getbetterhyccc/CAPSUL

CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization

📊 Dataset Specifications

This repository contains the complete dataset of the paper: "CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization" Accepted by ICLR 2026**.

Here we introduce the protein dataset used in our CAPSUL benchmark evaluation, with comprehensive 3D information and fine-grained localization annotations. Data is derived from AlphaFold2, UniProt, and the Human Protein Atlas (HPA).

📂 Data Structure

CAPSUL/
├── AlphaFold_pdb.tar      # Original AlphaFold2 PDB files
├── coordinate.tar         # Extracted C-alpha (Cα) coordinates
└── localization/          # Annotations and sequence features
    ├── union.csv               # 20-class annotations (UniProt + HPA), used for evaluation in the paper
    ├── uniprot_detail.csv      # Original raw annotations (UniProt)
    ├── hpa_detail.csv          # Original raw annotations (HPA)
    ├── uniprot_20.csv          # 20-class annotations (UniProt)
    └── hpa_20.csv              # 20-class annotations (HPA)

Note: The implementation code is available at https://github.com/getbetter-hyccc/CAPSUL

🔍 Detailed Component Breakdown

• —`AlphaFold_pdb.tar`: Contains the full original PDB files predicted by AlphaFold2. These include atomic coordinates, pLDDT scores, and complete protein metadata.
• —`coordinate.tar`: To facilitate faster training for geometric models, we provide pre-extracted C-alpha (Cα) coordinates. This reduces I/O overhead by focusing on the protein backbone trajectory.
• —`localization/`: Includes both the original raw labels and our integrated 20-class fine-grained labels. These annotations are specifically curated to evaluate model performance across diverse subcellular compartments, whose processing details can be found in our paper.

📜 Citation

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

@article{hu2026capsul,
  title={CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization},
  author={Hu, Yicheng and Lin, Xinyu and Li, Shulin and Wang, Wenjie and Zhu, Fengbin and Feng, Fuli},
  journal={arXiv preprint arXiv:2603.18571},
  year={2026}
}