Services
DeepLoc - 1.0
Prediction of eukaryotic protein subcellular localization using deep learning
DeepLoc-1.0 predicts the subcellular localization of eukaryotic proteins. It can differentiate between 10 different localizations: Nucleus, Cytoplasm, Extracellular, Mitochondrion, Cell membrane, Endoplasmic reticulum, Chloroplast, Golgi apparatus, Lysosome/Vacuole and Peroxisome.
NOTE: This is not the newest version of DeepLoc. To use the current version, please go to DeepLoc 2.0! |
Submission
Instructions/Help
The DeepLoc-1.0 server predicts the subcellular localization of eukaryotics proteins using Neural Networks algorithm trained on Uniprot proteins with experimental evidence of subcellular localization. It only uses the sequence information to perform the prediction.
The DeepLoc-1.0 server requires protein sequence(s) in fasta format, and can not handle nucleic acid sequences.
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. Use the navigation bar to flip through the various output pages.
Training and testing data sets
The dataset used to train and test the DeepLoc-1.0 server is available here deeploc_dataset
It is a fasta file composed by header and sequence. The header is composed by the accession number from Uniprot, the annotated subcellular localization and possibly a description field indicating if the protein was part of the test set. The subcellular localization includes an additional label, where S indicates soluble, M membrane and U unknown.
>Q3E7A9 Mitochondrion-S MSNPCQKEACAIQDCLLSHQYDDAKCAKVIDQLYICCSKFYNDNGKDSRSPCCPLPSLLELKMKQRKLTPGDS >Q9SMX3 Mitochondrion-M test MVKGPGLYTEIGKKARDLLYRDYQGDQKFSVTTYSSTGVAITTTGTNKGSLFLGDVATQVKNNNFTADVKVST DSSLLTTLTFDEPAPGLKVIVQAKLPDHKSGKAEVQYFHDYAGISTSVGFTATPIVNFSGVVGTNGLSLGTDV AYNTESGNFKHFNAGFNFTKDDLTASLILNDKGEKLNASYYQIVSPSTVVGAEISHNFTTKENAITVGTQHAL> DPLTTVKARVNNAGVANALIQHEWRPKSFFTVSGEVDSKAIDKSAKVGIALALKP