Analysis of amino acid network based on bond energy using graph mining techniques

Amino acids are building blocks of proteins which are essential for all biological functions. Each amino acid has different physico-chemical characteristics. The interconnection between one amino acid with other amino acids in surrounding environment creates a network which is very complex in nature. Graph mining is most efficient and effective methods for analyzing various complex datasets. However, the knowledge discovery from those heterogeneous datasets is a nontrivial task. The computational method automate the prediction of patterns and less expensive than experimental methods in terms of resources and time. This paper presents amino acid network based on bond energy where different graph mining techniques are used to extract information from the network. Further, different centrality measures are calculated to analyze and determine the amino acids relative importance in this network. Also we have examined the clustering coefficient, correlation coefficient among different centrality measures, eccentricity and center of the network. Our results reveal significant insights into the evolutionary roles of amino acids, highlighting the centrality of hydrophilic amino acids and the unique positions of specific residues like Tryptophan and Glycine. These findings provide a deeper understanding of amino acid interactions and their evolutionary implications, demonstrating the effectiveness of computational methods in biological research.