Dataset Information

Complementing the Eukaryotic Protein Interactome.

ABSTRACT:

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Protein interaction networks are important for the understanding of regulatory mechanisms, for the explanation of experimental data and for the prediction of protein functions. Unfortunately, most interaction data is available only for model organisms. As a possible remedy, the transfer of interactions to organisms of interest is common practice, but it is not clear when interactions can be transferred from one organism to another and, thus, the confidence in the derived interactions is low. Here, we propose to use a rich set of features to train Random Forests in order to score transferred interactions. We evaluated the transfer from a range of eukaryotic organisms to S. cerevisiae using orthologs. Directly transferred interactions to S. cerevisiae are on average only 24% consistent with the current S. cerevisiae interaction network. By using commonly applied filter approaches the transfer precision can be improved, but at the cost of a large decrease in the number of transferred interactions. Our Random Forest approach uses various features derived from both the target and the source network as well as the ortholog annotations to assign confidence values to transferred interactions. Thereby, we could increase the average transfer consistency to 85%, while still transferring almost 70% of all correctly transferable interactions. We tested our approach for the transfer of interactions to other species and showed that our approach outperforms competing methods for the transfer of interactions to species where no experimental knowledge is available. Finally, we applied our predictor to score transferred interactions to 83 targets species and we were able to extend the available interactome of B. taurus, M. musculus and G. gallus with over 40,000 interactions each. Our transferred interaction networks are publicly available via our web interface, which allows to inspect and download transferred interaction sets of different sizes, for various species, and at specified expected precision levels.

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http://services.bio.ifi.lmu.de/coin-db/.

SUBMITTER: Pesch R

PROVIDER: S-EPMC3688968 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Nipah virus is an emerging, highly pathogenic, zoonotic virus of the Paramyxoviridae family. Human transmission occurs by close contact with infected animals, the consumption of contaminated food, or, occasionally, via other infected individuals. Currently, we lack therapeutic or prophylactic treatments for Nipah virus. To develop these agents we must now improve our understanding of the host-virus interactions that underpin a productive infection. This aim led us to perform the present work, in which we identified 101 human-Nipah virus protein-protein interactions (PPIs), most of which (88) are novel. This data set provides a comprehensive view of the host complexes that are manipulated by viral proteins. Host targets include the PRP19 complex and the microRNA (miRNA) processing machinery. Furthermore, we explored the biologic consequences of the interaction with the PRP19 complex and found that the Nipah virus W protein is capable of altering p53 control and gene expression. We anticipate that these data will help in guiding the development of novel interventional strategies to counter this emerging viral threat.IMPORTANCE Nipah virus is a recently discovered virus that infects a wide range of mammals, including humans. Since its discovery there have been yearly outbreaks, and in some of them the mortality rate has reached 100% of the confirmed cases. However, the study of Nipah virus has been largely neglected, and currently we lack treatments for this infection. To develop these agents we must now improve our understanding of the host-virus interactions that underpin a productive infection. In the present work, we identified 101 human-Nipah virus protein-protein interactions using an affinity purification approach coupled with mass spectrometry. Additionally, we explored the cellular consequences of some of these interactions. Globally, this data set offers a comprehensive and detailed view of the host machinery's contribution to the Nipah virus's life cycle. Furthermore, our data present a large number of putative drug targets that could be exploited for the treatment of this infection.

Dataset Information

Complementing the Eukaryotic Protein Interactome.

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