MOTIVATION: The prediction of eukaryotic protein subcellular localization is a 
well-studied topic in bioinformatics due to its relevance in proteomics 
research. Many machine learning methods have been successfully applied in this 
task, but in most of them, predictions rely on annotation of homologues from 
knowledge databases. For novel proteins where no annotated homologues exist, and 
for predicting the effects of sequence variants, it is desirable to have methods 
for predicting protein properties from sequence information only.
RESULTS: Here, we present a prediction algorithm using deep neural networks to 
predict protein subcellular localization relying only on sequence information. 
At its core, the prediction model uses a recurrent neural network that processes 
the entire protein sequence and an attention mechanism identifying protein 
regions important for the subcellular localization. The model was trained and 
tested on a protein dataset extracted from one of the latest UniProt releases, 
in which experimentally annotated proteins follow more stringent criteria than 
previously. We demonstrate that our model achieves a good accuracy (78% for 10 
categories; 92% for membrane-bound or soluble), outperforming current 
state-of-the-art algorithms, including those relying on homology information.
AVAILABILITY AND IMPLEMENTATION: The method is available as a web server at 
http://www.cbs.dtu.dk/services/DeepLoc. Example code is available at 
https://github.com/JJAlmagro/subcellular_localization. The dataset is available 
at http://www.cbs.dtu.dk/services/DeepLoc/data.php.
CONTACT: jjalma@dtu.dk.