README.md

metadata

license: cc-by-nc-4.0
size_categories:
  - 100K<n<1M

Pep2Prob: Peptide-Specific Fragment Ion Probability Prediction Dataset

Pep2Prob is a comprehensive dataset designed to predict peptide-specific fragment ion probability in tandem mass spectrometry (MS/MS) based proteomics studies. This dataset addresses the limitations of conventional global statistical approaches by enabling the development of models that can predict fragmentation probabilities based on peptide sequence context.

📊 Dataset Overview and Highlights

• The dataset provides Fragment ion probability statistics for 610,117 unique peptide precursors derived from over 183 million high-resolution HCD spectra
• Diverse representation of precursors with varying lengths (6-40 amino acids) and charge states (1-8)
• High-quality annotations derived from validated peptide assignments with 0.1% false discovery rate
• A novel train-test split scheme is adapted to minimize structural similarity between entries in the training and the testing set.

🔗 Code and Documentation

• GitHub Repository: https://github.com/Bandeira-Lab/pep2prob-benchmark
• Documentation: Complete usage instructions and examples available in the GitHub repository

⚠️ Important Data Access Notice

HuggingFace Statistics Issue: The repository statistics incorrectly show ~3.15 million entries due to a platform counting error. HuggingFace sums across our five train-test splits (610,117 × 5 ≈ 3.15M) instead of recognizing these as the same unique precursors distributed into separate folds.

Recommended Data Access Methods:

1. Using the downloader in our GitHub (recommended):

# Download from GitHub
git clone https://github.com/Bandeira-Lab/pep2prob-benchmark.git
cd pep2prob-benchmark
pip install -r requirements.txt             (if missing any requried packages)
python data/download_data.py

2. Manual download for specific splits using wget with the URL.

📁 Dataset Structure

Pep2Prob/
├── data/pep2prob_dataset.parquet           # Info for all the precursors
├── meta_data/            
│   ├── X_columns.parquet                   # Input feature metadata
│   └── Y_columns.parquet                   # Target variable metadata
├── train_test_split_set_1/                 # Train-test split 1
│   ├── train_indices.parquet
│   └── test_indices.parquet
├── train_test_split_set_2/                 # Train-test split 2
│   ├── train_indices.parquet
│   └── test_indices.parquet
├── train_test_split_set_3/                 # Train-test split 3
│   ├── train_indices.parquet
│   └── test_indices.parquet
├── train_test_split_set_4/                 # Train-test split 4
│   ├── train_indices.parquet
│   └── test_indices.parquet
└── train_test_split_set_5/                 # Train-test split 5
    ├── train_indices.parquet
    └── test_indices.parquet

File Descriptions

• pep2prob_dataset.csv: Main dataset containing fragment ion probability statistics for 610,117 unique peptide precursors, derived from over 183 million high-resolution HCD MS/MS spectra
• X_columns.csv: Metadata describing input features (peptide sequence information, amino acid properties, fragment ion types, physicochemical features)
• Y_columns.csv: Metadata describing target variables (probability values for different fragment ion types)
• train_test_split_set_X/: Five pre-defined train-test splits ensuring no sequence similarity between training and testing sets, preventing data leakage

🎯 Train-test split Methodology

Our dataset uses a sophisticated sequence-similarity-aware splitting strategy:

1. Graph-based clustering: Precursors are grouped based on sequence similarity (identical sequences, shared prefixes/suffixes of length 6)
2. Component-based splitting: Connected components are distributed across five balanced folds
3. No data leakage: Ensures similar peptides appear in only one split
4. Robust evaluation: Enables fair assessment of model generalization to novel peptide sequences

🔬 Applications

This dataset enables:

• Model Development: Training peptide-specific fragment ion probability prediction models
• Benchmarking: Standardized evaluation of machine learning approaches with varying complexity
• Proteomics Enhancement: Improving peptide identification algorithms and tools for library search, database search, mass spectrum prediction, de novo sequencing...
• Fragmentation Research: Exploring relationships between peptide sequence context and fragmentation behavior
• Quality Control: Identifying problematic spectra through unexpected fragmentation patterns

📖 Citation

If you use this dataset in your research, please cite:

@misc{xu2025pep2prob,
      title={Pep2Prob Benchmark: Predicting Fragment Ion Probability for MS$^2$-based Proteomics}, 
      author={Hao Xu and Zhichao Wang and Shengqi Sang and Pisit Wajanasara and Nuno Bandeira},
      year={2025},
      eprint={2508.21076},
      archivePrefix={arXiv},
      primaryClass={q-bio.BM},
      url={https://arxiv.org/abs/2508.21076}, 
}

📄 License

This dataset is released under the CC-BY-NC-4.0 license. See LICENSE file for details.

🤝 Contributing

We welcome contributions! Please see our GitHub repository for:

• Bug reports and feature requests
• Usage examples and tutorials
• Benchmark improvements and new baseline models
• Documentation enhancements