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FINDMOL:电子密度图中大分子的自动识别

FINDMOL: automated identification of macromolecules in electron-density maps.

作者信息

McKee E W, Kanbi L D, Childs K L, Grosse-Kunstleve R W, Adams P D, Sacchettini J C, Ioerger T R

机构信息

Department of Computer Science, Texas A&M University, 301 H. R. Bright Building, 3112 Texas A&M University, College Station, TX 77843, USA.

出版信息

Acta Crystallogr D Biol Crystallogr. 2005 Nov;61(Pt 11):1514-20. doi: 10.1107/S0907444905027332. Epub 2005 Oct 19.

DOI:10.1107/S0907444905027332
PMID:16239729
Abstract

Automating the determination of novel macromolecular structures via X-ray crystallographic methods involves building a model into an electron-density map. Unfortunately, the conventional crystallographic asymmetric unit volumes are usually not well matched to the biological molecular units. In most cases, the facets of the asymmetric unit cut the molecules into a number of disconnected fragments, rendering interpretation by the crystallographer significantly more difficult. The FINDMOL algorithm is designed to quickly parse the arrangement of trace points (pseudo-atoms) derived from a skeletonized electron-density map without requiring higher level prior information such as sequence information or number of molecules in the asymmetric unit. The algorithm was tested with a variety of density-modified maps computed with medium- to low-resolution data. Typically, the resulting volume resembles the biological unit. In the remaining cases the number of disconnected fragments is very small. In all examples, secondary-structural elements such as alpha-helices or beta-sheets are easily identifiable in the defragmented arrangement. FINDMOL can greatly assist a crystallographer during manual model building or in cases where automatic model building can only build partial models owing to limitations of the data such as low resolution and/or poor phases.

摘要

通过X射线晶体学方法自动确定新型大分子结构涉及将模型构建到电子密度图中。不幸的是,传统的晶体学不对称单元体积通常与生物分子单元不太匹配。在大多数情况下,不对称单元的面将分子切割成许多不相连的片段,这使得晶体学家的解释变得更加困难。FINDMOL算法旨在快速解析从骨架化电子密度图中得出的迹点(伪原子)的排列,而无需诸如序列信息或不对称单元中分子数量等更高层次的先验信息。该算法用各种由中低分辨率数据计算得到的密度修正图进行了测试。通常,得到的体积类似于生物单元。在其余情况下,不相连片段的数量非常少。在所有示例中,在去碎片化排列中很容易识别出二级结构元件,如α螺旋或β折叠。FINDMOL可以在人工模型构建过程中,或者在由于数据限制(如低分辨率和/或差相位)导致自动模型构建只能构建部分模型的情况下,极大地帮助晶体学家。

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引用本文的文献

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Int J Data Min Bioinform. 2009;3(2):205-27. doi: 10.1504/ijdmb.2009.024852.