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Network analysis of human protein location.

Kumar G, Ranganathan S - BMC Bioinformatics (2010)

Bottom Line: PLCP analysis revealed that protein interactions are majorly restricted to the same SCL, though significant cross-compartment interactions are seen for nuclear proteins.The MLPI network differs significantly from the PPI network in its SCL distribution.The PPI network shows passive protein interaction, possibly due to its high false positive rate, across different subcellular compartments, which seem to be absent in the MLPI network, as the MLPI network has evolved to maintain high substrate specificity for proteins.

View Article: PubMed Central - HTML - PubMed

Affiliation: ARC Centre of Excellence in Bioinformatics and Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney NSW, Australia. gaurav.kumar@mq.edu.au

ABSTRACT

Background: Understanding cellular systems requires the knowledge of a protein's subcellular localization (SCL). Although experimental and predicted data for protein SCL are archived in various databases, SCL prediction remains a non-trivial problem in genome annotation. Current SCL prediction tools use amino-acid sequence features and text mining approaches. A comprehensive analysis of protein SCL in human PPI and metabolic networks for various subcellular compartments is necessary for developing a robust SCL prediction methodology.

Results: Based on protein-protein interaction (PPI) and metabolite-linked protein interaction (MLPI) networks of proteins, we have compared, contrasted and analysed the statistical properties across different subcellular compartments. We integrated PPI and metabolic datasets with SCL information of human proteins from LOCATE and GOA (Gene Ontology Annotation) and estimated three statistical properties: Chi-square (χ2) test, Paired Localisation Correlation Profile (PLCP) and network topological measures. For the PPI network, Pearson's chi-square test shows that for the same SCL category, twice as many interacting protein pairs are observed than estimated when compared to non-interacting protein pairs (χ2 = 1270.19, P-value < 2.2 × 10(-16)), whereas for MLPI, metabolite-linked protein pairs having the same SCL are observed 20% more than expected, compared to non-metabolite linked proteins (χ2 = 110.02, P-value < 2.2 x10(-16)). To address the issue of proteins with multiple SCLs, we have specifically used the PLCP (Pair Localization Correlation Profile) measure. PLCP analysis revealed that protein interactions are majorly restricted to the same SCL, though significant cross-compartment interactions are seen for nuclear proteins. Metabolite-linked protein pairs are restricted to specific compartments such as the mitochondrion (P-value < 6.0e-07), the lysosome (P-value < 4.7e-05) and the Golgi apparatus (P-value < 1.0e-15). These findings indicate that the metabolic network adds value to the information in the PPI network for the localisation process of proteins in human subcellular compartments.

Conclusions: The MLPI network differs significantly from the PPI network in its SCL distribution. The PPI network shows passive protein interaction, possibly due to its high false positive rate, across different subcellular compartments, which seem to be absent in the MLPI network, as the MLPI network has evolved to maintain high substrate specificity for proteins.

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Average connectivity of a neighbouring nodes. Correlation in the connectivity of neighbours, with respect to a specific node of a given degree in A. PPI network and B. Metabolic network.
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Figure 7: Average connectivity of a neighbouring nodes. Correlation in the connectivity of neighbours, with respect to a specific node of a given degree in A. PPI network and B. Metabolic network.

Mentions: Assortativity measures the collaboration of similar entities to achieve a single goal, whereas a disassortative nature suggests the association of different entities to achieve the same goal. Therefore, to observe the assortative or disassortative nature of human PPI and metabolic networks, we calculated the average degree of the neighbouring proteins as a function of the each nodes degree [18]. For the PPI network, Figure 7A shows an increase in the neighbouring node degrees associated with higher degree nodes. This topological behaviour is the characteristic signature of the assortative network, thus suggesting that PPI is an assortative network. This observation is absent in the metabolic network (Figure 7B), where there is a decrease in the association with the high degree neighbours for the high degree nodes, i.e. nodes with the high degree k tend to be disconnected on an average, to others of lower degree. The power-law exponents (γ) for the degree assortativity are 1.2 and 1.1 in PPI and metabolic networks, respectively.


Network analysis of human protein location.

Kumar G, Ranganathan S - BMC Bioinformatics (2010)

Average connectivity of a neighbouring nodes. Correlation in the connectivity of neighbours, with respect to a specific node of a given degree in A. PPI network and B. Metabolic network.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2957692&req=5

Figure 7: Average connectivity of a neighbouring nodes. Correlation in the connectivity of neighbours, with respect to a specific node of a given degree in A. PPI network and B. Metabolic network.
Mentions: Assortativity measures the collaboration of similar entities to achieve a single goal, whereas a disassortative nature suggests the association of different entities to achieve the same goal. Therefore, to observe the assortative or disassortative nature of human PPI and metabolic networks, we calculated the average degree of the neighbouring proteins as a function of the each nodes degree [18]. For the PPI network, Figure 7A shows an increase in the neighbouring node degrees associated with higher degree nodes. This topological behaviour is the characteristic signature of the assortative network, thus suggesting that PPI is an assortative network. This observation is absent in the metabolic network (Figure 7B), where there is a decrease in the association with the high degree neighbours for the high degree nodes, i.e. nodes with the high degree k tend to be disconnected on an average, to others of lower degree. The power-law exponents (γ) for the degree assortativity are 1.2 and 1.1 in PPI and metabolic networks, respectively.

Bottom Line: PLCP analysis revealed that protein interactions are majorly restricted to the same SCL, though significant cross-compartment interactions are seen for nuclear proteins.The MLPI network differs significantly from the PPI network in its SCL distribution.The PPI network shows passive protein interaction, possibly due to its high false positive rate, across different subcellular compartments, which seem to be absent in the MLPI network, as the MLPI network has evolved to maintain high substrate specificity for proteins.

View Article: PubMed Central - HTML - PubMed

Affiliation: ARC Centre of Excellence in Bioinformatics and Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney NSW, Australia. gaurav.kumar@mq.edu.au

ABSTRACT

Background: Understanding cellular systems requires the knowledge of a protein's subcellular localization (SCL). Although experimental and predicted data for protein SCL are archived in various databases, SCL prediction remains a non-trivial problem in genome annotation. Current SCL prediction tools use amino-acid sequence features and text mining approaches. A comprehensive analysis of protein SCL in human PPI and metabolic networks for various subcellular compartments is necessary for developing a robust SCL prediction methodology.

Results: Based on protein-protein interaction (PPI) and metabolite-linked protein interaction (MLPI) networks of proteins, we have compared, contrasted and analysed the statistical properties across different subcellular compartments. We integrated PPI and metabolic datasets with SCL information of human proteins from LOCATE and GOA (Gene Ontology Annotation) and estimated three statistical properties: Chi-square (χ2) test, Paired Localisation Correlation Profile (PLCP) and network topological measures. For the PPI network, Pearson's chi-square test shows that for the same SCL category, twice as many interacting protein pairs are observed than estimated when compared to non-interacting protein pairs (χ2 = 1270.19, P-value < 2.2 × 10(-16)), whereas for MLPI, metabolite-linked protein pairs having the same SCL are observed 20% more than expected, compared to non-metabolite linked proteins (χ2 = 110.02, P-value < 2.2 x10(-16)). To address the issue of proteins with multiple SCLs, we have specifically used the PLCP (Pair Localization Correlation Profile) measure. PLCP analysis revealed that protein interactions are majorly restricted to the same SCL, though significant cross-compartment interactions are seen for nuclear proteins. Metabolite-linked protein pairs are restricted to specific compartments such as the mitochondrion (P-value < 6.0e-07), the lysosome (P-value < 4.7e-05) and the Golgi apparatus (P-value < 1.0e-15). These findings indicate that the metabolic network adds value to the information in the PPI network for the localisation process of proteins in human subcellular compartments.

Conclusions: The MLPI network differs significantly from the PPI network in its SCL distribution. The PPI network shows passive protein interaction, possibly due to its high false positive rate, across different subcellular compartments, which seem to be absent in the MLPI network, as the MLPI network has evolved to maintain high substrate specificity for proteins.

Show MeSH
Related in: MedlinePlus