Instructions/Help

The DeepLoc 2.0 server predicts the multi-label subcellular localization of eukaryotics proteins using Neural Networks algorithm trained on Uniprot proteins with experimental evidence of subcellular localization. The model can predict whether a protein can be in one or multiple localizations inside the eukaryotic cell. It only uses the sequence information to perform the prediction. Additionally, DeepLoc 2.0 can predict the presence of the sorting signal(s) that had an influence on the prediction of the subcellular localization(s). The importance of each amino acid in the predicted localization is also included as an "attention" plot. Positions in the sequence with a high attention value are deemed more relevant for the prediction. This does not mean that a particular amino acid is very important for the prediction but that a region in the neighbourhood of those positions has more weight in the final prediction of the model.

The DeepLoc 2.0 server can be run using two versions of the same model.

• The high-quality model utilizes the ProtT5-XL-Uniref50 transformer (ProtT5). This model provides a more accurate prediction at the expense of longer computation time due to the size of the model (3 billion parameters). Use case: high-quality prediction for a small number of proteins.
• The high-throughput model utilizes the 33-layer ESM transformer (ESM1b). This smaller model (650 million parameters) has the advantage of a faster computation time with a slight decrease in accuracy compared to the ProtT5 model. Use case: high-throughput prediction for a larger number of proteins.

The DeepLoc 2.0 server requires protein sequence(s) in fasta format, and can not handle nucleic acid sequences.

Two different versions of the output can be selected before running DeepLoc 2.0. The long output will generate an attention plot per sequence while the short output will not generate any plots.

Paste protein sequence(s) in fasta format or upload a fasta file.

After the server successfully finishes the job, a summary page shows up. If an error happens during the prediction a log will appear specifying the error.