STRUCTURE DETERMIN OF PROTEIN ASSEMBLIES BY SATISFACTION OF SPATIAL RESTRAINTS

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 为了了解细胞，我们需要确定 大分子组件，其中许多由数十至数百个组件组成。 各种各样的实验数据可用于表征 从原子结构到组件，具有多个分辨率级别的组件 配置.最大限度地提高完整性、分辨率、准确性、精确性和效率 的结构确定，需要一种计算方法，可以 使用各种实验方法的空间信息。我们建议， 一种方法，由其三个主要组成部分定义： 组件，评分功能，包括来自实验的空间限制 数据，以及一种优化方法，该方法生成与 数据。我们正在使用这种方法，以获得一个更准确的模型的七成员NUP 84复合物的芽殖酵母核孔复合物。正在编写一份介绍这项工作的文件。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. To understand the cell, we need to determine the structures of macromolecular assemblies, many of which consist of tens to hundreds of components. A great variety of experimental data can be used to characterize the assemblies at several levels of resolution, from atomic structures to component configurations. To maximize completeness, resolution, accuracy, precision and efficiency of the structure determination, a computational approach is needed that can use spatial information from a variety of experimental methods. We propose such an approach, defined by its three main components: a hierarchical representation of the assembly, a scoring function consisting of spatial restraints derived from experimental data, and an optimization method that generates structures consistent with the data. We are using this approach to obtain a more accurate model of the seven member Nup84 complex from the budding yeast nuclear pore complex. A paper describing this work is in preparation.