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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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Percentages of different essential proteins resulted by ION and eight other existing centrality methods. Different proteins between two prediction methods are the proteins predicted by one method while neglected by the other method. The figure shows the percentages of the essential proteins in the different proteins between ION and eight other existing centrality methods (DC, BC, CC, SC, EC, IC, NC and PeC), respectively.
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Figure 6: Percentages of different essential proteins resulted by ION and eight other existing centrality methods. Different proteins between two prediction methods are the proteins predicted by one method while neglected by the other method. The figure shows the percentages of the essential proteins in the different proteins between ION and eight other existing centrality methods (DC, BC, CC, SC, EC, IC, NC and PeC), respectively.

Mentions: Secondly, we compare proteins ranked in top 100 by each method (DC, IC, EC, SC, BC, CC, NC, PeC and ION) to view how many overlap and different proteins are identified by these methods. In Table3, /ION ∩ Mi / denotes the number of proteins detected by both ION and one of the eight other existing centrality methods Mi. {Mi–ION} represents the set of proteins detected by Mi ignored by ION. /Mi–ION/ is the number of proteins in set {Mi–ION} .As described in Table3, there exist huge differences between the proteins identified by ION and Mi. Taking DC, IC, EC, SC, BC and CC for example, there are almost no common proteins identified by both ION and them. For NC and PeC, there are only few proteins identified by both ION and them. These results show that ION is a special method compared with the other methods. For further analysis, we compare the percentages of different essential proteins resulted by ION and by the eight other existing centrality methods. As shown in Figure6, ION can detect more different essential proteins than these methods. Compared with PeC, there are 73 different proteins detected by ION. 53 out 73(about 73%) of these proteins are essential. By contrast, there are only 54% of different proteins detected by PeC while ignored by ION are essential proteins. In fact, for the top 100 of proteins, ION can detect 52 different essential proteins which can’t be detected by anyone of the eight other existing centrality methods (seeAdditional file 2). Additionally, we also find that more than 50% of nonessential proteins in top 100 ranked by DC, IC, EC, SC, BC and CC possess low ranking scores (less than 0.55) computed by ION. As we can see from Table3, about 70% of nonessential proteins in the result of NC have low ION ranking scores. More over, among the top 100 of proteins predicted by PeC, there also exist about 46% of nonessential proteins with low ION ranking scores. This means that ION can exclude many nonessential proteins which can’t be ignored by the other methods. Since ION can not only detect more essential proteins ignored by the eight other existing centrality methods but also exclude a large number of nonessential proteins which can’t be ignored by these methods, it is not surprise that ION has high performance in the prediction of essential proteins.


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)

Percentages of different essential proteins resulted by ION and eight other existing centrality methods. Different proteins between two prediction methods are the proteins predicted by one method while neglected by the other method. The figure shows the percentages of the essential proteins in the different proteins between ION and eight other existing centrality methods (DC, BC, CC, SC, EC, IC, NC and PeC), respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Percentages of different essential proteins resulted by ION and eight other existing centrality methods. Different proteins between two prediction methods are the proteins predicted by one method while neglected by the other method. The figure shows the percentages of the essential proteins in the different proteins between ION and eight other existing centrality methods (DC, BC, CC, SC, EC, IC, NC and PeC), respectively.
Mentions: Secondly, we compare proteins ranked in top 100 by each method (DC, IC, EC, SC, BC, CC, NC, PeC and ION) to view how many overlap and different proteins are identified by these methods. In Table3, /ION ∩ Mi / denotes the number of proteins detected by both ION and one of the eight other existing centrality methods Mi. {Mi–ION} represents the set of proteins detected by Mi ignored by ION. /Mi–ION/ is the number of proteins in set {Mi–ION} .As described in Table3, there exist huge differences between the proteins identified by ION and Mi. Taking DC, IC, EC, SC, BC and CC for example, there are almost no common proteins identified by both ION and them. For NC and PeC, there are only few proteins identified by both ION and them. These results show that ION is a special method compared with the other methods. For further analysis, we compare the percentages of different essential proteins resulted by ION and by the eight other existing centrality methods. As shown in Figure6, ION can detect more different essential proteins than these methods. Compared with PeC, there are 73 different proteins detected by ION. 53 out 73(about 73%) of these proteins are essential. By contrast, there are only 54% of different proteins detected by PeC while ignored by ION are essential proteins. In fact, for the top 100 of proteins, ION can detect 52 different essential proteins which can’t be detected by anyone of the eight other existing centrality methods (seeAdditional file 2). Additionally, we also find that more than 50% of nonessential proteins in top 100 ranked by DC, IC, EC, SC, BC and CC possess low ranking scores (less than 0.55) computed by ION. As we can see from Table3, about 70% of nonessential proteins in the result of NC have low ION ranking scores. More over, among the top 100 of proteins predicted by PeC, there also exist about 46% of nonessential proteins with low ION ranking scores. This means that ION can exclude many nonessential proteins which can’t be ignored by the other methods. Since ION can not only detect more essential proteins ignored by the eight other existing centrality methods but also exclude a large number of nonessential proteins which can’t be ignored by these methods, it is not surprise that ION has high performance in the prediction of essential proteins.

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