Protein Structure and Function: Looking through the Network of Side-Chain Interactions

Author(s): Moitrayee Bhattacharyya, Soma Ghosh, Saraswathi Vishveshwara

Journal Name: Current Protein & Peptide Science

Volume 17 , Issue 1 , 2016

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Graphical Abstract:


Network theory has become an excellent method of choice through which biological data are smoothly integrated to gain insights into complex biological problems. Understanding protein structure, folding, and function has been an important problem, which is being extensively investigated by the network approach. Since the sequence uniquely determines the structure, this review focuses on the networks of non-covalently connected amino acid side chains in proteins. Questions in structural biology are addressed within the framework of such a formalism. While general applications are mentioned in this review, challenging problems which have demanded the attention of scientific community for a long time, such as allostery and protein folding, are considered in greater detail. Our aim has been to explore these important problems through the eyes of networks. Various methods of constructing protein structure networks (PSN) are consolidated. They include the methods based on geometry, edges weighted by different schemes, and also bipartite network of protein-nucleic acid complexes. A number of network metrics that elegantly capture the general features as well as specific features related to phenomena, such as allostery and protein model validation, are described. Additionally, an integration of network theory with ensembles of equilibrium structures of a single protein or that of a large number of structures from the data bank has been presented to perceive complex phenomena from network perspective. Finally, we discuss briefly the capabilities, limitations, and the scope for further explorations of protein structure networks.

Keywords: Allostery, graph theory, molecular dynamics, protein structure network, sidechain interactions, structure quality assessment, structure prediction, tRNA synthetases.

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Article Details

Year: 2016
Published on: 30 November, 2015
Page: [4 - 25]
Pages: 22
DOI: 10.2174/1389203716666150923105727

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