Refining Protein Subcellular Localization

Abstract

The study of protein subcellular localization is important to elucidate protein function. Even in well-studied organisms such as yeast, experimental methods have not been able to provide a full coverage of localization. The development of bioinformatic predictors of localization can bridge this gap. We have created a Bayesian network predictor called PSLT2 that considers diverse protein characteristics, including the combinatorial presence of InterPro motifs and protein interaction data. We compared the localization predictions of PSLT2 to high-throughput experimental localization datasets. Disagreements between these methods generally involve proteins that transit through or reside in the secretory pathway. We used our multi-compartmental predictions to refine the localization annotations of yeast proteins primarily by distinguishing between soluble lumenal proteins and soluble proteins peripherally associated with organelles. To our knowledge, this is the first tool to provide this functionality. We used these sub-compartmental predictions to characterize cellular processes on an organellar scale. The integration of diverse protein characteristics and protein interaction data in an appropriate setting can lead to high-quality detailed localization annotations for whole proteomes. This type of resource is instrumental in developing models of whole organelles that provide insight into the extent of interaction and communication between organelles and help define organellar functionality.

Synopsis

Eukaryotic cells are divided into various morphologically and functionally distinct compartments. Proteins must be targeted to the appropriate compartment to ensure proper function. Understanding protein subcellular localization is important to help understand not only the function of individual proteins but also the organization of the cell as a whole. Bioinformatic predictors of localization can provide such information quickly for large numbers of proteins. The authors of this paper have created a localization predictor called PSLT2 that considers the combinatorial presence of protein motifs and domains as well as protein interactions in yeast proteins. PSLT2 can predict the localization of all yeast proteins to nine different compartments: the endoplasmic reticulum, Golgi apparatus, cytosol, nucleus, peroxisome, plasma membrane, lysosome, mitochondrion, and extracellular space. The authors also investigated how to identify and predict proteins that localize to more than one compartment. They compared the localization predictions of PSLT2 to those determined through high-throughput tagging and microscopy experiments for yeast proteins. Disagreements between these methods generally involve proteins that transit through or reside in the secretory pathway.