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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
Cumulative percentage of essential proteins in orthologs sets. Figure1 shows the percentages of essential proteins out of the proteins that have orthologs in 99 reference organisms not less than number ep of times. It illustrates the relationship between the essentiality properties of proteins and the number of orthologs that they have in reference organisms.
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Figure 1: Cumulative percentage of essential proteins in orthologs sets. Figure1 shows the percentages of essential proteins out of the proteins that have orthologs in 99 reference organisms not less than number ep of times. It illustrates the relationship between the essentiality properties of proteins and the number of orthologs that they have in reference organisms.

Mentions: In order to get the relationship between essentiality and orthologous properties of proteins, we check the yeast proteins if they have orthologs in 99 reference organisms ranging from H.sapiens to E. coli. As a result, 4511 proteins (present in the yeast PPI network) are labeled to have orthologs in at least one of 99 reference organisms. Furthermore, 1118 out of 1167 known essential proteins are included in these 4511 proteins. It means that 96% (1118/1167) of essential proteins in the PPI network are evolutionarily conserved. For further analysis, Pep is used to describe the percentage of essential proteins out of all proteins that occur in orthologous seed pairs not less than number ep of times, here ep ranges from 1 to 99. Figure1 outlines the data.


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)

Cumulative percentage of essential proteins in orthologs sets. Figure1 shows the percentages of essential proteins out of the proteins that have orthologs in 99 reference organisms not less than number ep of times. It illustrates the relationship between the essentiality properties of proteins and the number of orthologs that they have in reference organisms.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Cumulative percentage of essential proteins in orthologs sets. Figure1 shows the percentages of essential proteins out of the proteins that have orthologs in 99 reference organisms not less than number ep of times. It illustrates the relationship between the essentiality properties of proteins and the number of orthologs that they have in reference organisms.
Mentions: In order to get the relationship between essentiality and orthologous properties of proteins, we check the yeast proteins if they have orthologs in 99 reference organisms ranging from H.sapiens to E. coli. As a result, 4511 proteins (present in the yeast PPI network) are labeled to have orthologs in at least one of 99 reference organisms. Furthermore, 1118 out of 1167 known essential proteins are included in these 4511 proteins. It means that 96% (1118/1167) of essential proteins in the PPI network are evolutionarily conserved. For further analysis, Pep is used to describe the percentage of essential proteins out of all proteins that occur in orthologous seed pairs not less than number ep of times, here ep ranges from 1 to 99. Figure1 outlines the data.

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