Novel protein-protein interactions inferred from literature context.
We have developed a method that predicts Protein-Protein Interactions (PPIs) based on the similarity of the context in which proteins appear in literature. This method outperforms previously developed PPI prediction algorithms that rely on the conjunction of two protein names in MEDLINE abstracts. W...
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2009
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oai:doaj.org-article:cf40eed6ff034debbe315d57638f44d32021-11-25T06:28:01ZNovel protein-protein interactions inferred from literature context.1932-620310.1371/journal.pone.0007894https://doaj.org/article/cf40eed6ff034debbe315d57638f44d32009-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19924298/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203We have developed a method that predicts Protein-Protein Interactions (PPIs) based on the similarity of the context in which proteins appear in literature. This method outperforms previously developed PPI prediction algorithms that rely on the conjunction of two protein names in MEDLINE abstracts. We show significant increases in coverage (76% versus 32%) and sensitivity (66% versus 41% at a specificity of 95%) for the prediction of PPIs currently archived in 6 PPI databases. A retrospective analysis shows that PPIs can efficiently be predicted before they enter PPI databases and before their interaction is explicitly described in the literature. The practical value of the method for discovery of novel PPIs is illustrated by the experimental confirmation of the inferred physical interaction between CAPN3 and PARVB, which was based on frequent co-occurrence of both proteins with concepts like Z-disc, dysferlin, and alpha-actinin. The relationships between proteins predicted by our method are broader than PPIs, and include proteins in the same complex or pathway. Dependent on the type of relationships deemed useful, the precision of our method can be as high as 90%. The full set of predicted interactions is available in a downloadable matrix and through the webtool Nermal, which lists the most likely interaction partners for a given protein. Our framework can be used for prioritizing potential interaction partners, hitherto undiscovered, for follow-up studies and to aid the generation of accurate protein interaction maps.Herman H H B M van HaagenPeter A C 't HoenAlessandro Botelho BovoAntoine de MorréeErik M van MulligenChristine ChichesterJan A KorsJohan T den DunnenGert-Jan B van OmmenSilvère M van der MaarelVinícius Medina KernBarend MonsMartijn J SchuemiePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 4, Iss 11, p e7894 (2009) |
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DOAJ |
| collection |
DOAJ |
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| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q Herman H H B M van Haagen Peter A C 't Hoen Alessandro Botelho Bovo Antoine de Morrée Erik M van Mulligen Christine Chichester Jan A Kors Johan T den Dunnen Gert-Jan B van Ommen Silvère M van der Maarel Vinícius Medina Kern Barend Mons Martijn J Schuemie Novel protein-protein interactions inferred from literature context. |
| description |
We have developed a method that predicts Protein-Protein Interactions (PPIs) based on the similarity of the context in which proteins appear in literature. This method outperforms previously developed PPI prediction algorithms that rely on the conjunction of two protein names in MEDLINE abstracts. We show significant increases in coverage (76% versus 32%) and sensitivity (66% versus 41% at a specificity of 95%) for the prediction of PPIs currently archived in 6 PPI databases. A retrospective analysis shows that PPIs can efficiently be predicted before they enter PPI databases and before their interaction is explicitly described in the literature. The practical value of the method for discovery of novel PPIs is illustrated by the experimental confirmation of the inferred physical interaction between CAPN3 and PARVB, which was based on frequent co-occurrence of both proteins with concepts like Z-disc, dysferlin, and alpha-actinin. The relationships between proteins predicted by our method are broader than PPIs, and include proteins in the same complex or pathway. Dependent on the type of relationships deemed useful, the precision of our method can be as high as 90%. The full set of predicted interactions is available in a downloadable matrix and through the webtool Nermal, which lists the most likely interaction partners for a given protein. Our framework can be used for prioritizing potential interaction partners, hitherto undiscovered, for follow-up studies and to aid the generation of accurate protein interaction maps. |
| format |
article |
| author |
Herman H H B M van Haagen Peter A C 't Hoen Alessandro Botelho Bovo Antoine de Morrée Erik M van Mulligen Christine Chichester Jan A Kors Johan T den Dunnen Gert-Jan B van Ommen Silvère M van der Maarel Vinícius Medina Kern Barend Mons Martijn J Schuemie |
| author_facet |
Herman H H B M van Haagen Peter A C 't Hoen Alessandro Botelho Bovo Antoine de Morrée Erik M van Mulligen Christine Chichester Jan A Kors Johan T den Dunnen Gert-Jan B van Ommen Silvère M van der Maarel Vinícius Medina Kern Barend Mons Martijn J Schuemie |
| author_sort |
Herman H H B M van Haagen |
| title |
Novel protein-protein interactions inferred from literature context. |
| title_short |
Novel protein-protein interactions inferred from literature context. |
| title_full |
Novel protein-protein interactions inferred from literature context. |
| title_fullStr |
Novel protein-protein interactions inferred from literature context. |
| title_full_unstemmed |
Novel protein-protein interactions inferred from literature context. |
| title_sort |
novel protein-protein interactions inferred from literature context. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2009 |
| url |
https://doaj.org/article/cf40eed6ff034debbe315d57638f44d3 |
| work_keys_str_mv |
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