IM2Deep
Collisional cross-section prediction for (modified) peptides.
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
With Python 3.11 or higher, install with pip:
pip install im2deep
We recommend using a venv or conda virtual environment.
For development
Clone this repository and use uv to install:
git clone https://github.com/CompOmics/IM2Deep.git
cd IM2Deep
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 sequencemodifications: Modifications listed aslocation|name, separated by pipe (|) characterslocation: Integer starting at 1 for the first amino acid0= N-terminal modification-1= C-terminal modification
name: Must correspond to a Unimod (PSI-MS) name
charge: Peptide precursor charge stateCCS: 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
Metadata
Release files for im2deep 2.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| im2deep-2.0.2.tar.gz | 70.3 MB | With provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| im2deep-2.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 140.6 MB
Release files / im2deep-2.0.2.tar.gz
| Download URL | im2deep-2.0.2.tar.gz |
|---|---|
| Size | 70.3 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
53bd587ddfe1d7bf3b3a7054a7bad513d636265d515a8619752b2af1af033b89
|
|
BLAKE2b-256 checksum How to use checksums |
14d53588c776eae75bbf5a5953fb747484ef65d54b1b58b8904110c8270584e4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 16, 2026.
Transparency logRelease files / im2deep-2.0.2-py3-none-any.whl
| Download URL | im2deep-2.0.2-py3-none-any.whl |
|---|---|
| Size | 70.3 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
cc0ae09ec5c578983812eb6d1ea412fe3717791dade5a1be9193a45ea2b945fa
|
|
BLAKE2b-256 checksum How to use checksums |
58d2499f27db85f8e0acc5afae0452fa3064d9c4600084e94b50e41d7255af86
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 16, 2026.
Transparency logSecurity for im2deep 2.0.2
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
Full provenance
All 2 files in this release have been uploaded with provenance metadata.
Attestation for im2deep-2.0.2.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 16, 2026.
Transparency logA note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.