OpenMS Peptide m/z Calculator

An open-source tool for calculating peptide m/z values.

Report Bug/Feature • Submit Code Changes

📋 Overview

This web application provides accurate peptide mass-to-charge (m/z) ratio calculations for proteomics research.

✨ Key Features

• Peptide m/z Calculations: Calculate mass-to-charge ratios with support for modifications and charge states
• Advanced Notation Support:
  • Square bracket modifications: M[Oxidation]PEPTIDE
  • UNIMOD notation: C[UNIMOD:4]PEPTIDE
  • ProForma mass shifts: PEPTIDE[+42.0106]
  • Charge notation: PEPTIDE/2
• Auto-Detection: Automatically recognizes modifications and charge states from sequence notation

🚀 Quick Start

Web Application

Access the calculator through your web browser - simply enter a peptide sequence and get instant m/z calculations.

Example Usage

PEPTIDE                    # Basic sequence
M[Oxidation]PEPTIDE       # With modification
C[UNIMOD:4]PEPTIDE        # UNIMOD notation
PEPTIDE/2                 # With charge state
PEPTIDE[+42.0106]         # ProForma mass shift

💻 Installation

Method: Direct Python Installation

This is the recommended method for development environments and direct Python execution.

Step 1: Create Python Virtual Environment

# Create virtual environment
python -m venv peptide-calculator-env

# Activate virtual environment
# On Windows:
peptide-calculator-env\Scripts\activate
# On macOS/Linux:
source peptide-calculator-env/bin/activate

Step 2: Install Dependencies

The application uses a compiled requirements file for reproducible installations:

# Install all dependencies
pip install -r requirements.txt

# Alternative: Install from pyproject.toml for development
pip install -e .

Step 3: Verify Core Dependencies

# Test PyOpenMS installation
python -c "import pyopenms; print('PyOpenMS version:', pyopenms.__version__)"

# Test Streamlit installation
streamlit --version

📖 Citation

If you use this work, please cite:

Müller, T. D., Siraj, A., et al. OpenMS WebApps: Building User-Friendly Solutions for MS Analysis. 
Journal of Proteome Research (2025). 
https://doi.org/10.1021/acs.jproteome.4c00872

📚 References

This work builds upon the following publications:

1. Pfeuffer, J., Bielow, C., Wein, S. et al. (2024). OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data. Nature Methods, 21, 365–367. https://doi.org/10.1038/s41592-024-02197-7

2. Röst, H. L., Schmitt, U., Aebersold, R., & Malmström, L. (2014). pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library. Proteomics, 14(1), 74-77. https://doi.org/10.1002/pmic.201300246 | PubMed: 24420968