Analysis of Oncogene Protein Structure Using Small World Network Concept

(E-pub Ahead of Print)

Author(s): Neetu Kumari, Anshul Verma*

Journal Name: Current Bioinformatics

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The basic building block of a body is protein which is a complex system whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore, careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug designing. Protein structures are described at their different levels of complexity: primary (chain), secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of protein is a difficult task but it can be analyzed as a network of interconnection between its component, where amino acids are considered as nodes and interconnection between them are edges. However, the many literature works have proven that the small world network concept provides many new opportunities to investigate network of biological system. In this paper, protein is analyzed using small world network concept, specifically where extreme condition is having a degree distribution which follows power law. For the correct verification of the proposed approach, dataset of the Oncogene protein structure is analyzed using Python programming. Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG)) using distance matrix of nodes with given threshold, then various centrality measures are calculated for 1323 nodes and graphs are plotted. Ultimately, it is concluded that there exist hubs with higher centrality degree but less in number, and they are expected to be robust toward harmful effects of mutations with new functions.

Keywords: Small world concepts, Protein structure analysis, Oncogene protein, Degree distribution, Centrality measures

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1574893614666191113143840
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