Prosit is a deep neural network to predict iRT values and MS2 spectra for given peptide sequences. You can use it at proteomicsdb.org/prosit/ without installation.
Prosit was tested on Ubuntu 16.04, CUDA 8.0, CUDNN 6 with Nvidia Tesla K40c and Titan Xp graphic cards with the dependencies above.
The time installation takes is dependent on your download speed (Prosit downloads a 3GB docker container). In our tests installation time is ~5 minutes.
Prosit assumes your models are in directories that look like this:
- model.yml - a saved keras model
- config.yml - a model specifying names of inputs and outputs of the model
- weights file(s) - that follow the template
You can download pre-trained models for HCD fragmentation prediction and iRT prediction on https://figshare.com/projects/Prosit/35582.
The following command will load your model from
In the example GPU device 0 is used for computation. The default PORT is 5000.
make server MODEL_SPECTRA=/path/to/fragmentation_model/ MODEL_IRT=/path/to/irt_model/
Currently two output formats are supported: a MaxQuant style
msms.txt not including the iRT value and a generic text file (that works with Spectronaut)
Please find an example input file at
example/peptidelist.csv. After starting the server you can run the following commands, depending on what output format you prefer:
curl -F "peptides=@examples/peptidelist.csv" http://127.0.0.1:5000/predict/generic curl -F "peptides=@examples/peptidelist.csv" http://127.0.0.1:5000/predict/msp curl -F "peptides=@examples/peptidelist.csv" http://127.0.0.1:5000/predict/msms
The examples take about 4s to run. Expected output files (.generic, .msp and .msms) can be found in
Using Prosit on your data
You can adjust the example above to your own needs. Send any list of (Peptide, Precursor charge, Collision energy) in the format of
/example/peptidelist.csv to a running instance of the Prosit server.
Please note: Sequences with amino acid U, O, or X are not supported. Modifications except "M(ox)" are not supported. Each C is treated as Cysteine with carbamidomethylation (fixed modification in MaxQuant).
- Load the models given as in the MODEL_X environment variables
- Start a server and wait for inputs
- On incomming request
- transform peptide list to model input format (numpy arrays)
- predict fragment intensity and iRT with the loaded models for the given peptides
- transform prediction to the requested output format and return response