Ohio State University Biophysics Program, Department of Chemistry and Biochemistry, and Center for RNA Biology, The Ohio State University, Columbus, OH, USA Ohio State University Biophysics Program, Department of Chemistry and Biochemistry, and Center for RNA Biology, The Ohio State University, Columbus, OH, USA.
Ohio State University Biophysics Program, Department of Chemistry and Biochemistry, and Center for RNA Biology, The Ohio State University, Columbus, OH, USA Ohio State University Biophysics Program, Department of Chemistry and Biochemistry, and Center for RNA Biology, The Ohio State University, Columbus, OH, USA Ohio State University Biophysics Program, Department of Chemistry and Biochemistry, and Center for RNA Biology, The Ohio State University, Columbus, OH, USA.
Bioinformatics. 2015 Jun 15;31(12):1951-8. doi: 10.1093/bioinformatics/btv079. Epub 2015 Feb 10.
Macromolecular structures and interactions are intrinsically heterogeneous, temporally adopting a range of configurations that can confound the analysis of data from bulk experiments. To obtain quantitative insights into heterogeneous systems, an ensemble-based approach can be employed, in which predicted data computed from a collection of models is compared to the observed experimental results. By simultaneously fitting orthogonal structural data (e.g. small-angle X-ray scattering, nuclear magnetic resonance residual dipolar couplings, dipolar electron-electron resonance spectra), the range and population of accessible macromolecule structures can be probed.
We have developed MESMER, software that enables the user to identify ensembles that can recapitulate experimental data by refining thousands of component collections selected from an input pool of potential structures. The MESMER suite includes a powerful graphical user interface (GUI) to streamline usage of the command-line tools, calculate data from structure libraries and perform analyses of conformational and structural heterogeneity. To allow for incorporation of other data types, modular Python plugins enable users to compute and fit data from nearly any type of quantitative experimental data.
Conformational heterogeneity in three macromolecular systems was analyzed with MESMER, demonstrating the utility of the streamlined, user-friendly software.
大分子结构和相互作用本质上是不均匀的，会在一段时间内采用一系列构象，从而使从批量实验中获得的数据的分析变得复杂。为了深入了解不均匀系统，我们可以采用基于集合的方法，从一组模型中计算出预测数据，并将其与观察到的实验结果进行比较。通过同时拟合正交结构数据（例如小角 X 射线散射、核磁共振残磁偶合、偶极电子-电子共振谱），可以探测到可及大分子结构的范围和种群。
我们开发了 MESMER 软件，它使用户能够通过从潜在结构输入池中选择的数千个组件集合来识别可以再现实验数据的集合。MESMER 套件包括一个强大的图形用户界面（GUI），可简化命令行工具的使用，从结构库中计算数据并分析构象和结构异质性。为了允许纳入其他数据类型，模块化 Python 插件使用户能够计算和拟合几乎任何类型的定量实验数据。
使用 MESMER 分析了三个大分子系统中的构象异质性，展示了这款简化的、用户友好的软件的实用性。
