Frankenstein Ziv, Sperling Joseph, Sperling Ruth, Eisenstein Miriam
Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel.
PLoS One. 2008;3(10):e3594. doi: 10.1371/journal.pone.0003594. Epub 2008 Oct 31.
Studies of the structure and dynamics of macromolecular assemblies often involve comparison of low resolution models obtained using different techniques such as electron microscopy or atomic force microscopy. We present new computational tools for comparing (matching) and docking of low resolution structures, based on shape complementarity. The matched or docked objects are represented by three dimensional grids where the value of each grid point depends on its position with regard to the interior, surface or exterior of the object. The grids are correlated using fast Fourier transformations producing either matches of related objects or docking models depending on the details of the grid representations. The procedures incorporate thickening and smoothing of the surfaces of the objects which effectively compensates for differences in the resolution of the matched/docked objects, circumventing the need for resolution modification. The presented matching tool FitEM2EMin successfully fitted electron microscopy structures obtained at different resolutions, different conformers of the same structure and partial structures, ranking correct matches at the top in every case. The differences between the grid representations of the matched objects can be used to study conformation differences or to characterize the size and shape of substructures. The presented low-to-low docking tool FitEM2EMout ranked the expected models at the top.
对大分子组装体的结构和动力学研究通常涉及对使用不同技术(如电子显微镜或原子力显微镜)获得的低分辨率模型进行比较。我们基于形状互补性,提出了用于比较(匹配)和对接低分辨率结构的新计算工具。匹配或对接的对象由三维网格表示,其中每个网格点的值取决于其相对于对象内部、表面或外部的位置。通过快速傅里叶变换对网格进行关联,根据网格表示的细节产生相关对象的匹配或对接模型。该程序包含对象表面的加厚和平滑处理,有效地补偿了匹配/对接对象分辨率的差异,避免了分辨率修改的需要。所提出的匹配工具FitEM2EMin成功地拟合了在不同分辨率、相同结构的不同构象和部分结构下获得的电子显微镜结构,在每种情况下都将正确的匹配排在首位。匹配对象的网格表示之间的差异可用于研究构象差异或表征子结构的大小和形状。所提出的低分辨率到低分辨率对接工具FitEM2EMout将预期模型排在首位。