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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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Number of yeast orthologs in each reference organism and percentage of essential proteins in each ortholog set. The figure lists 99 reference organisms. These organisms are ordered by their phylum and the decreasing percentage of essential proteins out of their yeast orthologs (red: vertebrate, blue: invertebrate, yellow: plant, green: fungi, purple: protist, prey: prokaryote). The number of yeast proteins which have orthologs in each reference organism is shown in the left part of the figure. The percentage of essential proteins in each ortholog set is shown in the right part of the figure.
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Figure 8: Number of yeast orthologs in each reference organism and percentage of essential proteins in each ortholog set. The figure lists 99 reference organisms. These organisms are ordered by their phylum and the decreasing percentage of essential proteins out of their yeast orthologs (red: vertebrate, blue: invertebrate, yellow: plant, green: fungi, purple: protist, prey: prokaryote). The number of yeast proteins which have orthologs in each reference organism is shown in the left part of the figure. The percentage of essential proteins in each ortholog set is shown in the right part of the figure.

Mentions: We assign orthologous scores to yeast proteins based on the counts they have orthologs in 99 organisms. The orthologous data can be conveniently obtained from the Inparanoid database. How about the performance of ION if we select a small number of reference organisms? Hence, according to known essential protein data in yeast, we first calculate how many proteins have orthologs in each of the 99 reference organisms and then analyze the percentages of essential proteins in each ortholog set. With respect to NCBI Taxonomy common tree, the 99 organisms are divided into six groups. They include 19 vertebrates, 35 invertebrates, 7 plants, 19 fungus, 18 protists and 1 prokaryote (E. coli). Figure8 illustrates the detailed information.


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)

Number of yeast orthologs in each reference organism and percentage of essential proteins in each ortholog set. The figure lists 99 reference organisms. These organisms are ordered by their phylum and the decreasing percentage of essential proteins out of their yeast orthologs (red: vertebrate, blue: invertebrate, yellow: plant, green: fungi, purple: protist, prey: prokaryote). The number of yeast proteins which have orthologs in each reference organism is shown in the left part of the figure. The percentage of essential proteins in each ortholog set is shown in the right part of the figure.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Number of yeast orthologs in each reference organism and percentage of essential proteins in each ortholog set. The figure lists 99 reference organisms. These organisms are ordered by their phylum and the decreasing percentage of essential proteins out of their yeast orthologs (red: vertebrate, blue: invertebrate, yellow: plant, green: fungi, purple: protist, prey: prokaryote). The number of yeast proteins which have orthologs in each reference organism is shown in the left part of the figure. The percentage of essential proteins in each ortholog set is shown in the right part of the figure.
Mentions: We assign orthologous scores to yeast proteins based on the counts they have orthologs in 99 organisms. The orthologous data can be conveniently obtained from the Inparanoid database. How about the performance of ION if we select a small number of reference organisms? Hence, according to known essential protein data in yeast, we first calculate how many proteins have orthologs in each of the 99 reference organisms and then analyze the percentages of essential proteins in each ortholog set. With respect to NCBI Taxonomy common tree, the 99 organisms are divided into six groups. They include 19 vertebrates, 35 invertebrates, 7 plants, 19 fungus, 18 protists and 1 prokaryote (E. coli). Figure8 illustrates the detailed information.

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