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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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PR curves of ION when α is set as 0, 0.5 and 0.99. In ION, the ranking score of a protein which stands for its essentiality in PPI network is viewed as a linear combination of its orthologous score and the neighbor-induced score. The parameter α is used to adjust the weight of two score in the ranking score. As the value of α is equal to 0, the ranking scores only depends on the orthologous properties of proteins. As the value of α is equal to 0.99, the ranking score almost only depends the neighbor’s information. When α is set as other values ranging from 0.1 to 0.9, such as 0.5, the prediction is implemented by integrating the proteins’ orthologous property with their neighbor’s property in the PPI network. The Figure shows precision-recall (PR) curves of ION when α is set as 0, 0.5 and 0.99, respectively.
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Figure 2: PR curves of ION when α is set as 0, 0.5 and 0.99. In ION, the ranking score of a protein which stands for its essentiality in PPI network is viewed as a linear combination of its orthologous score and the neighbor-induced score. The parameter α is used to adjust the weight of two score in the ranking score. As the value of α is equal to 0, the ranking scores only depends on the orthologous properties of proteins. As the value of α is equal to 0.99, the ranking score almost only depends the neighbor’s information. When α is set as other values ranging from 0.1 to 0.9, such as 0.5, the prediction is implemented by integrating the proteins’ orthologous property with their neighbor’s property in the PPI network. The Figure shows precision-recall (PR) curves of ION when α is set as 0, 0.5 and 0.99, respectively.

Mentions: In ION, the ranking scores of proteins are changed with different values of α. To study the effect of parameter α on performance of ION, we evaluate the prediction accuracy by setting different values of α, ranging from 0 to 0.99. The detailed results are listed in Table1. Here, the parameter K is from top 1% to top 25%. The prediction accuracy is measured in terms of the percentage of true essential proteins in candidates. Moreover, to display the overall performance of ION when α is set as 0, 0.5 and 0.99, we plot their PR curves in Figure2.


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)

PR curves of ION when α is set as 0, 0.5 and 0.99. In ION, the ranking score of a protein which stands for its essentiality in PPI network is viewed as a linear combination of its orthologous score and the neighbor-induced score. The parameter α is used to adjust the weight of two score in the ranking score. As the value of α is equal to 0, the ranking scores only depends on the orthologous properties of proteins. As the value of α is equal to 0.99, the ranking score almost only depends the neighbor’s information. When α is set as other values ranging from 0.1 to 0.9, such as 0.5, the prediction is implemented by integrating the proteins’ orthologous property with their neighbor’s property in the PPI network. The Figure shows precision-recall (PR) curves of ION when α is set as 0, 0.5 and 0.99, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: PR curves of ION when α is set as 0, 0.5 and 0.99. In ION, the ranking score of a protein which stands for its essentiality in PPI network is viewed as a linear combination of its orthologous score and the neighbor-induced score. The parameter α is used to adjust the weight of two score in the ranking score. As the value of α is equal to 0, the ranking scores only depends on the orthologous properties of proteins. As the value of α is equal to 0.99, the ranking score almost only depends the neighbor’s information. When α is set as other values ranging from 0.1 to 0.9, such as 0.5, the prediction is implemented by integrating the proteins’ orthologous property with their neighbor’s property in the PPI network. The Figure shows precision-recall (PR) curves of ION when α is set as 0, 0.5 and 0.99, respectively.
Mentions: In ION, the ranking scores of proteins are changed with different values of α. To study the effect of parameter α on performance of ION, we evaluate the prediction accuracy by setting different values of α, ranging from 0 to 0.99. The detailed results are listed in Table1. Here, the parameter K is from top 1% to top 25%. The prediction accuracy is measured in terms of the percentage of true essential proteins in candidates. Moreover, to display the overall performance of ION when α is set as 0, 0.5 and 0.99, we plot their PR curves in Figure2.

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