At least a quarter of all genes in most genomes contain putative transmembrane (TM) helices, and helical membrane protein interactions are a major component of the overall cellular interactome. However, current experimental techniques for large-scale detection of protein-protein interactions are biased against membrane proteins. Here, we define protein-protein interaction broadly as co-complexation, and develop a weighted-voting procedure to predict interactions among yeast helical membrane proteins by optimally combining evidence based on diverse genome-wide information such as sequence, function, localization, abundance, regulation, and phenotype. We use logistic regression to simultaneously optimize the weights of all evidence sources for best discrimination based on a set of known helical membrane protein interactions. The resulting integrated classifier not only significantly outperforms classifiers based on any single genomic feature, but also does better than a benchmark Naive Bayes classifier (using a simplifying assumption of conditional independence among features). Finally, we apply the optimized classifier genome-wide, and construct a comprehensive map of predicted helical membrane protein interactome in yeast. This can serve as a guide for prioritizing further experimental validation efforts.
|Evidence ID||Analyze ID||Interactor||Interactor Systematic Name||Interactor||Interactor Systematic Name||Type||Assay||Annotation||Action||Modification||Phenotype||Source||Reference||Note|
|Evidence ID||Analyze ID||Gene||Gene Systematic Name||Gene Ontology Term||Gene Ontology Term ID||Qualifier||Aspect||Method||Evidence||Source||Assigned On||Annotation Extension||Reference|
|Evidence ID||Analyze ID||Gene||Gene Systematic Name||Phenotype||Experiment Type||Experiment Type Category||Mutant Information||Strain Background||Chemical||Details||Reference|
|Evidence ID||Analyze ID||Regulator||Regulator Systematic Name||Target||Target Systematic Name||Experiment||Assay||Construct||Conditions||Strain Background||Reference|