Deep generative model for protein subcellular localization prediction

Abstract Protein sequence not only determines its structure but also provides important clues of its subcellular localization. Although a series of artificial intelligence models have been reported to predict protein subcellular localization, most of them provide only textual outputs. Here, we present deepGPS, a deep generative model for protein subcellular localization prediction. After training with protein primary sequences and fluorescence images, deepGPS shows the ability to predict cytoplasmic and nuclear localizations by reporting both textual labels and generative images as outputs. In addition, cell-type-specific deepGPS models can be developed by using distinct image datasets from different cell lines for comparative analyses. Moreover, deepGPS shows potential to be further extended for other specific organelles, such as vesicles and endoplasmic reticulum, even with limited volumes of training data. Finally, the openGPS website (https://bits.fudan.edu.cn/opengps) is constructed to provide a publicly accessible and user-friendly platform for studying protein subcellular localization and function.