im2deep 0.3.0

Framework for prediction of collisional cross-section of peptides.

IM2Deep

Collisional cross-section prediction for (modified) peptides.

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Introduction

IM2Deep is a CCS predictor for (modified) peptides. It is able to accurately predict CCS for modified peptides, even if the modification wasn't observed during training.

Installation

Install with pip: pip install im2deep

Usage

Basic CLI usage:

im2deep <path/to/peptide_file.csv>

If you want to calibrate your predictions (HIGHLY recommended), please provide a calibration file:

im2deep <path/to/peptide_file.csv> --calibration_file <path/to/peptide_file_with_CCS.csv>

For an overview of all CLI arguments, run im2deep --help.

Input files

Both peptide and calibration files are expected to be comma-separated values (CSV) with the following columns:

• seq: unmodified peptide sequence
• modifications: every modifications should be listed as location|name, separated by a pipe character (|) between the location, the name, and other modifications. location is an integer counted starting at 1 for the first AA. 0 is reserved for N-terminal modifications, -1 for C-terminal modifications. name has to correspond to a Unimod (PSI-MS) name.
• charge: peptide precursor charge
• CCS: collisional cross-section (only for calibration file)

For example:

seq,modifications,charge,CCS
VVDDFADITTPLK,,2,422.9984309464991
GVEVLSLTPSFMDIPEK,12|Oxidation,2,464.6568644356109
SYSGREFDDLSPTEQK,,2,468.9863221739147
SYSQSILLDLTDNR,,2,460.9340710819608
DEELIHLDGK,,2,383.8693416055445
IPQEKCILQTDVK,5|Butyryl|6|Carbamidomethyl,3,516.2079366048176