title: Assessment of the reliability of protein-protein interactions and protein function prediction.
abstract: As more and more high-throughput protein-protein interaction data are collected, the task of estimating the reliability of different data sets becomes increasingly important. In this paper, we present our study of two groups of protein-protein interaction data, the physical interaction data and the protein complex data, and estimate the reliability of these data sets using three different measurements: (1) the distribution of gene expression correlation coefficients, (2) the reliability based on gene expression correlation coefficients, and (3) the accuracy of protein function predictions. We develop a maximum likelihood method to estimate the reliability of protein interaction data sets according to the distribution of correlation coefficients of gene expression profiles of putative interacting protein pairs. The results of the three measurements are consistent with each other. The MIPS protein complex data have the highest mean gene expression correlation coefficients (0.256) and the highest accuracy in predicting protein functions (70% sensitivity and specificity), while Ito's Yeast two-hybrid data have the lowest mean (0.041) and the lowest accuracy (15% sensitivity and specificity). Uetz's data are more reliable than Ito's data in all three measurements, and the TAP protein complex data are more reliable than the HMS-PCI data in all three measurements as well. The complex data sets generally perform better in function predictions than do the physical interaction data sets. Proteins in complexes are shown to be more highly correlated in gene expression. The results confirm that the components of a protein complex can be assigned to functions that the complex carries out within a cell. There are three interaction data sets different from the above two groups: the genetic interaction data, the in-silico data and the syn-express data. Their capability of predicting protein functions generally falls between that of the Y2H data and that of the MIPS protein complex data. The supplementary information is available at the following Web site: http://www-hto.usc.edu/-msms/AssessInteraction/.
authors.affiliation: Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, 1042 West 36th Place, Los Angeles, CA 90089-1113, USA.
authors.forename: Minghua
authors.initials: M
authors.lastname: Deng
authors.forename: Fengzhu
authors.initials: F
authors.lastname: Sun
authors.forename: Ting
authors.initials: T
authors.lastname: Chen
mesh_terms: D019295:Computational Biology; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D011506:Proteins / Q000235:genetics / Q000378:metabolism / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D011506:Proteins