Journal
JOURNAL OF MOLECULAR BIOLOGY
Volume 357, Issue 1, Pages 339-349Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jmb.2005.12.067
Keywords
helical membrane protein; protein-protein interaction; integrated prediction; Naive Bayes; logistic regression
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Funding
- NIGMS NIH HHS [P50 GM62413-01] Funding Source: Medline
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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. (c) 2005 Elsevier Ltd. All rights resented.
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