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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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Protein paired localisation correlation profile. Paired Localisation Correlation Profile (PLCP) for LOCATE and GOA SCLs for major subcellular compartments for the physically interacting or metabolite-linked protein pairs. The subcellular compartments are CP (cytoplasm), CV (cytoplasmic vesicle), EC (extracellular), ER (endoplasmic reticulum), ES (endosome), GA (Golgi apparatus), LS (lysosome), MC (mitochondrion), N (nucleus), PM (plasma membrane), and TJ (tight junction). A and B are LOCATE SCL correlation profiles, whereas C and D are GOA correlation profiles.
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Figure 3: Protein paired localisation correlation profile. Paired Localisation Correlation Profile (PLCP) for LOCATE and GOA SCLs for major subcellular compartments for the physically interacting or metabolite-linked protein pairs. The subcellular compartments are CP (cytoplasm), CV (cytoplasmic vesicle), EC (extracellular), ER (endoplasmic reticulum), ES (endosome), GA (Golgi apparatus), LS (lysosome), MC (mitochondrion), N (nucleus), PM (plasma membrane), and TJ (tight junction). A and B are LOCATE SCL correlation profiles, whereas C and D are GOA correlation profiles.

Mentions: We measured the statistical significance of SCL correlation profile based on the Paired-Localisation Conditional Probability (PLCP; see Methods section for details), for both the LOCATE (manually curated from the literature) data as well as the GOA assigned SCL (excluding electronic annotation, which is automatically-assigned evidence code). Figure 3 shows significant correlation along the diagonals suggesting that the interacting protein pairs tend to co-localize in the same compartment. Comparing the LOCATE-assigned SCL (Figure 3A), we observe a strong correlation for physically interacting protein pairs to occupy the same compartment in the cytoplasm (CP), cytoplasmic vesicles (CV), extracellular (EC), endosomes (ES), Golgi apparatus (GA), lysosome (LS), mitochondrion (MC), nucleus (N) and plasma membrane (PM). The same comparison on the GOA SCL (Figure 3C) shows conservation for EC, ES, GA, MC, N, PM and TJ. We also observed significantly strong correlation of nuclear proteins (Figures 3A and 3C) to interact with proteins found in cytoplasm, ER and Golgi for the LOCATE dataset and the cytoplasm, ER and mitochondrion for the GOA dataset. Similarly, plasma membrane proteins show significant interaction with the proteins in the several other subcellular compartments (Figures 3A and 3C).


Network analysis of human protein location.

Kumar G, Ranganathan S - BMC Bioinformatics (2010)

Protein paired localisation correlation profile. Paired Localisation Correlation Profile (PLCP) for LOCATE and GOA SCLs for major subcellular compartments for the physically interacting or metabolite-linked protein pairs. The subcellular compartments are CP (cytoplasm), CV (cytoplasmic vesicle), EC (extracellular), ER (endoplasmic reticulum), ES (endosome), GA (Golgi apparatus), LS (lysosome), MC (mitochondrion), N (nucleus), PM (plasma membrane), and TJ (tight junction). A and B are LOCATE SCL correlation profiles, whereas C and D are GOA correlation profiles.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Protein paired localisation correlation profile. Paired Localisation Correlation Profile (PLCP) for LOCATE and GOA SCLs for major subcellular compartments for the physically interacting or metabolite-linked protein pairs. The subcellular compartments are CP (cytoplasm), CV (cytoplasmic vesicle), EC (extracellular), ER (endoplasmic reticulum), ES (endosome), GA (Golgi apparatus), LS (lysosome), MC (mitochondrion), N (nucleus), PM (plasma membrane), and TJ (tight junction). A and B are LOCATE SCL correlation profiles, whereas C and D are GOA correlation profiles.
Mentions: We measured the statistical significance of SCL correlation profile based on the Paired-Localisation Conditional Probability (PLCP; see Methods section for details), for both the LOCATE (manually curated from the literature) data as well as the GOA assigned SCL (excluding electronic annotation, which is automatically-assigned evidence code). Figure 3 shows significant correlation along the diagonals suggesting that the interacting protein pairs tend to co-localize in the same compartment. Comparing the LOCATE-assigned SCL (Figure 3A), we observe a strong correlation for physically interacting protein pairs to occupy the same compartment in the cytoplasm (CP), cytoplasmic vesicles (CV), extracellular (EC), endosomes (ES), Golgi apparatus (GA), lysosome (LS), mitochondrion (MC), nucleus (N) and plasma membrane (PM). The same comparison on the GOA SCL (Figure 3C) shows conservation for EC, ES, GA, MC, N, PM and TJ. We also observed significantly strong correlation of nuclear proteins (Figures 3A and 3C) to interact with proteins found in cytoplasm, ER and Golgi for the LOCATE dataset and the cytoplasm, ER and mitochondrion for the GOA dataset. Similarly, plasma membrane proteins show significant interaction with the proteins in the several other subcellular compartments (Figures 3A and 3C).

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