Refining Protein Interaction Network for Identifying Essential Proteins

Aim: The study aimed to reconstruct the protein-protein interaction network for the identification of essential proteins. Background: In a living organism, essential proteins play an indispensable role in its survival and development. Hence, how to identify essential proteins from the protein interaction network (PIN) has become a hot topic in the field of bioinformatics. However, existing methods' accuracies for identifying essential proteins are still limited due to the false positives of the protein-protein interaction data. Objective: The objective of the study was to propose an efficient algorithm for the reconstruction of a protein-protein interaction network. Methods: In this paper, a method for the refinement of PIN based on three kinds of biological data (subcellular localization data, protein complex data, and gene expression data) is proposed. Through evaluating each interaction within the original PIN, a refined clean PIN could be obtained. To verify the effectiveness of the refined PIN for the identification of essential proteins, we applied eight network-based essential protein discovery methods (DC, BC, CC, LC, HC, SC, LAC, and NC) to it. Results: Based on the obtained experimental results, we demonstrated that the precision for identifying essential proteins could be greatly improved by refining the original PIN using our method. Conclusion: Our method could effectively enhance the protein-protein interaction network and improve the accuracy of identifying essential proteins. In the future, we plan to integrate more biological information to enhance our refinement method and apply it to more species and more PIN-based discovery tasks, like the identification of protein complexes or functional modules.

Automated summary

The study aimed to reconstruct the protein-protein interaction network and proposed a method to refine the original network based on subcellular localization data, protein complex data, and gene expression data. The results showed that the method greatly improved the accuracy of identifying essential proteins.