Abstract
Identification of essential proteins is key to understanding the minimal requirements for cellular life and important for drug design. Rapid increasing of available protein-protein interaction data has made it possible to detect protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. However, most of them tended to focus only on topologies of single proteins, but ignored the relevance between interactions and protein essentiality. In this paper, a new method for identifying essential proteins based on edge clustering coefficient, named as SoECC, is proposed. This method binds characteristics of edges and nodes effectively. The experimental results on yeast protein interaction network show that the number of essential proteins discovered by SoECC universally exceeds that discovered by other six centrality measures. Especially, compared to BC and CC, SoECC is 20% higher in prediction accuracy. Moreover, the essential proteins discovered by SoECC show significant cluster effect.
This work is supported in part by the National Natural Science Foundation of China under Grant No.61003124 and No.61073036, the Ph.D. Programs Foundation of Ministry of Education of China No.20090162120073, the Freedom Explore Program of Central South University No.201012200124, the U.S. National Science Foundation under Grants CCF-0514750, CCF-0646102, and CNS-0831634.
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Wang, H., Li, M., Wang, J., Pan, Y. (2011). A New Method for Identifying Essential Proteins Based on Edge Clustering Coefficient. In: Chen, J., Wang, J., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2011. Lecture Notes in Computer Science(), vol 6674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21260-4_12
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DOI: https://doi.org/10.1007/978-3-642-21260-4_12
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