Limits...
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.

Show MeSH

Related in: MedlinePlus

Distribution of 6900 LOCATE proteins for various subcellular compartments. The subcellular compartments are CP (cytoplasm), CV (cytoplasmic vesicle), EC (extracellular), ER (endoplasmic reticulum), ES (endosome), GA (Golgi apparatus), LS (lysosome), MC (mitochondria), N (nucleus), PM (plasma membrane), and TJ (tight junction).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2957692&req=5

Figure 2: Distribution of 6900 LOCATE proteins for various subcellular compartments. The subcellular compartments are CP (cytoplasm), CV (cytoplasmic vesicle), EC (extracellular), ER (endoplasmic reticulum), ES (endosome), GA (Golgi apparatus), LS (lysosome), MC (mitochondria), N (nucleus), PM (plasma membrane), and TJ (tight junction).

Mentions: As there is no specific database which combines protein interaction, metabolic and SCL information, we integrated data from independent individual databases containing pertinent information. The SCL data from LOCATE [13], PPI data from five interaction databases and metabolic data from two databases (Figure 1; details in materials and methods section) were integrated. LOCATE contains literature-curated SCL information for about 6900 human proteins (Figure 2) in various subcellular compartments. The distribution of proteins is not homogeneous across the various subcellular compartments, with proteins from some compartments such as the nucleus and the plasma membrane being over-represented. Therefore, we have carefully normalized the dataset, while measuring the statistical properties of our networks, to remove any bias toward specific SCL compartments.


Network analysis of human protein location.

Kumar G, Ranganathan S - BMC Bioinformatics (2010)

Distribution of 6900 LOCATE proteins for various subcellular compartments. The subcellular compartments are CP (cytoplasm), CV (cytoplasmic vesicle), EC (extracellular), ER (endoplasmic reticulum), ES (endosome), GA (Golgi apparatus), LS (lysosome), MC (mitochondria), N (nucleus), PM (plasma membrane), and TJ (tight junction).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Distribution of 6900 LOCATE proteins for various subcellular compartments. The subcellular compartments are CP (cytoplasm), CV (cytoplasmic vesicle), EC (extracellular), ER (endoplasmic reticulum), ES (endosome), GA (Golgi apparatus), LS (lysosome), MC (mitochondria), N (nucleus), PM (plasma membrane), and TJ (tight junction).
Mentions: As there is no specific database which combines protein interaction, metabolic and SCL information, we integrated data from independent individual databases containing pertinent information. The SCL data from LOCATE [13], PPI data from five interaction databases and metabolic data from two databases (Figure 1; details in materials and methods section) were integrated. LOCATE contains literature-curated SCL information for about 6900 human proteins (Figure 2) in various subcellular compartments. The distribution of proteins is not homogeneous across the various subcellular compartments, with proteins from some compartments such as the nucleus and the plasma membrane being over-represented. Therefore, we have carefully normalized the dataset, while measuring the statistical properties of our networks, to remove any bias toward specific SCL compartments.

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