Prediction of protein function using protein-protein interaction data.
Assigning functions to novel proteins is one of the most important problems in the post-genomic era. Several approaches have been applied to this problem, including analyzing gene expression patterns, phylogenetic profiles, protein fusions and protein-protein interactions. We develop a novel approach that applies the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of its interaction protein partners. For each function of interest and a protein, we predict the probability that the protein has that function using Bayesian approaches. Unlike in other available approaches for protein annotation where a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We apply our method to predict cellular functions (43 categories including a category "others") for yeast proteins defined in the Yeast Proteome Database (YPD), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS, http://mips.gsf.de). We show that our approach outperforms other available methods for function prediction based on protein interaction data.
Deng
Minghua
M
Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-1113, USA.
Zhang
Kui
K
Mehta
Shipra
S
Chen
Ting
T
Sun
Fengzhu
F
D000595:Amino Acid Sequence; D001665:Binding Sites; D005656:Fungal Proteins / Q000032:analysis / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D008390:Markov Chains; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D008969:Molecular Sequence Data; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
D005656:Fungal Proteins