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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.

Show MeSH
Jackknife curves of ION and seven other centrality methods based on protein data fromE. coli. The prediction performance of ION and seven other existing centrality methods (DC, BC, CC, SC, EC, IC and NC) based on protein data from E. coli are validated by the jackknife method.
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Figure 11: Jackknife curves of ION and seven other centrality methods based on protein data fromE. coli. The prediction performance of ION and seven other existing centrality methods (DC, BC, CC, SC, EC, IC and NC) based on protein data from E. coli are validated by the jackknife method.

Mentions: The ranking scores of E. coli proteins are calculated by using of ION (α=0.5) and the seven other existing centrality methods (DC, BC, CC, SC, EC, IC and NC), respectively. The number of essential proteins in top 1%, 5%, 10%, 15%, 20% and 25% of proteins ranked by these methods are listed in Table6. The PR curves and jackknife curves of each method are illustrated in Figures10 and11. We do not compare ION with PeC because it requires gene express data of E. coli. All of these experimental result shows that the performance of ION in predicting essential proteins is better than that of the seven other existing centrality methods. Specially, as selecting top 10% and 25% ranked proteins, ION achieves 33% and 23% improvement than the average result of the seven methods, respectively.


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 seven other centrality methods based on protein data fromE. coli. The prediction performance of ION and seven other existing centrality methods (DC, BC, CC, SC, EC, IC and NC) based on protein data from E. coli are validated by the jackknife method.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 11: Jackknife curves of ION and seven other centrality methods based on protein data fromE. coli. The prediction performance of ION and seven other existing centrality methods (DC, BC, CC, SC, EC, IC and NC) based on protein data from E. coli are validated by the jackknife method.
Mentions: The ranking scores of E. coli proteins are calculated by using of ION (α=0.5) and the seven other existing centrality methods (DC, BC, CC, SC, EC, IC and NC), respectively. The number of essential proteins in top 1%, 5%, 10%, 15%, 20% and 25% of proteins ranked by these methods are listed in Table6. The PR curves and jackknife curves of each method are illustrated in Figures10 and11. We do not compare ION with PeC because it requires gene express data of E. coli. All of these experimental result shows that the performance of ION in predicting essential proteins is better than that of the seven other existing centrality methods. Specially, as selecting top 10% and 25% ranked proteins, ION achieves 33% and 23% improvement than the average result of the seven methods, respectively.

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