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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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Schematic representation of data integration. Schematic representation of data integration. SCL information of LOCATE database integrated with that of interaction and metabolic data. The resulting integrated data is represented in XML format.
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Figure 1: Schematic representation of data integration. Schematic representation of data integration. SCL information of LOCATE database integrated with that of interaction and metabolic data. The resulting integrated data is represented in XML format.

Mentions: The availability of a large number of protein interaction and metabolic datasets from multiple databases has motivated us to conduct a statistical study to benchmark the predictive ability of localisation of human proteins, with respect to the various subcellular compartments. In this study, we collated PPI interaction and metabolite-linked protein interaction (metabolic information) from seven major databases and integrated these with the high quality SCL information present in the LOCATE database [13] (Figure 1; see Materials and Methods for details), to critically analyze the PPI and metabolic datasets for the SCL assignment of human proteins. Using experimentally validated physical interaction and metabolic datasets archived in various databases, we compared SCL annotations assigned by LOCATE with that of the Gene Ontology (GO) assignment for major subcellular compartments: cytoplasm (GO:0005737), cytoplasmic vesicle (GO:0016023), extracellular (GO:0005576), endoplasmic reticulum (GO:0005783), endosomes (GO:0005767), Golgi apparatus (GO:0005794), lysosomes (GO:0005764), mitochondria (GO:0005739), nucleus (GO:0005634), plasma membrane (GO:0005886) and tight junction (GO:0005923). Our results provide an estimate of the reliability of SCL predictive ability of human proteins in the absence of sequence and structural features using the high-throughput protein interaction and metabolic dataset.


Network analysis of human protein location.

Kumar G, Ranganathan S - BMC Bioinformatics (2010)

Schematic representation of data integration. Schematic representation of data integration. SCL information of LOCATE database integrated with that of interaction and metabolic data. The resulting integrated data is represented in XML format.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Schematic representation of data integration. Schematic representation of data integration. SCL information of LOCATE database integrated with that of interaction and metabolic data. The resulting integrated data is represented in XML format.
Mentions: The availability of a large number of protein interaction and metabolic datasets from multiple databases has motivated us to conduct a statistical study to benchmark the predictive ability of localisation of human proteins, with respect to the various subcellular compartments. In this study, we collated PPI interaction and metabolite-linked protein interaction (metabolic information) from seven major databases and integrated these with the high quality SCL information present in the LOCATE database [13] (Figure 1; see Materials and Methods for details), to critically analyze the PPI and metabolic datasets for the SCL assignment of human proteins. Using experimentally validated physical interaction and metabolic datasets archived in various databases, we compared SCL annotations assigned by LOCATE with that of the Gene Ontology (GO) assignment for major subcellular compartments: cytoplasm (GO:0005737), cytoplasmic vesicle (GO:0016023), extracellular (GO:0005576), endoplasmic reticulum (GO:0005783), endosomes (GO:0005767), Golgi apparatus (GO:0005794), lysosomes (GO:0005764), mitochondria (GO:0005739), nucleus (GO:0005634), plasma membrane (GO:0005886) and tight junction (GO:0005923). Our results provide an estimate of the reliability of SCL predictive ability of human proteins in the absence of sequence and structural features using the high-throughput protein interaction and metabolic dataset.

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