Alber Frank, Förster Friedrich, Korkin Dmitry, Topf Maya, Sali Andrej
Department of Biopharmaceutical Sciences, and California Institute for Quantitative Biosciences, University of California at San Francisco, CA 94158-2330, USA.
Annu Rev Biochem. 2008;77:443-77. doi: 10.1146/annurev.biochem.77.060407.135530.
To understand the cell, we need to determine the macromolecular assembly structures, which may consist of tens to hundreds of components. First, we review the varied experimental data that characterize the assemblies at several levels of resolution. We then describe computational methods for generating the structures using these data. To maximize completeness, resolution, accuracy, precision, and efficiency of the structure determination, a computational approach is required that uses 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. This approach is illustrated by determining the configuration of the 456 proteins in the nuclear pore complex (NPC) from baker's yeast. With these tools, we are poised to integrate structural information gathered at multiple levels of the biological hierarchy--from atoms to cells--into a common framework.
为了了解细胞，我们需要确定可能由数十到数百个组件组成的大分子组装结构。首先，我们回顾在几个分辨率水平上表征这些组装体的各种实验数据。然后，我们描述使用这些数据生成结构的计算方法。为了使结构确定的完整性、分辨率、准确性、精确性和效率最大化，需要一种计算方法，该方法使用来自各种实验方法的空间信息。我们提出了这样一种方法，它由三个主要部分定义：组装体的层次表示、由来自实验数据的空间约束组成的评分函数，以及生成与数据一致的结构的优化方法。通过确定面包酵母核孔复合体（NPC）中456种蛋白质的构型来说明这种方法。有了这些工具，我们准备将在生物层次结构的多个层面（从原子到细胞）收集的结构信息整合到一个通用框架中。
