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

Peng W, Wang J, Wang W, Liu Q, Wu FX, Pan Y - BMC Syst Biol (2012)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, People's Republic of China.

ABSTRACT

Background: 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.

Results: 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.

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

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Jackknife curves of ION and eight other existing centrality methods. The x-axis represents the proteins in PPI network ranked by ION and eight other existing centrality methods, ranked from left to right as strongest to weakest prediction of essentiality. The Y-axis is the cumulative count of essential proteins encountered moving left to right through the ranked. The areas under the curve for ION and the eight other existing centrality methods are used to compare their prediction performance. In addition, the 10 random assortments are also plotted for comparison. (a) shows the comparison results of ION, PeC, NC and DC. (b) shows the comparison results of ION and two global centrality methods: BC and CC. (c) shows the comparison results of ION and other three centrality methods: IC, EC and SC.
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Figure 5: Jackknife curves of ION and eight other existing centrality methods. The x-axis represents the proteins in PPI network ranked by ION and eight other existing centrality methods, ranked from left to right as strongest to weakest prediction of essentiality. The Y-axis is the cumulative count of essential proteins encountered moving left to right through the ranked. The areas under the curve for ION and the eight other existing centrality methods are used to compare their prediction performance. In addition, the 10 random assortments are also plotted for comparison. (a) shows the comparison results of ION, PeC, NC and DC. (b) shows the comparison results of ION and two global centrality methods: BC and CC. (c) shows the comparison results of ION and other three centrality methods: IC, EC and SC.

Mentions: For further comparison, the jackknife methodology[46] is used to test the prediction performance of ION and the eight other existing centrality methods. The experimental results are described in Figure5, where the x-axis from left to right represents the proteins in PPI networks ranked in the descending order according to their ranking scores computed by corresponding methods while the Y-axis is the cumulative count of essential proteins with respect to ranked proteins moving left to right. The areas under the curve for ION and the eight other existing centrality methods are used to compare their prediction performance. In addition, the 10 random assortments are also plotted for comparison. Figure5 (a) shows the comparison result of ION, PeC, NC, DC. From this figure, ION has consistently excelled PeC which identifies essential proteins by integrating gene expression data with PPI data. Figure5 (b) shows the comparison result of ION and two global centrality methods: BC and CC. Figure5 (c) shows the comparison result of ION and other three centrality methods: IC, EC, SC. Compared with the seven centrality methods, ION also outperforms them. Moreover, all of the nine methods achieve better prediction performance than the randomized sorting.


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

Peng W, Wang J, Wang W, Liu Q, Wu FX, Pan Y - BMC Syst Biol (2012)

Jackknife curves of ION and eight other existing centrality methods. The x-axis represents the proteins in PPI network ranked by ION and eight other existing centrality methods, ranked from left to right as strongest to weakest prediction of essentiality. The Y-axis is the cumulative count of essential proteins encountered moving left to right through the ranked. The areas under the curve for ION and the eight other existing centrality methods are used to compare their prediction performance. In addition, the 10 random assortments are also plotted for comparison. (a) shows the comparison results of ION, PeC, NC and DC. (b) shows the comparison results of ION and two global centrality methods: BC and CC. (c) shows the comparison results of ION and other three centrality methods: IC, EC and SC.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472210&req=5

Figure 5: Jackknife curves of ION and eight other existing centrality methods. The x-axis represents the proteins in PPI network ranked by ION and eight other existing centrality methods, ranked from left to right as strongest to weakest prediction of essentiality. The Y-axis is the cumulative count of essential proteins encountered moving left to right through the ranked. The areas under the curve for ION and the eight other existing centrality methods are used to compare their prediction performance. In addition, the 10 random assortments are also plotted for comparison. (a) shows the comparison results of ION, PeC, NC and DC. (b) shows the comparison results of ION and two global centrality methods: BC and CC. (c) shows the comparison results of ION and other three centrality methods: IC, EC and SC.
Mentions: For further comparison, the jackknife methodology[46] is used to test the prediction performance of ION and the eight other existing centrality methods. The experimental results are described in Figure5, where the x-axis from left to right represents the proteins in PPI networks ranked in the descending order according to their ranking scores computed by corresponding methods while the Y-axis is the cumulative count of essential proteins with respect to ranked proteins moving left to right. The areas under the curve for ION and the eight other existing centrality methods are used to compare their prediction performance. In addition, the 10 random assortments are also plotted for comparison. Figure5 (a) shows the comparison result of ION, PeC, NC, DC. From this figure, ION has consistently excelled PeC which identifies essential proteins by integrating gene expression data with PPI data. Figure5 (b) shows the comparison result of ION and two global centrality methods: BC and CC. Figure5 (c) shows the comparison result of ION and other three centrality methods: IC, EC, SC. Compared with the seven centrality methods, ION also outperforms them. Moreover, all of the nine methods achieve better prediction performance than the randomized sorting.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, People's Republic of China.

ABSTRACT

Background: 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.

Results: 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.

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

Show MeSH