Assessment of prediction accuracy of protein function from protein--protein interaction data.
Functional prediction of open reading frames coded in the genome is one of the most important tasks in yeast genomics. Among a number of large-scale experiments for assigning certain functional classes to proteins, experiments determining protein-protein interaction are especially important because interacting proteins usually have the same function. Thus, it seems possible to predict the function of a protein when the function of its interacting partner is known. However, in vitro experiments often suffer from artifacts and a protein can often have multiple binding partners with different functions. We developed an objective prediction method that can systematically include the information of indirect interaction. Our method can predict the subcellular localization, the cellular role and the biochemical function of yeast proteins with accuracies of 72.7%, 63.6% and 52.7%, respectively. The prediction accuracy rises for proteins with more than three binding partners and thus we present the open prediction results for 16 such proteins.
Laboratory of Genome Database, Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai Minato-ku, Tokyo 108-8639, Japan.
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Hishigaki
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Nakai
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Ono
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Tanigami
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Takagi
D005656:Fungal Proteins / Q000235:genetics / Q000378:metabolism / Q000502:physiology*; D008954:Models, Biological; D016366:Open Reading Frames; D011237:Predictive Value of Tests; D011485:Protein Binding; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism / Q000502:physiology*; D013347:Subcellular Fractions / Q000378:metabolism / Q000502:physiology
D005656:Fungal Proteins