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Rechecking the Centrality-Lethality Rule in the Scope of Protein Subcellular Localization Interaction Networks.

Peng X, Wang J, Wang J, Wu FX, Pan Y - PLoS ONE (2015)

Bottom Line: However, neglecting the temporal and spatial features of protein-protein interactions, the centrality scores calculated by centrality methods are not effective enough for measuring the essentiality of proteins in a PIN.It indicates that proteins with high LCSs measured from PSLINs are more likely to be essential and the performance of centrality methods can be improved by LSED.Furthermore, LSED provides a wide applicable prediction model to identify essential proteins for different species.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science and Engineering, Central South University, Changsha, Hunan, 410083, China.

ABSTRACT
Essential proteins are indispensable for living organisms to maintain life activities and play important roles in the studies of pathology, synthetic biology, and drug design. Therefore, besides experiment methods, many computational methods are proposed to identify essential proteins. Based on the centrality-lethality rule, various centrality methods are employed to predict essential proteins in a Protein-protein Interaction Network (PIN). However, neglecting the temporal and spatial features of protein-protein interactions, the centrality scores calculated by centrality methods are not effective enough for measuring the essentiality of proteins in a PIN. Moreover, many methods, which overfit with the features of essential proteins for one species, may perform poor for other species. In this paper, we demonstrate that the centrality-lethality rule also exists in Protein Subcellular Localization Interaction Networks (PSLINs). To do this, a method based on Localization Specificity for Essential protein Detection (LSED), was proposed, which can be combined with any centrality method for calculating the improved centrality scores by taking into consideration PSLINs in which proteins play their roles. In this study, LSED was combined with eight centrality methods separately to calculate Localization-specific Centrality Scores (LCSs) for proteins based on the PSLINs of four species (Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster). Compared to the proteins with high centrality scores measured from the global PINs, more proteins with high LCSs measured from PSLINs are essential. It indicates that proteins with high LCSs measured from PSLINs are more likely to be essential and the performance of centrality methods can be improved by LSED. Furthermore, LSED provides a wide applicable prediction model to identify essential proteins for different species.

No MeSH data available.


Related in: MedlinePlus

Percentage of different proteins, resulted by LSED-XC methods and the corresponding XC methods, to be essential proteins.Different proteins between two prediction methods are the proteins predicted by one method while neglected by the other method. (a)-(d) illustrate the percentages of true essential proteins in the different proteins from the top 100 protein sets ranked by LSED-XC methods and XC methods for Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster, respectively. In (a)-(d), the X axis represents the number of different proteins between LSED-XC and XC, and the Y axis represents the percentage of true essential proteins in the different proteins. In (a)-(d), all the centrality methods are denoted as XC in the legend, and LSED with different XC methods are denoted as LSED-XC in the legend.
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pone.0130743.g008: Percentage of different proteins, resulted by LSED-XC methods and the corresponding XC methods, to be essential proteins.Different proteins between two prediction methods are the proteins predicted by one method while neglected by the other method. (a)-(d) illustrate the percentages of true essential proteins in the different proteins from the top 100 protein sets ranked by LSED-XC methods and XC methods for Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster, respectively. In (a)-(d), the X axis represents the number of different proteins between LSED-XC and XC, and the Y axis represents the percentage of true essential proteins in the different proteins. In (a)-(d), all the centrality methods are denoted as XC in the legend, and LSED with different XC methods are denoted as LSED-XC in the legend.

Mentions: LSED-XC methods measure the centrality of proteins based on the PSLINs, while XC methods measure the centrality of proteins based on the global PINs. To figure out the difference between essential proteins identified from the global PINs and the PSLINs, we compare the differences in the top 100 proteins ranked by XC methods and LSED-XC methods, respectively. In Fig 8, from (a) to (d), the X axis represents the number of different proteins between LSED-XC and XC, and the Y axis represents the percentage of true essential proteins in the different proteins. For example, DC(38) means that there are 38 different proteins in the two top 100 protein sets ranked by LSED-DC and DC, while there are 62 common proteins in the two top 100 ranked protein sets. In each different protein set, 65.7% ranked by LSED-DC are true essential proteins, while 26.3% ranked by DC are true essential proteins.


Rechecking the Centrality-Lethality Rule in the Scope of Protein Subcellular Localization Interaction Networks.

Peng X, Wang J, Wang J, Wu FX, Pan Y - PLoS ONE (2015)

Percentage of different proteins, resulted by LSED-XC methods and the corresponding XC methods, to be essential proteins.Different proteins between two prediction methods are the proteins predicted by one method while neglected by the other method. (a)-(d) illustrate the percentages of true essential proteins in the different proteins from the top 100 protein sets ranked by LSED-XC methods and XC methods for Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster, respectively. In (a)-(d), the X axis represents the number of different proteins between LSED-XC and XC, and the Y axis represents the percentage of true essential proteins in the different proteins. In (a)-(d), all the centrality methods are denoted as XC in the legend, and LSED with different XC methods are denoted as LSED-XC in the legend.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482623&req=5

pone.0130743.g008: Percentage of different proteins, resulted by LSED-XC methods and the corresponding XC methods, to be essential proteins.Different proteins between two prediction methods are the proteins predicted by one method while neglected by the other method. (a)-(d) illustrate the percentages of true essential proteins in the different proteins from the top 100 protein sets ranked by LSED-XC methods and XC methods for Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster, respectively. In (a)-(d), the X axis represents the number of different proteins between LSED-XC and XC, and the Y axis represents the percentage of true essential proteins in the different proteins. In (a)-(d), all the centrality methods are denoted as XC in the legend, and LSED with different XC methods are denoted as LSED-XC in the legend.
Mentions: LSED-XC methods measure the centrality of proteins based on the PSLINs, while XC methods measure the centrality of proteins based on the global PINs. To figure out the difference between essential proteins identified from the global PINs and the PSLINs, we compare the differences in the top 100 proteins ranked by XC methods and LSED-XC methods, respectively. In Fig 8, from (a) to (d), the X axis represents the number of different proteins between LSED-XC and XC, and the Y axis represents the percentage of true essential proteins in the different proteins. For example, DC(38) means that there are 38 different proteins in the two top 100 protein sets ranked by LSED-DC and DC, while there are 62 common proteins in the two top 100 ranked protein sets. In each different protein set, 65.7% ranked by LSED-DC are true essential proteins, while 26.3% ranked by DC are true essential proteins.

Bottom Line: However, neglecting the temporal and spatial features of protein-protein interactions, the centrality scores calculated by centrality methods are not effective enough for measuring the essentiality of proteins in a PIN.It indicates that proteins with high LCSs measured from PSLINs are more likely to be essential and the performance of centrality methods can be improved by LSED.Furthermore, LSED provides a wide applicable prediction model to identify essential proteins for different species.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science and Engineering, Central South University, Changsha, Hunan, 410083, China.

ABSTRACT
Essential proteins are indispensable for living organisms to maintain life activities and play important roles in the studies of pathology, synthetic biology, and drug design. Therefore, besides experiment methods, many computational methods are proposed to identify essential proteins. Based on the centrality-lethality rule, various centrality methods are employed to predict essential proteins in a Protein-protein Interaction Network (PIN). However, neglecting the temporal and spatial features of protein-protein interactions, the centrality scores calculated by centrality methods are not effective enough for measuring the essentiality of proteins in a PIN. Moreover, many methods, which overfit with the features of essential proteins for one species, may perform poor for other species. In this paper, we demonstrate that the centrality-lethality rule also exists in Protein Subcellular Localization Interaction Networks (PSLINs). To do this, a method based on Localization Specificity for Essential protein Detection (LSED), was proposed, which can be combined with any centrality method for calculating the improved centrality scores by taking into consideration PSLINs in which proteins play their roles. In this study, LSED was combined with eight centrality methods separately to calculate Localization-specific Centrality Scores (LCSs) for proteins based on the PSLINs of four species (Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster). Compared to the proteins with high centrality scores measured from the global PINs, more proteins with high LCSs measured from PSLINs are essential. It indicates that proteins with high LCSs measured from PSLINs are more likely to be essential and the performance of centrality methods can be improved by LSED. Furthermore, LSED provides a wide applicable prediction model to identify essential proteins for different species.

No MeSH data available.


Related in: MedlinePlus