Triangle network motifs predict complexes by complementing high-error interactomes with structural information
<p>Abstract</p> <p>Background</p> <p>A lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2009-06-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/10/196 |
| _version_ | 1818664209370578944 |
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| author | Labudde Dirk Winter Christof Andreopoulos Bill Schroeder Michael |
| author_facet | Labudde Dirk Winter Christof Andreopoulos Bill Schroeder Michael |
| author_sort | Labudde Dirk |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>A lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.</p> <p>Results</p> <p>We find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.</p> <p>Conclusion</p> <p>Given high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN.</p> |
| first_indexed | 2024-12-17T05:29:06Z |
| format | Article |
| id | doaj.art-24333bcae6a043cabe417db59882a5f2 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-17T05:29:06Z |
| publishDate | 2009-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-24333bcae6a043cabe417db59882a5f22022-12-21T22:01:47ZengBMCBMC Bioinformatics1471-21052009-06-0110119610.1186/1471-2105-10-196Triangle network motifs predict complexes by complementing high-error interactomes with structural informationLabudde DirkWinter ChristofAndreopoulos BillSchroeder Michael<p>Abstract</p> <p>Background</p> <p>A lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.</p> <p>Results</p> <p>We find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.</p> <p>Conclusion</p> <p>Given high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN.</p>http://www.biomedcentral.com/1471-2105/10/196 |
| spellingShingle | Labudde Dirk Winter Christof Andreopoulos Bill Schroeder Michael Triangle network motifs predict complexes by complementing high-error interactomes with structural information BMC Bioinformatics |
| title | Triangle network motifs predict complexes by complementing high-error interactomes with structural information |
| title_full | Triangle network motifs predict complexes by complementing high-error interactomes with structural information |
| title_fullStr | Triangle network motifs predict complexes by complementing high-error interactomes with structural information |
| title_full_unstemmed | Triangle network motifs predict complexes by complementing high-error interactomes with structural information |
| title_short | Triangle network motifs predict complexes by complementing high-error interactomes with structural information |
| title_sort | triangle network motifs predict complexes by complementing high error interactomes with structural information |
| url | http://www.biomedcentral.com/1471-2105/10/196 |
| work_keys_str_mv | AT labuddedirk trianglenetworkmotifspredictcomplexesbycomplementinghigherrorinteractomeswithstructuralinformation AT winterchristof trianglenetworkmotifspredictcomplexesbycomplementinghigherrorinteractomeswithstructuralinformation AT andreopoulosbill trianglenetworkmotifspredictcomplexesbycomplementinghigherrorinteractomeswithstructuralinformation AT schroedermichael trianglenetworkmotifspredictcomplexesbycomplementinghigherrorinteractomeswithstructuralinformation |