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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: 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.In this paper, we demonstrate that the centrality-lethality rule also exists in Protein Subcellular Localization Interaction Networks (PSLINs).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

The AKAccs of each method in different top percentages of ranked proteins over four species.Four species are Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster. When 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 of each species, the Acc of each method in each top percentage of ranked proteins was calculated. (a)-(f) illustrate the AKAccs of LSED-XC methods and XC methods in the top 1%, 5%, 10%, 15%, 20%, and 25% of ranked proteins over four species, respectively. In (a)-(f), 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.g007: The AKAccs of each method in different top percentages of ranked proteins over four species.Four species are Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster. When 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 of each species, the Acc of each method in each top percentage of ranked proteins was calculated. (a)-(f) illustrate the AKAccs of LSED-XC methods and XC methods in the top 1%, 5%, 10%, 15%, 20%, and 25% of ranked proteins over four species, respectively. In (a)-(f), 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: The AKAcc of each method in each top percentage of ranked proteins over four species was calculated. As shown in Fig 7, the AKAccs of LSED-XC methods in each top percentage of ranked proteins are higher than those of XC methods consistently. Especially, the AKAccs of LSED-BC and LSED-DC are always higher, compared with XC methods and other LSED-XC methods, which indicates their superior performances to identify essential proteins for different species. Furthermore, the higher AKAccs of LSED-XC methods suggest that in most situations the LCSs taking into consideration the cellular compartments seem to be more predictive than the centrality scores measured in the global PINs and the essential proteins of different species can be explored better in the PSLINs.


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)

The AKAccs of each method in different top percentages of ranked proteins over four species.Four species are Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster. When 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 of each species, the Acc of each method in each top percentage of ranked proteins was calculated. (a)-(f) illustrate the AKAccs of LSED-XC methods and XC methods in the top 1%, 5%, 10%, 15%, 20%, and 25% of ranked proteins over four species, respectively. In (a)-(f), 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.g007: The AKAccs of each method in different top percentages of ranked proteins over four species.Four species are Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster. When 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 of each species, the Acc of each method in each top percentage of ranked proteins was calculated. (a)-(f) illustrate the AKAccs of LSED-XC methods and XC methods in the top 1%, 5%, 10%, 15%, 20%, and 25% of ranked proteins over four species, respectively. In (a)-(f), 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: The AKAcc of each method in each top percentage of ranked proteins over four species was calculated. As shown in Fig 7, the AKAccs of LSED-XC methods in each top percentage of ranked proteins are higher than those of XC methods consistently. Especially, the AKAccs of LSED-BC and LSED-DC are always higher, compared with XC methods and other LSED-XC methods, which indicates their superior performances to identify essential proteins for different species. Furthermore, the higher AKAccs of LSED-XC methods suggest that in most situations the LCSs taking into consideration the cellular compartments seem to be more predictive than the centrality scores measured in the global PINs and the essential proteins of different species can be explored better in the PSLINs.

Bottom Line: 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.In this paper, we demonstrate that the centrality-lethality rule also exists in Protein Subcellular Localization Interaction Networks (PSLINs).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