Background:
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.
Objective:
Many literature works have proven that the small world network concept provides
many new opportunities to investigate network of biological systems. The objective of this paper is
analyzing the protein structure using small world concept.
Methods:
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.
Results:
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 (i.e., degree
distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are
calculated for 1323 nodes and graphs are plotted.
Conclusion:
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.
Navigation safety domain and collision risk index for decision support of collision avoidance of USVs
