A Novel Method for Predicting Essential Proteins by Integrating Multidimensional Biological Attribute Information and Topological Properties

Background: Essential proteins are indispensable to the maintenance of life activities and play essential roles in the areas of synthetic biology. Identification of essential proteins by computational methods has become a hot topic in recent years because of its efficiency. Objective: Identification of essential proteins is of important significance and practical use in the areas of synthetic biology, drug targets, and human disease genes. Methods: In this paper, a method called EOP (Edge clustering coefficient -Orthologous-Protein) is proposed to infer potential essential proteins by combining Multidimensional Biological Attribute Information of proteins with Topological Properties of the protein-protein interaction network. Results: The simulation results on the yeast protein interaction network show that the number of essential proteins identified by this method is more than the number identified by the other 12 methods (DC, IC, EC, SC, BC, CC, NC, LAC, PEC, CoEWC, POEM, DWE). Especially compared with DC(Degree Centrality), the SN (sensitivity) is 9% higher, when the candidate protein is 1%, the recognition rate is 34% higher, when the candidate protein is 5%, 10%, 15%, 20%, 25% the recognition rate is 36%, 22%, 15%, 11%, 8% higher, respectively. Conclusion: Experimental results show that our method can achieve satisfactory prediction results, which may provide references for future research.

Automated summary

This paper proposes a new method (EOP) for inferring potential essential proteins by combining the multidimensional biological attribute information of proteins with the topological properties of the protein-protein interaction network. The simulation results show that this method identifies more essential proteins compared to other methods, and has higher recognition rates under various conditions.