Clustering of proteins in interaction networks based on motif features

Biological networks such as gene regulatory network, metabolic network and protein interaction network are extensively studied in the literature since last two decades. The various concept of graph theory is widely used to extract biological information from these networks, such as prediction of biological function, detection of protein complexes, the discovery of new interactions, diagnosis of disease, and drug design etc. Network motif analysis is one of the important approaches for functional analysis in the biological network. However, the contribution of biological elements towards these motifs is not clearly defined. Most of the literature discussed the biological significance of motifs as a whole. In this manuscript, the role of proteins for each identified motif is defined in an interaction network. These roles are concatenated to form a motif feature vector. The agglomerative hierarchical clustering algorithm is applied for clustering of proteins based on the above-identified feature vectors. Clustering of proteins leads to many application like protein superfamily classification, protein function annotation etc. The proposed method is evaluated on the protein interaction data of Human herpesvirus-1, Human herpesvirus-8 and Escherichia coli from the MINT database. The performance of the proposed clustering algorithm is evaluated by using the cophenetic correlation coefficient. Cophenetic correlation coefficients of all the output clusters are almost close to 1 which indicates their high quality.