Identification of domains and domain interface residues in multidomain proteins from graph spectral method

Abstract

We present a novel method for the identification of structural domains and domain interface residues in proteins by graph spectral method. This method converts the three-dimensional structure of the protein into a graph by using atomic coordinates from the PDB file. Domain definitions are obtained by constructing either a protein backbone graph or a protein side-chain graph. The graph is constructed based on the interactions between amino acid residues in the three-dimensional structure of the proteins. The spectral parameters of such a graph contain information regarding the domains and subdomains in the protein structure. This is based on the fact that the interactions among amino acids are higher within a domain than across domains. This is evident in the spectra of the protein backbone and the side-chain graphs, thus differentiating the structural domains from one another. Further, residues that occur at the interface of two domains can also be easily identified from the spectra. This method is simple, elegant, and robust. Moreover, a single numeric computation yields both the domain definitions and the interface residues.

Application of Network Theory in Understanding and Predicting Protein Structure and Function

“…Domain identification by means of network approaches has been reported by Vishveshwara's group (Sistla and Vishveshwara, 2005). The group originally developed the method to identify backbone clusters, in which, edges can be defined in two ways, either based on C atom contacts (Method A), or taking side-chain interactions into consideration (Method C).…”

“…The clusters were identified by examining eigenvectors and eigenvalues, and the latter were obtained from the diagonalization of the network matrix. A domain within a protein was defined as a network community within which intensive interactions exist, and was identified using this method (Sistla and Vishveshwara, 2005). This method yields both domain definitions and interface residues in a single numeric computation.…”

Graph theoretic approach for studying correlated motions in biomolecules

“…A graph is a mathematical structure, which can be defined as a collection of vertices or nodes and a collection of edges that connect pairs of vertices. In particular, cliques and communities detection algorithms have been used in varied biological fields, for instance, in protein interaction predictions, protein domain detection [2], protein structural alignment, protein structural and dynamics studies and even in detection of RNA secondary structure motifs [3,4,5,6,7]. However, there are no available reports on successful application of graph theoretic algorithms for analyzing RNA dynamics and studying the correlated motions of the RNA.…”

Predicting Designability of Small Proteins from Graph Features of Contact Maps

“…The constructed representations were called protein structure graphs. Sistla et al (2005) converted the three-dimensional structure defined by the atomic coordinates of proteins into a graph and presented a method for the identification of structural domains of proteins. Jha et al (2009) showed how topological parameters derived from protein structures can be used for the sequence design for a given set of structures.…”