Identification of side-chain clusters in protein structures by a graph spectral method

Kannan, N and Vishveshwara, S (1999) Identification of side-chain clusters in protein structures by a graph spectral method. In: Journal of Molecular Biology, 292 (2). pp. 441-464.

PDF Identification_of_side-chain_clusters.pdf Restricted to Registered users only Download (1MB) | Request a copy

Abstract

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 $C^\beta$atoms and the edges represent the distance between the $C^\beta$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 as determined by $\phi$ Fvalues (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 side-chain clusters in topologically similar proteins.

Item Type:	Journal Article
Publication:	Journal of Molecular Biology
Publisher:	Academic Press
Additional Information:	copyright of this article belongs to Academic Press
Keywords:	clusters;hydrophobic;graph theory;Laplacian matrix eigenvalue protein folding
Department/Centre:	Division of Biological Sciences > Molecular Biophysics Unit
Date Deposited:	10 Oct 2007
Last Modified:	19 Sep 2010 04:40
URI:	http://eprints.iisc.ac.in/id/eprint/12205

Actions (login required)

View Item