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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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PR curves of ION and eight other existing centrality methods. The proteins ranked in top K (cut-off value) by each method (ION, DC, BC, CC, SC, EC, IC, NC and PeC) are selected as candidate essential proteins (positive data set) and the remaining proteins in PPI network are regarded as candidate nonessential proteins (negative data set). With different values of K selected, the values of precision and recall are computed for each method. The values of precision and recall are plotted in PR curves with different cut-off values. (a) shows the PR curves of ION, PeC, NC and DC. (b) shows the PR curves of ION and two global centrality methods: BC and CC, and other tree centrality methods: IC, EC and SC.
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Figure 4: PR curves of ION and eight other existing centrality methods. The proteins ranked in top K (cut-off value) by each method (ION, DC, BC, CC, SC, EC, IC, NC and PeC) are selected as candidate essential proteins (positive data set) and the remaining proteins in PPI network are regarded as candidate nonessential proteins (negative data set). With different values of K selected, the values of precision and recall are computed for each method. The values of precision and recall are plotted in PR curves with different cut-off values. (a) shows the PR curves of ION, PeC, NC and DC. (b) shows the PR curves of ION and two global centrality methods: BC and CC, and other tree centrality methods: IC, EC and SC.

Mentions: Moreover, we also employ precision-recall (PR) curves and the corresponding areas under the PR curve (AUC) values to evaluate the overall performance of each method. At the beginning, the proteins in PPI networks are ranked in the descending order according to the ranking scores computed by each method. After that, the top K proteins are selected as candidate essential proteins (positive data set), then the remaining proteins in PPI networks are regarded as candidate nonessential proteins (negative data set). The cut-off values of K range from 1 to 5093. With different values of K selected, the values of precision and recall are computed for each method, respectively. Then, the values of precision and recall are plotted in PR curves with different cut-off values. The experimental results are illustrated in Figure4. Figure4 (a) shows the PR curves of ION, PeC, NC and DC. Figure4 (b) shows the PR curves of ION and two global centrality methods: BC and CC, and other tree centrality methods: IC, EC, SC. Note that the PR curves of EC and SC are undistinguishable in Figure4(b). From Figure4, we can see that the PR of ION is clearly above those of all other methods.


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)

PR curves of ION and eight other existing centrality methods. The proteins ranked in top K (cut-off value) by each method (ION, DC, BC, CC, SC, EC, IC, NC and PeC) are selected as candidate essential proteins (positive data set) and the remaining proteins in PPI network are regarded as candidate nonessential proteins (negative data set). With different values of K selected, the values of precision and recall are computed for each method. The values of precision and recall are plotted in PR curves with different cut-off values. (a) shows the PR curves of ION, PeC, NC and DC. (b) shows the PR curves of ION and two global centrality methods: BC and CC, and other tree centrality methods: IC, EC and SC.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC3472210&req=5

Figure 4: PR curves of ION and eight other existing centrality methods. The proteins ranked in top K (cut-off value) by each method (ION, DC, BC, CC, SC, EC, IC, NC and PeC) are selected as candidate essential proteins (positive data set) and the remaining proteins in PPI network are regarded as candidate nonessential proteins (negative data set). With different values of K selected, the values of precision and recall are computed for each method. The values of precision and recall are plotted in PR curves with different cut-off values. (a) shows the PR curves of ION, PeC, NC and DC. (b) shows the PR curves of ION and two global centrality methods: BC and CC, and other tree centrality methods: IC, EC and SC.
Mentions: Moreover, we also employ precision-recall (PR) curves and the corresponding areas under the PR curve (AUC) values to evaluate the overall performance of each method. At the beginning, the proteins in PPI networks are ranked in the descending order according to the ranking scores computed by each method. After that, the top K proteins are selected as candidate essential proteins (positive data set), then the remaining proteins in PPI networks are regarded as candidate nonessential proteins (negative data set). The cut-off values of K range from 1 to 5093. With different values of K selected, the values of precision and recall are computed for each method, respectively. Then, the values of precision and recall are plotted in PR curves with different cut-off values. The experimental results are illustrated in Figure4. Figure4 (a) shows the PR curves of ION, PeC, NC and DC. Figure4 (b) shows the PR curves of ION and two global centrality methods: BC and CC, and other tree centrality methods: IC, EC, SC. Note that the PR curves of EC and SC are undistinguishable in Figure4(b). From Figure4, we can see that the PR of ION is clearly above those of all other methods.

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