Version history

2.0 The current server. New in this version:
  • Model architecture: DeepLoc 2.0 is based on a transformer language model, trained on a massive dataset of unlabeled protein sequences.
  • Multi-localization prediction: DeepLoc 2.0 is able to predict proteins that are located in more than one compartment.
  • Sorting signal prediction: DeepLoc 2.0 predicts the presence of nine types of sorting signals. For prediction of the precise positions of N- or C-terminal sorting signals, we refer to specific predictors such as SignalP, TargetP, or NetGPI.
  • Logo-like attention plot: The plot visualizes which part(s) of the input sequence were important for prediction. We show in the article that there is a correlation between the attention and the positions of known sorting signals, and that this correlation is stronger than for DeepLoc 1.0.
DeepLoc 2.0: multi-label subcellular localization prediction using protein language models.
Vineet Thumuluri, Jose Juan Almagro Armenteros, Alexander Rosenberg Johansen, Henrik Nielsen, Ole Winther.
Nucleic Acids Research, Web server issue 2022.
1.0 The original DeepLoc server.
DeepLoc: prediction of protein subcellular localization using deep learning
Jose Juan Almagro Armenteros, Casper Kaae Sønderby, Søren Kaae Sønderby, Henrik Nielsen, Ole Winther.
Bioinformatics, 33:3387–3395 (2017).