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Open Access Methodology article

Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks

Wei Peng12, Jianxin Wang1*, Weiping Wang1, Qing Liu1, Fang-Xiang Wu3 and Yi Pan4

Author Affiliations

1 School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, People’s Republic of China

2 Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, Yunnan, 650093, People’s Republic of China

3 Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada

4 Department of Computer Science, Georgia State University, Atlanta, GA, 30302-4110, USA

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BMC Systems Biology 2012, 6:87  doi:10.1186/1752-0509-6-87

Published: 18 July 2012



Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Many computational methods have been proposed for predicting essential proteins by using the topological features of protein-protein interaction (PPI) networks. However, most of these methods ignored intrinsic biological meaning of proteins. Moreover, PPI data contains many false positives and false negatives. To overcome these limitations, recently many research groups have started to focus on identification of essential proteins by integrating PPI networks with other biological information. However, none of their methods has widely been acknowledged.


By considering the facts that essential proteins are more evolutionarily conserved than nonessential proteins and essential proteins frequently bind each other, we propose an iteration method for predicting essential proteins by integrating the orthology with PPI networks, named by ION. Differently from other methods, ION identifies essential proteins depending on not only the connections between proteins but also their orthologous properties and features of their neighbors. ION is implemented to predict essential proteins in S. cerevisiae. Experimental results show that ION can achieve higher identification accuracy than eight other existing centrality methods in terms of area under the curve (AUC). Moreover, ION identifies a large amount of essential proteins which have been ignored by eight other existing centrality methods because of their low-connectivity. Many proteins ranked in top 100 by ION are both essential and belong to the complexes with certain biological functions. Furthermore, no matter how many reference organisms were selected, ION outperforms all eight other existing centrality methods. While using as many as possible reference organisms can improve the performance of ION. Additionally, ION also shows good prediction performance in E. coli K-12.


The accuracy of predicting essential proteins can be improved by integrating the orthology with PPI networks.