Requirements:

• Python numpy
• Python pandas
• Python multiprocessing
• PyTables
• XGBoost (python API) (only required for training)
• Cython (http://cython.org/)

MS2PIPC requires the machine specific compilation of the C-code:

sh compile.sh

Pre-trained HCD models for the b- and y-ions can be found in the /models folder. These C-coded decision tree models are compiled by running the compile.sh script that writes the python module ms2pipfeatures_pyx.so which is imported into the main python script ms2pipC.py:

usage: ms2pipC.py [-h] [-s FILE] [-w FILE.ext] [-c INT] <peptide file>

positional arguments:
  <peptide file>  list of peptides

optional arguments:
  -h, --help      show this help message and exit
  -c FILE         config file
  -s FILE         .mgf MS2 spectrum file (optional)
  -w FILE         write feature vectors to FILE.pkl (optional)
  -i              iTRAQ models
  -p              phospho models
  -m INT          number of cpu's to use

The -i flag makes ms2pipC use the NIST iTRAQ4 models (HCD onnly).

The -i flag in combination with the -p flag makes ms2pipC use the NIST iTRAQ4 phospho models (HCD onnly).

Several ms2pipC options need to be set in this configfile.

The models that should be used are set as frag_method=X where X is either CID or HCD. The fragment ion error tolerance is set as frag_error=X where is X is the tolerance in Da.

PTMs (see further) are set as ptm=X,Y,Z for each internal PTM where X is a string that represents the PTM, Y is the difference in Da associated with the PTM and Z is the amino acid that is modified by the PTM. N-terminal modifications are specified as nterm=X,Y where X is gain a string that represents the PTM, Y is again the difference in Da associated with the PTM. Similarly, c-terminal modifications are specified as cterm=X,Y where X is gain a string that represents the PTM, Y is again the difference in Da associated with the PTM.

To apply the pre-trained models you need to pass only a <peptide file> to ms2pipC.py. This file contains the peptide sequences for which you want to predict the b- and y-ion peak intensities. The file is space separated and contains four columns with the following header names:

• spec_id: an id for the peptide/spectrum
• modifications: a string indicating the modified amino acids
• peptide: the unmodified amino acid sequence
• charge: charge state to predict

The predictions are saved in a .csv file with the name <peptide_file>_predictions.csv. If you want the output to be in the form of an .mgf file, replace the variable mgf in line 142 of ms2pipC.py.

The spec_id column is a unique identifier for each peptide that will be used in the TITLE field of the predicted MS2 .mgf file. The modifications column is a string that lists the PTMs in the peptide. Each PTM is written as A|B where A is the location of the PTM in the peptide (the first amino acid has location 1, location 0 is used for n-term modificatios, while -1 is used for c-term modifications) and B is a string that represent the PTM as defined in the configfile (-c command line argument). Multiple PTMs in the modifications column are concatenated with '|'. As an example, suppose the configfile contains the line

ptm=Cam,57.02146,C
nterm=Ace,42.010565
cterm=Glyloss,-58.005479

then a modifications string could like 0|Ace|2|Cam|5|Cam|-1|Glyloss which means that the second and fifth amin acid is modified with Cam,
that there is a n-terminal modification Ace, and that there is a c-terminal modification Glyloss,

To compile a feature vector dataset you need to supply the MS2 .mgf file (option -s) and the name of the file to write the feature vectors to (option -w) to ms2pipC.py. The spec_id column in the <peptide file> should match the TITLE field of the corresponding MS2 spectrum in the .mgf file and is used to find the targets for the feature vectors.

In the folder tests, run pytest. This will run the tests in test_features.py, which verify if the feature and target extraction are working properly. (The tests must be updated when we add or remove features!) To do this the pytest package must be installed (pip install pytest)

The python script

$ python convert_to_mgf.py <file>.msp <title>

converts a spectral library in .msp format into a spectrum .mgf file, a <peptide file> and a <meta> file.

The script

usage: train_xgboost_c.py [-h] [-c INT] <vectors.pkl or .h5> <type>

XGBoost training

positional arguments:
  <vectors.pkl or .h5>  feature vector file
  <type>         model type

optional arguments:
  -h, --help     show this help message and exit
  -c INT         number of cpu's to use

reads the pickled feature vector file <vectors.pkl or .h5> and trains an XGBoost model. The type option should be "B" for b-ions and "Y" for y-ions.

Hyperparameters should still be optimized. You will need to digg into the script for model selection.

This script will write the XGBoost models as .c files that can be compiled and linked through Cython. Just put the models in the /models folder , change the #include directives in ms2pipfeatures_c.c, and recompile the ms2pipfeatures_pyx.so model by running the compile.sh script.