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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 top c% ranked proteins, identified by LSED-XC methods and XC methods, to be essential proteins of Saccharomyces cerevisiae.Eight centrality methods (DC, BC, CC, SC, EC, IC, NC, and ION) were adopted to calculate centrality scores from the global PIN, respectively. LSED was combined with these centrality methods to calculate Localization-specific Centrality Scores from PSLINs separately. 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. The proteins are ranked in the descending order based on their Localization-specific Centrality Scores (LCSs) and centrality scores computed by LSED-XC methods and XC methods, respectively. 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 percentages of true essential proteins were calculated. The figure shows the percentage of true essential proteins identified by each method in each top percentage of ranked proteins. The digits in brackets stand for the number of proteins ranked in each top percentage. For example, since the total number of ranked proteins of Saccharomyces cerevisiae is 6,304, the number of proteins ranked in top 1% is about 63 (= 6,304*1%).
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pone.0130743.g003: Percentage of top c% ranked proteins, identified by LSED-XC methods and XC methods, to be essential proteins of Saccharomyces cerevisiae.Eight centrality methods (DC, BC, CC, SC, EC, IC, NC, and ION) were adopted to calculate centrality scores from the global PIN, respectively. LSED was combined with these centrality methods to calculate Localization-specific Centrality Scores from PSLINs separately. 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. The proteins are ranked in the descending order based on their Localization-specific Centrality Scores (LCSs) and centrality scores computed by LSED-XC methods and XC methods, respectively. 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 percentages of true essential proteins were calculated. The figure shows the percentage of true essential proteins identified by each method in each top percentage of ranked proteins. The digits in brackets stand for the number of proteins ranked in each top percentage. For example, since the total number of ranked proteins of Saccharomyces cerevisiae is 6,304, the number of proteins ranked in top 1% is about 63 (= 6,304*1%).

Mentions: Fig 3 shows the percentage of true essential proteins identified by LSED-XC methods and XC methods in each top percentage of ranked proteins of Saccharomyces cerevisiae. For most topology-based centrality methods, we can observe that the percentages of true essential proteins correctly predicted by LSED-XC methods are greatly higher than those of the corresponding XC methods in the top 1%–25% of ranked proteins. The IAcc of LSED-XC methods is shown in Table 2. In Table 2, the positive value of IAcc gained by LSED-XC method indicates that the Acc of the corresponding XC method can be improved by LSED method. LSED-DC, LSED-IC, LSED-EC, LSED-SC, LSED-BC, LSED-CC, and LSED-NC always gain positive values of IAcc in the top 1%–25% of ranked proteins of Saccharomyces cerevisiae.


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 top c% ranked proteins, identified by LSED-XC methods and XC methods, to be essential proteins of Saccharomyces cerevisiae.Eight centrality methods (DC, BC, CC, SC, EC, IC, NC, and ION) were adopted to calculate centrality scores from the global PIN, respectively. LSED was combined with these centrality methods to calculate Localization-specific Centrality Scores from PSLINs separately. 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. The proteins are ranked in the descending order based on their Localization-specific Centrality Scores (LCSs) and centrality scores computed by LSED-XC methods and XC methods, respectively. 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 percentages of true essential proteins were calculated. The figure shows the percentage of true essential proteins identified by each method in each top percentage of ranked proteins. The digits in brackets stand for the number of proteins ranked in each top percentage. For example, since the total number of ranked proteins of Saccharomyces cerevisiae is 6,304, the number of proteins ranked in top 1% is about 63 (= 6,304*1%).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4482623&req=5

pone.0130743.g003: Percentage of top c% ranked proteins, identified by LSED-XC methods and XC methods, to be essential proteins of Saccharomyces cerevisiae.Eight centrality methods (DC, BC, CC, SC, EC, IC, NC, and ION) were adopted to calculate centrality scores from the global PIN, respectively. LSED was combined with these centrality methods to calculate Localization-specific Centrality Scores from PSLINs separately. 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. The proteins are ranked in the descending order based on their Localization-specific Centrality Scores (LCSs) and centrality scores computed by LSED-XC methods and XC methods, respectively. 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 percentages of true essential proteins were calculated. The figure shows the percentage of true essential proteins identified by each method in each top percentage of ranked proteins. The digits in brackets stand for the number of proteins ranked in each top percentage. For example, since the total number of ranked proteins of Saccharomyces cerevisiae is 6,304, the number of proteins ranked in top 1% is about 63 (= 6,304*1%).
Mentions: Fig 3 shows the percentage of true essential proteins identified by LSED-XC methods and XC methods in each top percentage of ranked proteins of Saccharomyces cerevisiae. For most topology-based centrality methods, we can observe that the percentages of true essential proteins correctly predicted by LSED-XC methods are greatly higher than those of the corresponding XC methods in the top 1%–25% of ranked proteins. The IAcc of LSED-XC methods is shown in Table 2. In Table 2, the positive value of IAcc gained by LSED-XC method indicates that the Acc of the corresponding XC method can be improved by LSED method. LSED-DC, LSED-IC, LSED-EC, LSED-SC, LSED-BC, LSED-CC, and LSED-NC always gain positive values of IAcc in the top 1%–25% of ranked proteins of Saccharomyces cerevisiae.

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