenodo.10360718。补充信息可在线获得。
UNASSIGNED: Integrative structural modeling combines data from experiments, physical principles, statistics of previous structures, and prior models to obtain structures of macromolecular assemblies that are challenging to characterize experimentally. The choice of model representation is a key decision in integrative modeling, as it dictates the accuracy of scoring, efficiency of sampling, and resolution of analysis. But currently, the choice is usually made ad hoc, manually.
UNASSIGNED: Here, we report NestOR (Nested Sampling for Optimizing Representation), a fully automated, statistically rigorous method based on Bayesian model selection to identify the optimal coarse-grained representation for a given integrative modeling setup. Given an integrative modeling setup, it determines the optimal representations from given candidate representations based on their model evidence and sampling efficiency. The performance of NestOR was evaluated on a benchmark of four macromolecular assemblies.
UNASSIGNED: NestOR is implemented in the Integrative Modeling Platform (https://integrativemodeling.org) and is available at https://github.com/isblab/nestor.