Thomas Edison*
Protein-protein interaction (PPI) network architectures (maximal complete subnets) are a crucial tool for analysing protein complexes and functional modules. The data defection from biological studies is complemented by PPI prediction techniques based on cliques. Clique-based prediction techniques, on the other hand, simply consider the network's topology. In a network, false-positive and false-negative interactions frequently obstruct prediction. To address this issue and increase prediction accuracy, we provide an approach that combines the gene ontology (GO) annotations with clique-based method of prediction. We produce two predicted interaction sets that ensure the quality and amount of anticipated protein interactions based on several GO correction algorithms. The PPI network from the Database of Interacting Proteins (DIP) is subjected to the suggested technique, and the majority of the predicted interactions are confirmed by BioGRID, a different biological database. The original protein network is supplemented with the predicted protein interactions, resulting in clique expansion and demonstrating the importance of biological significance. This research establishes the best technique for building protein-interaction networks that include information derived from protein complexes by merging crystallographic information with protein-interaction data collected through conventional experimental methods. We suggest that a hybrid approach should be taken, in which complexes with five chains or fewer are deconstructed using the matrix model and those with six chains or more are used to derive pairwise interactions using the spoke model. The findings should increase the precision and applicability of studies examining the topology of protein-interaction networks.
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