Tissue-specific Subcellular Localization Prediction using Multi-label Markov Random Fields.

The understanding of subcellular localization (SCL) of proteins and proteome variation in the different tissues and organs of the human body are two crucial aspects for increasing our knowledge of the dynamic rules of proteins, the cell biology and the mechanism of diseases. Although there have been tremendous contributions to these two fields independently, it still exists the lack of the knowledge of the variation of spatial distribution of proteins in the different tissues. Here, we proposed an approach that allows predicting protein localization on tissue specificity through the use of tissue-specific functional associations and physical protein-protein interactions (PPIs). We applied our previously developed Bayesian collective Markov random fields (BCMRFs) on protein-protein interaction networks filtered by tissue-specific functional associations for nine types of tissues focusing on eight high-level SCLs. The evaluated results demonstrate the strength of our approach in predicting tissue-specific SCLs. We identified 1314 proteins that their SCLs were previously proven cell line dependent. We predicted 549 novel tissue-specific located candidate proteins while some of them were validated via text-mining.