TCPP-2PPIN: trustworthy centrality prediction paradigm for analyzing two protein-protein interaction networks using centrality measures and graph theory concepts

The process of understanding and analyzing the structure and the evolution of protein-protein interaction networks is not only getting a deep insight into cellular life but also significantly helping us in drug discovery. Given the intricate nature of biological systems, creating a trustworthy experimental framework is a formidable challenge in experimental biology. Systems biology aims to construct suitable, trustworthy models through computational methodologies that leverage observational biological data stored in bioinformatics databases. These models make predictions, which, in turn, prove valuable in shaping subsequent experimental designs. The proposed model is explained by analysing the protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using 31 centrality measures, and the common substructure has been predicted with graph theory concepts. The unsupervised learning method 'Principal Component Analysis' has been used to predict the most influenced centrality measures on the protein-protein interaction networks, which will turn to help in predicting the root node of the tensor product graph to finalize a large-sized common substructure of the protein networks. The trustworthiness of the proposed paradigm is ensured with a sequence of comparisons to predict the similarity and dissimilarity responses of various centrality measures on the protein networks with r Pearson Correlation Coefficient and Spearman rank correlation. Utilizing centrality measures, an unsupervised learning approach, and concepts rooted in graph theory, the ultimate result of the proposed model comprises a set of influential and crucial proteins. Furthermore, it identifies the shared substructure among these highly essential proteins within the protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza. This study reveals that centrality measures such as eigenvector, eigenvector Numpy, degree, Laplacian, betweenness, current flow betweenness, Communicability Betweenness, load, Pagerank, and Katz centrality play significant roles in predicting the essential proteins within PPI networks. The proposed paradigm enables the prediction of essential proteins and the identification of common patterns between two PPI networks. This approach effectively reduces the search space and accelerates the drug discovery process. However, users of this model must possess a deep understanding of PPI networks to develop novel drugs successfully.