Identification of side-chain clusters in protein structures by a graph spectral method
Identification of side-chain clusters in protein structures by a graph spectral method
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DOI：
10.1006/jmbi.1999.3058
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发表时间：
1999-09-17
影响因子：
5.6
通讯作者：
Vishveshwara, S
中科院分区：
文献类型：
--
作者：
Kanna, N;Vishveshwara, S
本文提出了一种新的方法来检测蛋白质的三维结构中的侧链簇使用的图谱方法。蛋白质侧链相互作用由标记图表示，其中图的节点表示CP原子，边表示CB原子之间的距离。残基的距离信息和非键连接性以称为拉普拉斯矩阵的矩阵的形式表示。构造的矩阵对角化和聚类信息是从与第二个最低的特征值和聚类中心的向量分量与顶部的特征值相关联的向量分量获得。该方法使用全局信息进行聚类，并且需要单个数值计算来检测感兴趣的聚类。该方法已被采用在这里检测各种侧链簇，并确定的残基，使最大数量的相互作用的残基形成的集群（集群中心）。从蛋白质结构和折叠的角度来看，检测这样的簇和簇中心是重要的。在折叠途径中重要的关键残基是由Phi（F）值（其是突变对折叠过渡态稳定性的影响的量度）确定的，如从蛋白质工程方法获得的，可以从对应于顶部特征值的矢量分量中鉴定。在蛋白质的活性和结合位点附近检测到扩展的簇，支持折叠的成核冷凝假说。该方法也被证明可以检测蛋白质结构中的结构域和拓扑相似蛋白质中的保守侧链簇。(C)北京：科学出版社.
This paper presents a novel method to detect side-chain clusters in protein three-dimensional structures using a graph spectral approach. Protein side-chain interactions are represented by a labeled graph in which the nodes of the graph represent the CP atoms and the edges represent the distance between the CB atoms. The distance information and the non-bonded connectivity of the residues are represented in the form of a matrix called the Laplacian matrix. The constructed matrix is diagonalized and clustering information is obtained from the vector components associated with the second lowest eigenvalue and cluster centers are obtained from the vector components associated with the top eigenvalues. The method uses global information for clustering and a single numeric computation is required to detect clusters of interest. The approach has been adopted here to detect a variety of side-chain clusters and identify the residue which makes the largest number of interactions among the residues forming the cluster (cluster centers). Detecting such clusters and cluster centers are important from a protein structure and folding point of view. The crucial residues which are important in the folding pathway ais determined by Phi(F), values (which is a measure of the effect of a mutation on the stability of the transition state of folding) as obtained from protein engineering methods, can be identified from the vector components corresponding to the top eigenvalues. Expanded clusters are detected near the active and binding site of the protein, supporting the nucleation condensation hypothesis for folding. The method is also shown to detect domains in protein structures and conserved sidechain clusters in topologically similar proteins. (C) 1999 Academic Press.