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使用片段字符串比对来比较蛋白质结构。

Using an alignment of fragment strings for comparing protein structures.

作者信息

Friedberg Iddo, Harder Tim, Kolodny Rachel, Sitbon Einat, Li Zhanwen, Godzik Adam

机构信息

Program in Bioinformatics and Systems Biology, Burnham Institute for Medical Research, La Jolla, CA, USA.

出版信息

Bioinformatics. 2007 Jan 15;23(2):e219-24. doi: 10.1093/bioinformatics/btl310.

Abstract

MOTIVATION

Most methods that are used to compare protein structures use three-dimensional (3D) structural information. At the same time, it has been shown that a 1D string representation of local protein structure retains a degree of structural information. This type of representation can be a powerful tool for protein structure comparison and classification, given the arsenal of sequence comparison tools developed by computational biology. However, in order to do so, there is a need to first understand how much information is contained in various possible 1D representations of protein structure.

RESULTS

Here we describe the use of a particular structure fragment library, denoted here as KL-strings, for the 1D representation of protein structure. Using KL-strings, we develop an infrastructure for comparing protein structures with a 1D representation. This study focuses on the added value gained from such a description. We show the new local structure language adds resolution to the traditional three-state (helix, strand and coil) secondary structure description, and provides a high degree of accuracy in recognizing structural similarities when used with a pairwise alignment benchmark. The results of this study have immediate applications towards fast structure recognition, and for fold prediction and classification.

摘要

动机

大多数用于比较蛋白质结构的方法都使用三维(3D)结构信息。同时,研究表明,局部蛋白质结构的一维字符串表示保留了一定程度的结构信息。鉴于计算生物学开发的一系列序列比较工具,这种表示形式可以成为蛋白质结构比较和分类的有力工具。然而,要做到这一点,首先需要了解蛋白质结构的各种可能的一维表示中包含多少信息。

结果

在这里,我们描述了使用一种特定的结构片段库(在此表示为KL字符串)来对蛋白质结构进行一维表示。使用KL字符串,我们开发了一种用于比较具有一维表示的蛋白质结构的基础架构。本研究重点关注这种描述所带来的附加值。我们表明,这种新的局部结构语言为传统的三态(螺旋、链和卷曲)二级结构描述增加了分辨率,并且在与成对排列基准一起使用时,在识别结构相似性方面具有高度准确性。这项研究的结果可立即应用于快速结构识别以及折叠预测和分类。

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