TMT model files not compatible with Prost codes provided here

[cctsou]

I was able to build the server with TMT model files, but then I encountered the following error by running a small peptide list as a csv file:

modified_sequence,collision_energy,precursor_charge,fragmentation
ALNNLPALQAM(ox)TLALNR,35,2,HCD
EAAALLDDCIFNM(ox)VLLK,35,3,CID
DPLSSYNIIAWDWNGPK,35,2,HCD
KTDCCILSALLFQGLLR,35,3,CID

Error message
[2023-02-22 18:35:16,739] ERROR in app: Exception on /predict/msp [POST] Traceback (most recent call last): File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 2447, in wsgi_app response = self.full_dispatch_request() File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1952, in full_dispatch_request rv = self.handle_user_exception(e) File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1821, in handle_user_exception reraise(exc_type, exc_value, tb) File "/usr/local/lib/python3.5/dist-packages/flask/_compat.py", line 39, in reraise raise value File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1950, in full_dispatch_request rv = self.dispatch_request() File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1936, in dispatch_request return self.view_functions[rule.endpoint](**req.view_args) File "/root/prosit/server.py", line 51, in return_msp result = predict(flask.request.files["peptides"]) File "/root/prosit/server.py", line 29, in predict data = prediction.predict(data, d_spectra) File "/root/prosit/prediction.py", line 13, in predict x = io_local.get_array(data, d_model["config"]["x"]) File "/root/prosit/io_local.py", line 5, in get_array utils.check_mandatory_keys(tensor, keys) File "/root/prosit/utils.py", line 7, in check_mandatory_keys raise KeyError("key {} is missing".format(key)) KeyError: 'key fragmentation is missing'

I believe that the error was because "fragmentation" was not parsed in the input data frame, I tried adding "fragmentation" into the csv parsing function below but I do not know how fragmentation is encoded. Could you please help? Could you provide the Prosit codes that are fully compatible with the TMT models you provided?

def csv(df):
df.reset_index(drop=True, inplace=True)
assert "modified_sequence" in df.columns
assert "collision_energy" in df.columns
assert "precursor_charge" in df.columns
data = {
"collision_energy_aligned_normed": get_numbers(df.collision_energy) / 100.0,
"sequence_integer": get_sequence_integer(df.modified_sequence),
"fragmentation": df.fragmentation,
"precursor_charge_onehot": get_precursor_charge_onehot(df.precursor_charge),
"masses_pred": get_mz_applied(df),
}

Originally posted by @cctsou in #84 (comment)