Integrated prediction of the helical membrane protein interactome in yeast.
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 Naïve 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.
Xia
Yu
Y
Department of Molecular Biophysics and Biochemistry, Yale University New Haven, CT 06520, USA.
Lu
Long J
LJ
Gerstein
Mark
M
D008565:Membrane Proteins / Q000737:chemistry*; D008958:Models, Molecular; D008962:Models, Theoretical; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae / Q000737:chemistry*; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000378:metabolism
D008565:Membrane Proteins; D029701:Saccharomyces cerevisiae Proteins