im2deep 2.0.0a2

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 deep learning-based CCS predictor for (modified) peptides. It accurately predicts collisional cross-section (CCS) values for modified peptides, even if the modification wasn't observed during training. The tool supports both single-conformer and multi-conformer predictions for peptide ions.

Installation

Install with pip:

pip install im2deep

For local development/docs:

uv sync --group dev --group docs

Usage

Command Line Interface (CLI)

Basic prediction:

im2deep <path/to/peptide_file.csv>

With calibration (HIGHLY recommended):

im2deep <path/to/peptide_file.csv> --calibration-precursors <path/to/calibration_file.csv>

Calibration options:

• --calibrate-per-charge: Calculate separate calibration shift factors per charge state (recommended, default true)
• --use-charge-state: Charge state for global calibration when --calibrate-per-charge is disabled

Multi-conformer prediction: To use the multi-output prediction model (requires optional dependencies):

im2deep <path/to/peptide_file.csv> --calibration-precursors <path/to/calibration_file.csv> --multi

Output options:

im2deep <path/to/peptide_file.csv> --output-file predictions.csv

For a complete overview of all CLI arguments, run:

im2deep --help

Python API

IM2Deep can also be used programmatically:

from im2deep import predict, predict_and_calibrate
from psm_utils import PSMList

# Load your peptides as PSMList
psm_list = PSMList(psm_list=[...])  # or use psm_utils.io.read_file()

# Simple prediction
predictions = predict(psm_list)

# Prediction with calibration
psm_list_calibration = PSMList(psm_list=[...])  # Must contain CCS values
calibrated_predictions = predict_and_calibrate(
    psm_list=psm_list,
    psm_list_cal=psm_list_calibration
)

Input Files

Standard Format

IM2Deep accepts any format supported by psm_utils, including:

• Peptide Record (.peprec)
• MaxQuant msms.txt
• MSFragger PSM files
• And more...

Legacy CSV Format

Alternatively, use comma-separated values (CSV) with the following columns:

• seq: Unmodified peptide sequence
• modifications: Modifications listed as location|name, separated by pipe (|) characters
  • location: Integer starting at 1 for the first amino acid
    • 0 = N-terminal modification
    • -1 = C-terminal modification
  • name: Must correspond to a Unimod (PSI-MS) name
• charge: Peptide precursor charge state
• CCS: Collisional cross-section (only required for calibration files)

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

Important Notes

• Calibration: Highly recommended for accurate predictions. Calibration corrects for systematic differences between predicted and observed CCS values.
• Charge states: IM2Deep predictions are reliable for charge states up to z=6. PSMs with higher charge states are automatically filtered out during validation.

Citing

If you use IM2Deep within the context of (TI)MS²Rescore, please cite the following:

TIMS²Rescore: A DDA-PASEF optimized data-driven rescoring pipeline based on MS²Rescore. Arthur Declercq*, Robbe Devreese*, Jonas Scheid, Caroline Jachmann, Tim Van Den Bossche, Annica Preikschat, David Gomez-Zepeda, Jeewan Babu Rijal, Aurélie Hirschler, Jonathan R Krieger, Tharan Srikumar, George Rosenberger, Dennis Trede, Christine Carapito, Stefan Tenzer, Juliane S Walz, Sven Degroeve, Robbin Bouwmeester, Lennart Martens, and Ralf Gabriels. Journal of Proteome Research (2025) doi:10.1021/acs.jproteome.4c00609

In other cases, please cite the following:

Collisional cross-section prediction for multiconformational peptide ions with IM2Deep. Robbe Devreese, Alireza Nameni, Arthur Declercq, Emmy Terryn, Ralf Gabriels, Francis Impens, Kris Gevaert, Lennart Martens, Robbin Bouwmeester. Anal. Chem. (2025) doi:10.1021/acs.analchem.5c01142