A Bayesian system integrating expression data with sequence patterns for localizing proteins: comprehensive application to the yeast genome.
We develop a probabilistic system for predicting the subcellular localization of proteins and estimating the relative population of the various compartments in yeast. Our system employs a Bayesian approach, updating a protein's probability of being in a compartment, based on a diverse range of 30 features. These range from specific motifs (e.g. signal sequences or the HDEL motif) to overall properties of a sequence (e.g. surface composition or isoelectric point) to whole-genome data (e.g. absolute mRNA expression levels or their fluctuations). The strength of our approach is the easy integration of many features, particularly the whole-genome expression data. We construct a training and testing set of approximately 1300 yeast proteins with an experimentally known localization from merging, filtering, and standardizing the annotation in the MIPS, Swiss-Prot and YPD databases, and we achieve 75 % accuracy on individual protein predictions using this dataset. Moreover, we are able to estimate the relative protein population of the various compartments without requiring a definite localization for every protein. This approach, which is based on an analogy to formalism in quantum mechanics, gives better accuracy in determining relative compartment populations than that obtained by simply tallying the localization predictions for individual proteins (on the yeast proteins with known localization, 92% versus 74%). Our training and testing also highlights which of the 30 features are informative and which are redundant (19 being particularly useful). After developing our system, we apply it to the 4700 yeast proteins with currently unknown localization and estimate the relative population of the various compartments in the entire yeast genome. An unbiased prior is essential to this extrapolated estimate; for this, we use the MIPS localization catalogue, and adapt recent results on the localization of yeast proteins obtained by Snyder and colleagues using a minitransposon system. Our final localizations for all approximately 6000 proteins in the yeast genome are available over the web at: http://bioinfo.mbb.yale. edu/genome/localize.
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Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
Gerstein
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D020816:Amino Acid Motifs; D001499:Bayes Theorem; D001692:Biological Transport; D002462:Cell Membrane / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D003593:Cytoplasm / Q000737:chemistry; D019992:Databases as Topic; D004721:Endoplasmic Reticulum / Q000737:chemistry; D005656:Fungal Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D016681:Genome, Fungal; D006056:Golgi Apparatus / Q000737:chemistry; D020407:Internet; D007526:Isoelectric Point; D008565:Membrane Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D008928:Mitochondria / Q000737:chemistry; D009687:Nuclear Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D021382:Protein Sorting Signals / Q000737:chemistry / Q000235:genetics / Q000502:physiology; D020543:Proteome; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013499:Surface Properties; D015003:Yeasts / Q000737:chemistry / Q000166:cytology / Q000235:genetics / Q000378:metabolism*
D005656:Fungal Proteins; D008565:Membrane Proteins; D009687:Nuclear Proteins; D021382:Protein Sorting Signals; D020543:Proteome