MSModDetector

A Python module to study post-translational modfication (PTM) patterns from individual ion mass spectrometry (I2MS) data. I2MS is a top-down mass spectrometry (MS) approach that generates true mass spectra without the need to infer charge states. MSModDetector first detects and quantifies mass shifts for a protein of interest in profile I2MS data and subsequently infers potential PTM patterns using linear programming. Before running MSModDetector on your profile I2MS data, make sure you have a fasta file containing the protein sequnce of interest and a list of PTMs that should be considered for the analysis.

Installation and requirements

MSModDetector can be installed with the following command:

$ git clone https://github.com/marjanfaizi/MSModDetector.git

Required Python packages to run this code will be installed with the following command:

$ cd MSModDetector
$ pip install -r requirements.txt

This repository was tested with Python 3.8.5 on macOS 12.2.

Required input data

In order to run MSModDetector on your raw I2MS data to identify mass shifts for a protein of interest and to infer potential PTM patterns, the following files are rquired:

• profile raw I2MS data should be stored in the directory "raw_data"
• a csv table, called metadata.csv, containing information about the experimental set up stored in the directory "raw_data"
• fasta file of the protein of interest stored in the directory "fasta_files"
• a csv table with all the PTMs that should be considered for inferring PTM patterns stored in the directory "modifications"

Raw data and metadata table

The profile raw I2MS data and the metadata table should be stored in the "raw_data" directory. The metadata file should contain informtation about the file names of the raw data, their condition and the replicate number. See in the "raw_data" directory for an example table.

Fasta file

The fasta file containing the sequence of the protein of interest can be downloaded from https://www.uniprot.org (or any other protein sequence database).

Modification table

The modification table should contain the following columns:

• unimod_id: The Unimod identifier of the PTM from https://www.unimod.org
• ptm_id: Abbreviation of the PTM, which is used to display the PTM patterns
• ptm_name: Full name of the PTM
• ptm_mass: Average mass in Da of the PTM
• composition: Elemental composition of the PTM
• upper_bound: Determines the upper bound of modification sites for the PTM
• site: Determines the amino acid resiudes that can be modified
• function: Is the PTM function biological or is it an artifact

A modification table example is given in the directory "modifications".

How to run MSModDetector

Make sure that the required Python packages are installed and all required files are stored in the correct directories. To run MSModDetector you need to specify the directory where the raw data and metadata table are stored, the name of the modification table and fasta file, the start mass and end mass of the range where the algorithm should search for mass shifts, and the size of the sliding window that iterates through the mass spectrum and searches for mass shifts. Here is an example how to run MSModDetector for experimental data of endogenous p53.

$ cd src
$ python main.py -data "../raw_data/" -mod "modifications_P04637.csv" -fasta "P04637.fasta" -start 43750.0 -end 44520.0 -wsize 10

Other meta parameters can be changed if the default values are not suited. A description of all meta parameters can be find using the help function:

$ python main.py --help

MSModDetector outputs a table with the identified mass shifts, the corresponding potential PTM patterns, and relative abundances for every mass shift. If you choose to obtain more than one possible PTM pattern solution for every mass shift, then another table with k optimal solutions will be generated as well. All results will be stored in "output".