Limits...
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.

Show MeSH
Jackknife curves and PR curves of NC, PeC and different ION results. The prediction performance of ION with respect to different number of reference organisms are validated by the jackknife method and the PR method, respectively. All of those results are also compared with both NC and PeC by using the jackknife method and the PR method.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: Jackknife curves and PR curves of NC, PeC and different ION results. The prediction performance of ION with respect to different number of reference organisms are validated by the jackknife method and the PR method, respectively. All of those results are also compared with both NC and PeC by using the jackknife method and the PR method.

Mentions: To check the influence of referent organisms on prediction performance, according to Taxonomy common tree, we assign orthologous scores to proteins by selecting 10, 20, 40, 60, 80, 90 organisms as reference (seeAdditional file 4), respectively. The prediction results are correspondingly named by ION_10, ION_20, ION_40, ION_60, ION_80 and ION_90. Table5 shows the number of true essential proteins in top 1%, 5%, 10%, 15%, 20% and 25% of these results. Furthermore, these results are also validated by PR curve and jackknife curve, which are illustrated in Figure9. It can be seen from Table5 and Figure9 that no matter how many organisms are selected as references, the prediction accuracy of ION surpasses that of both NC and PeC. In general, the more reference organisms are used, the better prediction performance of ION can be achieved. However, when selecting more than 10 organisms as references, the difference of these results is not obvious.


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 and PR curves of NC, PeC and different ION results. The prediction performance of ION with respect to different number of reference organisms are validated by the jackknife method and the PR method, respectively. All of those results are also compared with both NC and PeC by using the jackknife method and the PR method.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: Jackknife curves and PR curves of NC, PeC and different ION results. The prediction performance of ION with respect to different number of reference organisms are validated by the jackknife method and the PR method, respectively. All of those results are also compared with both NC and PeC by using the jackknife method and the PR method.
Mentions: To check the influence of referent organisms on prediction performance, according to Taxonomy common tree, we assign orthologous scores to proteins by selecting 10, 20, 40, 60, 80, 90 organisms as reference (seeAdditional file 4), respectively. The prediction results are correspondingly named by ION_10, ION_20, ION_40, ION_60, ION_80 and ION_90. Table5 shows the number of true essential proteins in top 1%, 5%, 10%, 15%, 20% and 25% of these results. Furthermore, these results are also validated by PR curve and jackknife curve, which are illustrated in Figure9. It can be seen from Table5 and Figure9 that no matter how many organisms are selected as references, the prediction accuracy of ION surpasses that of both NC and PeC. In general, the more reference organisms are used, the better prediction performance of ION can be achieved. However, when selecting more than 10 organisms as references, the difference of these results is not obvious.

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