Framework for prediction of collisional cross-section of peptides.
Project description
IM2Deep
Collisional cross-section prediction for (modified) peptides.
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 sequencemodifications
: every modifications should be listed aslocation|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 chargeCCS
: 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
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