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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
Number of essential proteins identified by ION and eight other existing centrality methods. The proteins in PPI network are ranked in the descending order based on their ranking scores computed by ION, Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Edge Clustering Coefficient Centrality (NC), and centrality based on edge clustering coefficient and Pearson correlation coefficient (PeC). Then, top 1%, 5%, 10%, 15%, 20% and 25% of the ranked proteins are selected as candidates for essential proteins. According to the list of known essential proteins, the number of true essential proteins is used to judge the performance of each method. The figure shows the number of true essential proteins identified by each method in each top percentage of ranked proteins. Since the total number of ranked proteins is 5093. The number of proteins ranked in top 1% is about 51(=5093*1%). The digits in brackets denote the number of proteins ranked in each top percentage.
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Figure 3: Number of essential proteins identified by ION and eight other existing centrality methods. The proteins in PPI network are ranked in the descending order based on their ranking scores computed by ION, Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Edge Clustering Coefficient Centrality (NC), and centrality based on edge clustering coefficient and Pearson correlation coefficient (PeC). Then, top 1%, 5%, 10%, 15%, 20% and 25% of the ranked proteins are selected as candidates for essential proteins. According to the list of known essential proteins, the number of true essential proteins is used to judge the performance of each method. The figure shows the number of true essential proteins identified by each method in each top percentage of ranked proteins. Since the total number of ranked proteins is 5093. The number of proteins ranked in top 1% is about 51(=5093*1%). The digits in brackets denote the number of proteins ranked in each top percentage.

Mentions: To evaluate the performance of ION, we compare the number of essential proteins identified by ION (α=0.5) and eight other existing centrality methods, when selecting various top percentages of ranked proteins as candidates for essential proteins. Figure3 shows the comparison of results.


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 essential proteins identified by ION and eight other existing centrality methods. The proteins in PPI network are ranked in the descending order based on their ranking scores computed by ION, Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Edge Clustering Coefficient Centrality (NC), and centrality based on edge clustering coefficient and Pearson correlation coefficient (PeC). Then, top 1%, 5%, 10%, 15%, 20% and 25% of the ranked proteins are selected as candidates for essential proteins. According to the list of known essential proteins, the number of true essential proteins is used to judge the performance of each method. The figure shows the number of true essential proteins identified by each method in each top percentage of ranked proteins. Since the total number of ranked proteins is 5093. The number of proteins ranked in top 1% is about 51(=5093*1%). The digits in brackets denote the number of proteins ranked in each top percentage.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Number of essential proteins identified by ION and eight other existing centrality methods. The proteins in PPI network are ranked in the descending order based on their ranking scores computed by ION, Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Edge Clustering Coefficient Centrality (NC), and centrality based on edge clustering coefficient and Pearson correlation coefficient (PeC). Then, top 1%, 5%, 10%, 15%, 20% and 25% of the ranked proteins are selected as candidates for essential proteins. According to the list of known essential proteins, the number of true essential proteins is used to judge the performance of each method. The figure shows the number of true essential proteins identified by each method in each top percentage of ranked proteins. Since the total number of ranked proteins is 5093. The number of proteins ranked in top 1% is about 51(=5093*1%). The digits in brackets denote the number of proteins ranked in each top percentage.
Mentions: To evaluate the performance of ION, we compare the number of essential proteins identified by ION (α=0.5) and eight other existing centrality methods, when selecting various top percentages of ranked proteins as candidates for essential proteins. Figure3 shows the comparison of results.

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