Predicting protein cellular localization using a domain projection method
We investigate the co-occurrence of domain families in eukaryotic proteins to predict protein cellular localization. Approximately half (300) of SMART domains form a "small-world network", linked by no more than seven degrees of separation. Projection of the domains onto two-dimensional sp...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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2003
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| _version_ | 1797088801945812992 |
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| author | Mott, R Schultz, J Bork, P Ponting, C |
| author_facet | Mott, R Schultz, J Bork, P Ponting, C |
| author_sort | Mott, R |
| collection | OXFORD |
| description | We investigate the co-occurrence of domain families in eukaryotic proteins to predict protein cellular localization. Approximately half (300) of SMART domains form a "small-world network", linked by no more than seven degrees of separation. Projection of the domains onto two-dimensional space reveals three clusters that correspond to cellular compartments containing secreted, cytoplasmic, and nuclear proteins. The projection method takes into account the existence of "bridging" domains, that is, instances where two domains might not occur with each other but frequently co-occur with a third domain; in such circumstances the domains are neighbors in the projection. While the majority of domains are specific to a compartment ("locale"), and hence may be used to localize any protein that contains such a domain, a small subset of domains either are present in multiple locales or occur in transmembrane proteins. Comparison with previously annotated proteins shows that SMART domain data used with this approach can predict, with 92% accuracy, the localizations of 23% of eukaryotic proteins. The coverage and accuracy will increase with improvements in domain database coverage. This method is complementary to approaches that use amino-acid composition or identify sorting sequences; these methods may be combined to further enhance prediction accuracy. |
| first_indexed | 2024-03-07T02:55:17Z |
| format | Journal article |
| id | oxford-uuid:af10ec16-e512-4acc-a8bc-a4696faafe94 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T02:55:17Z |
| publishDate | 2003 |
| record_format | dspace |
| spelling | oxford-uuid:af10ec16-e512-4acc-a8bc-a4696faafe942022-03-27T03:47:05ZPredicting protein cellular localization using a domain projection methodJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:af10ec16-e512-4acc-a8bc-a4696faafe94EnglishSymplectic Elements at Oxford2003Mott, RSchultz, JBork, PPonting, CWe investigate the co-occurrence of domain families in eukaryotic proteins to predict protein cellular localization. Approximately half (300) of SMART domains form a "small-world network", linked by no more than seven degrees of separation. Projection of the domains onto two-dimensional space reveals three clusters that correspond to cellular compartments containing secreted, cytoplasmic, and nuclear proteins. The projection method takes into account the existence of "bridging" domains, that is, instances where two domains might not occur with each other but frequently co-occur with a third domain; in such circumstances the domains are neighbors in the projection. While the majority of domains are specific to a compartment ("locale"), and hence may be used to localize any protein that contains such a domain, a small subset of domains either are present in multiple locales or occur in transmembrane proteins. Comparison with previously annotated proteins shows that SMART domain data used with this approach can predict, with 92% accuracy, the localizations of 23% of eukaryotic proteins. The coverage and accuracy will increase with improvements in domain database coverage. This method is complementary to approaches that use amino-acid composition or identify sorting sequences; these methods may be combined to further enhance prediction accuracy. |
| spellingShingle | Mott, R Schultz, J Bork, P Ponting, C Predicting protein cellular localization using a domain projection method |
| title | Predicting protein cellular localization using a domain projection method |
| title_full | Predicting protein cellular localization using a domain projection method |
| title_fullStr | Predicting protein cellular localization using a domain projection method |
| title_full_unstemmed | Predicting protein cellular localization using a domain projection method |
| title_short | Predicting protein cellular localization using a domain projection method |
| title_sort | predicting protein cellular localization using a domain projection method |
| work_keys_str_mv | AT mottr predictingproteincellularlocalizationusingadomainprojectionmethod AT schultzj predictingproteincellularlocalizationusingadomainprojectionmethod AT borkp predictingproteincellularlocalizationusingadomainprojectionmethod AT pontingc predictingproteincellularlocalizationusingadomainprojectionmethod |