Quantifying steric hindrance and topological obstruction to protein structure superposition.

IF 1.5 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Peter Røgen
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引用次数: 3

Abstract

Background: In computational structural biology, structure comparison is fundamental for our understanding of proteins. Structure comparison is, e.g., algorithmically the starting point for computational studies of structural evolution and it guides our efforts to predict protein structures from their amino acid sequences. Most methods for structural alignment of protein structures optimize the distances between aligned and superimposed residue pairs, i.e., the distances traveled by the aligned and superimposed residues during linear interpolation. Considering such a linear interpolation, these methods do not differentiate if there is room for the interpolation, if it causes steric clashes, or more severely, if it changes the topology of the compared protein backbone curves.

Results: To distinguish such cases, we analyze the linear interpolation between two aligned and superimposed backbones. We quantify the amount of steric clashes and find all self-intersections in a linear backbone interpolation. To determine if the self-intersections alter the protein's backbone curve significantly or not, we present a path-finding algorithm that checks if there exists a self-avoiding path in a neighborhood of the linear interpolation. A new path is constructed by altering the linear interpolation using a novel interpretation of Reidemeister moves from knot theory working on three-dimensional curves rather than on knot diagrams. Either the algorithm finds a self-avoiding path or it returns a smallest set of essential self-intersections. Each of these indicates a significant difference between the folds of the aligned protein structures. As expected, we find at least one essential self-intersection separating most unknotted structures from a knotted structure, and we find even larger motions in proteins connected by obstruction free linear interpolations. We also find examples of homologous proteins that are differently threaded, and we find many distinct folds connected by longer but simple deformations. TM-align is one of the most restrictive alignment programs. With standard parameters, it only aligns residues superimposed within 5 Ångström distance. We find 42165 topological obstructions between aligned parts in 142068 TM-alignments. Thus, this restrictive alignment procedure still allows topological dissimilarity of the aligned parts.

Conclusions: Based on the data we conclude that our program ProteinAlignmentObstruction provides significant additional information to alignment scores based solely on distances between aligned and superimposed residue pairs.

定量的位阻和拓扑阻对蛋白质结构叠加的影响。
背景:在计算结构生物学中,结构比较是我们理解蛋白质的基础。例如,结构比较在算法上是结构进化计算研究的起点,它指导我们从氨基酸序列预测蛋白质结构。大多数蛋白质结构比对方法都优化了对齐和重叠残基对之间的距离,即线性插值过程中对齐和重叠残基之间的距离。考虑到这样的线性插值,这些方法不区分是否有插值的空间,如果它引起空间冲突,或者更严重的是,如果它改变了比较的蛋白质主链曲线的拓扑结构。结果:为了区分这种情况,我们分析了两个对齐和叠加的骨干之间的线性插值。我们量化了空间冲突的数量,并在线性主干插值中找到所有自交。为了确定自交是否显著改变了蛋白质的主链曲线,我们提出了一种寻路算法,该算法检查在线性插值的邻域中是否存在自回避路径。通过对三维曲线而不是结图上的结理论的Reidemeister移动的新解释来改变线性插值,构建了一条新的路径。该算法要么找到一条自我避免路径,要么返回一个最小的基本自交集。每一个都表明了排列的蛋白质结构的折叠之间的显著差异。正如预期的那样,我们发现至少有一个基本的自交集将大多数未结结构从结结构中分离出来,并且我们发现通过无阻碍线性插值连接的蛋白质中有更大的运动。我们还发现了同源蛋白质的不同螺纹的例子,我们发现许多不同的折叠由较长但简单的变形连接起来。tm对准是最严格的对准程序之一。在标准参数下,它只对5 Ångström距离内叠加的残基进行对齐。我们发现在142068个tm对齐中,对齐部分之间存在42165个拓扑障碍物。因此,这种限制性对齐过程仍然允许对齐部件的拓扑不相似性。结论:基于数据,我们得出结论,我们的程序ProteinAlignmentObstruction提供了重要的额外信息,仅基于对齐和重叠残基对之间的距离。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Algorithms for Molecular Biology
Algorithms for Molecular Biology 生物-生化研究方法
CiteScore
2.40
自引率
10.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Algorithms for Molecular Biology publishes articles on novel algorithms for biological sequence and structure analysis, phylogeny reconstruction, and combinatorial algorithms and machine learning. Areas of interest include but are not limited to: algorithms for RNA and protein structure analysis, gene prediction and genome analysis, comparative sequence analysis and alignment, phylogeny, gene expression, machine learning, and combinatorial algorithms. Where appropriate, manuscripts should describe applications to real-world data. However, pure algorithm papers are also welcome if future applications to biological data are to be expected, or if they address complexity or approximation issues of novel computational problems in molecular biology. Articles about novel software tools will be considered for publication if they contain some algorithmically interesting aspects.
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