Dataset Information

Novel protein-protein interactions inferred from literature context.

ABSTRACT: 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.

SUBMITTER: van Haagen HH

PROVIDER: S-EPMC2774517 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Novel protein-protein interactions inferred from literature context.

van Haagen Herman H H B M HH 't Hoen Peter A C PA Botelho Bovo Alessandro A de Morrée Antoine A van Mulligen Erik M EM Chichester Christine C Kors Jan A JA den Dunnen Johan T JT van Ommen Gert-Jan B GJ van der Maarel Silvère M SM Kern Vinícius Medina VM Mons Barend B Schuemie Martijn J MJ

PloS one 20091118 11

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 ana ...[more]

PMID: 19924298

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Project description:BACKGROUND: Deciphering physical protein-protein interactions is fundamental to elucidating both the functions of proteins and biological processes. The development of high-throughput experimental technologies such as the yeast two-hybrid screening has produced an explosion in data relating to interactions. Since manual curation is intensive in terms of time and cost, there is an urgent need for text-mining tools to facilitate the extraction of such information. The BioCreative (Critical Assessment of Information Extraction systems in Biology) challenge evaluation provided common standards and shared evaluation criteria to enable comparisons among different approaches. RESULTS: During the benchmark evaluation of BioCreative 2006, all of our results ranked in the top three places. In the task of filtering articles irrelevant to physical protein interactions, our method contributes a precision of 75.07%, a recall of 81.07%, and an AUC (area under the receiver operating characteristic curve) of 0.847. In the task of identifying protein mentions and normalizing mentions to molecule identifiers, our method is competitive among runs submitted, with a precision of 34.83%, a recall of 24.10%, and an F1 score of 28.5%. In extracting protein interaction pairs, our profile-based method was competitive on the SwissProt-only subset (precision = 36.95%, recall = 32.68%, and F1 score = 30.40%) and on the entire dataset (30.96%, 29.35%, and 26.20%, respectively). From the biologist's point of view, however, these findings are far from satisfactory. The error analysis presented in this report provides insight into how performance could be improved: three-quarters of false negatives were due to protein normalization problems (532/698), and about one-quarter were due to problems with correctly extracting interactions for this system. CONCLUSION: We present a text-mining framework to extract physical protein-protein interactions from the literature. Three key issues are addressed, namely filtering irrelevant articles, identifying protein names and normalizing them to molecule identifiers, and extracting protein-protein interactions. Our system is among the top three performers in the benchmark evaluation of BioCreative 2006. The tool will be helpful for manual interaction curation and can greatly facilitate the process of extracting protein-protein interactions.

Project description:Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways.

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Novel protein-protein interactions inferred from literature context.

Publications

Novel protein-protein interactions inferred from literature context.

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