Proceedings of the National Academy of Sciences · 2005 · 275 citations · 46 references
Life SciencesBiochemistryBioinformaticsComputational BiologyProtein-protein InteractionsProteomicsMembrane Protein InteractionsMembrane ProteinsProtein Database SearchProtein Function AnnotationProtein IdentificationProtein-protein Interaction AnalysisAssayHigh-throughput Interaction Data AnalysisIntegral Membrane ProteinsProtein Interaction PredictionProtein-membrane InteractionsProtein-protein Interaction PredictionSupport Vector ClassificationSupport Vector Machine Classification
We carried out a large-scale screen to identify interactions between integral membrane proteins of Saccharomyces cerevisiae by using a modified split-ubiquitin technique. Among 705 proteins annotated as integral membrane, we identified 1,985 putative interactions involving 536 proteins. To ascribe confidence levels to the interactions, we used a support vector machine algorithm to classify interactions based on the assay results and protein data derived from the literature. Previously identified and computationally supported interactions were used to train the support vector machine, which identified 131 interactions of highest confidence, 209 of the next highest confidence, 468 of the next highest, and the remaining 1,085 of low confidence. This study provides numerous putative interactions among a class of proteins that have been difficult to analyze on a high-throughput basis by other approaches. The results identify potential previously undescribed components of established biological processes and roles for integral membrane proteins of ascribed functions.
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