Identification of Protein Subcellular Localization With Network and Functional Embeddings

The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein-protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods.

Automated summary

This study introduces an embedding-based method for predicting the subcellular localization of proteins by learning functional and network embeddings. The combined embeddings result in a novel representation of protein locations, leading to a final classification model with superior performance compared to conventional methods, as demonstrated in a benchmark dataset with 4,861 proteins from 16 locations.