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通过聚类对距离变化在生物分子中寻找半刚性结构域。

Finding semirigid domains in biomolecules by clustering pair-distance variations.

作者信息

Kenn Michael, Ribarics Reiner, Ilieva Nevena, Schreiner Wolfgang

机构信息

Section of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics, and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.

Institute for Nuclear Research and Nuclear Energy (INRNE), Bulgarian Academy of Sciences, 72, Tzarigradsko Chaussee, 1784 Sofia, Bulgaria.

出版信息

Biomed Res Int. 2014;2014:731325. doi: 10.1155/2014/731325. Epub 2014 May 15.

DOI:10.1155/2014/731325
PMID:24959586
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4052062/
Abstract

Dynamic variations in the distances between pairs of atoms are used for clustering subdomains of biomolecules. We draw on a well-known target function for clustering and first show mathematically that the assignment of atoms to clusters has to be crisp, not fuzzy, as hitherto assumed. This reduces the computational load of clustering drastically, and we demonstrate results for several biomolecules relevant in immunoinformatics. Results are evaluated regarding the number of clusters, cluster size, cluster stability, and the evolution of clusters over time. Crisp clustering lends itself as an efficient tool to locate semirigid domains in the simulation of biomolecules. Such domains seem crucial for an optimum performance of subsequent statistical analyses, aiming at detecting minute motional patterns related to antigen recognition and signal transduction.

摘要

原子对之间距离的动态变化被用于对生物分子的子域进行聚类。我们借鉴了一种著名的聚类目标函数，并首先从数学上表明，与迄今为止所假设的情况不同，原子到聚类的分配必须是清晰的，而非模糊的。这极大地降低了聚类的计算量，并且我们展示了几种免疫信息学中相关生物分子的结果。从聚类数量、聚类大小、聚类稳定性以及聚类随时间的演变等方面对结果进行了评估。清晰聚类适合作为在生物分子模拟中定位半刚性域的有效工具。此类域对于后续统计分析的最佳性能似乎至关重要，这些分析旨在检测与抗原识别和信号转导相关的微小运动模式。

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