Abstract
BackgroundIdentification 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.ResultsBy 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.ConclusionsThe accuracy of predicting essential proteins can be improved by integrating the orthology with PPI networks.
Highlights
Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development
In order to evaluate the essentiality of proteins in protein-protein interaction (PPI) networks, they are ranked in the descending order based on their ranking scores computed by ION in Section 2,5 as well as eight other existing centrality methods (DC, Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), NC and pearson correlation coefficient (PeC))
In this work we propose ION, an iteration method for predicting essential proteins based on orthology and PPI networks
Summary
Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Identification of essential proteins provides insight in understanding minimal requirements for cellular survival and development It plays a significant role in the emerging science of accurate computational method becomes a very important choice for identifying essential proteins. Besides the phyletic retention trait of proteins, other types of genomic features, such as GC content, protein length, ORF length [10,11,12], cellular localization [13], and so on, are mentioned for predicting essential proteins by taking the advantage of supervised machine learning-based methods. The performance of these methods closely depends on classifier and the distance between training organisms and test organisms
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