Protein Homology Analysis for Function Prediction with Parallel Sub-Graph Isomorphism

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Author(s): Alper Küçükural (University of Kansas, USA & Sabanci University, Turkey), Andras Szilagyi (University of Kansas, USA), O. Ugur Sezerman (Sabanci University, Turkey)and Yang Zhang (University of Kansas, USA)
Copyright: 2013
Pages: 14
Source title: Bioinformatics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-3604-0.ch021

Keywords: Bioinformatics / Medical Information Science Reference / Medicine & Healthcare

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Abstract

To annotate the biological function of a protein molecule, it is essential to have information on its 3D structure. Many successful methods for function prediction are based on determining structurally conserved regions because the functional residues are proved to be more conservative than others in protein evolution. Since the 3D conformation of a protein can be represented by a contact map graph, graph matching, algorithms are often employed to identify the conserved residues in weakly homologous protein pairs. However, the general graph matching algorithm is computationally expensive because graph similarity searching is essentially a NP-hard problem. Parallel implementations of the graph matching are often exploited to speed up the process. In this chapter,the authors review theoretical and computational approaches of graph theory and the recently developed graph matching algorithms for protein function prediction.

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Protein Homology Analysis for Function Prediction with Parallel Sub-Graph Isomorphism

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