Dataset Information

Proteome-wide protein interaction measurements of bacterial proteins of unknown function.

ABSTRACT: Despite the enormous proliferation of bacterial genome data, surprisingly persistent collections of bacterial proteins have resisted functional annotation. In a typical genome, roughly 30% of genes have no assigned function. Many of these proteins are conserved across a large number of bacterial genomes. To assign a putative function to these conserved proteins of unknown function, we created a physical interaction map by measuring biophysical interaction of these proteins. Binary protein--protein interactions in the model organism Streptococcus pneumoniae (TIGR4) are measured with a microfluidic high-throughput assay technology. In some cases, informatic analysis was used to restrict the space of potential binding partners. In other cases, we performed in vitro proteome-wide interaction screens. We were able to assign putative functions to 50 conserved proteins of unknown function that we studied with this approach.

SUBMITTER: Meier M

PROVIDER: S-EPMC3545810 | biostudies-other | 2013 Jan

REPOSITORIES: biostudies-other

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Proteome-wide protein interaction measurements of bacterial proteins of unknown function.

Meier Matthias M Sit Rene V RV Quake Stephen R SR

Proceedings of the National Academy of Sciences of the United States of America 20121224 2

Despite the enormous proliferation of bacterial genome data, surprisingly persistent collections of bacterial proteins have resisted functional annotation. In a typical genome, roughly 30% of genes have no assigned function. Many of these proteins are conserved across a large number of bacterial genomes. To assign a putative function to these conserved proteins of unknown function, we created a physical interaction map by measuring biophysical interaction of these proteins. Binary protein--prote ...[more]

PMID: 23267104

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Project description:BackgroundHuman T-cell leukemia viruses (HTLV) tend to induce some fatal human diseases like Adult T-cell Leukemia (ATL) by targeting human T lymphocytes. To indentify the protein-protein interactions (PPI) between HTLV viruses and Homo sapiens is one of the significant approaches to reveal the underlying mechanism of HTLV infection and host defence. At present, as biological experiments are labor-intensive and expensive, the identified part of the HTLV-human PPI networks is rather small. Although recent years have witnessed much progress in computational modeling for reconstructing pathogen-host PPI networks, data scarcity and data unavailability are two major challenges to be effectively addressed. To our knowledge, no computational method for proteome-wide HTLV-human PPI networks reconstruction has been reported.ResultsIn this work we develop Multi-instance Adaboost method to conduct homolog knowledge transfer for computationally reconstructing proteome-wide HTLV-human PPI networks. In this method, the homolog knowledge in the form of gene ontology (GO) is treated as auxiliary homolog instance to address the problems of data scarcity and data unavailability, while the potential negative knowledge transfer is automatically attenuated by AdaBoost instance reweighting. The cross validation experiments show that the homolog knowledge transfer in the form of independent homolog instances can effectively enrich the feature information and substitute for the missing GO information. Moreover, the independent tests show that the method can validate 70.3% of the recently curated interactions, significantly exceeding the 2.1% recognition rate by the HT-Y2H experiment. We have used the method to reconstruct the proteome-wide HTLV-human PPI networks and further conducted gene ontology based clustering of the predicted networks for further biomedical research. The gene ontology based clustering analysis of the predictions provides much biological insight into the pathogenesis of HTLV retroviruses.ConclusionsThe Multi-instance AdaBoost method can effectively address the problems of data scarcity and data unavailability for the proteome-wide HTLV-human PPI interaction networks reconstruction. The gene ontology based clustering analysis of the predictions reveals some important signaling pathways and biological modules that HTLV retroviruses are likely to target.

Dataset Information

Proteome-wide protein interaction measurements of bacterial proteins of unknown function.

Publications

Proteome-wide protein interaction measurements of bacterial proteins of unknown function.

Similar Datasets

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