---
viewer: true
license: cc-by-4.0
configs:
  - config_name: "train"
    data_files:
      - split: train
        path: cluster_assignments_train.csv
    default: true
  - config_name: "test"
    data_files:
      - split: test
        path: cluster_assignments_test.csv
  - config_name: "validate"
    data_files:
      - split: validate
        path: cluster_assignments_validate.csv
task_categories:
  - other
tags:
  - biology
  - protein
  - structure
  - PDB
  - PISCES
  - CullPDB
  - sequence
  - curation
language: en
size_categories:
  - "n>1M"
---
# PISCES-CulledPDB database as of January 2026
Recurated on Hugging Face on March 5th 2026

The **PISCES dataset** provides curated sets of protein sequences from the Protein Data Bank (PDB) based on sequence identity and structural quality criteria. PISCES yields **non-redundant subsets of protein chains** by applying filters such as sequence identity, experimental resolution, R-factor, chain length, and experimental method (e.g., X-ray, NMR, cryo-EM). The goal is to maximize structural reliability while minimizing sequence redundancy. Unlike culling tools that rely on BLAST or global alignments, PISCES uses **PSI-BLAST** for position-specific scoring matrices, improving detection of homologs below 40% sequence identity.

## Dataset sources

- **Server:** [PISCES](https://dunbrack.fccc.edu/pisces/)
- **Reference:** Wang, G., & Dunbrack, R. L. Jr. (2003). *Bioinformatics* 19(12), 1589–1591.

## Citation

```bibtex
@article{wang2003pisces,
  title={PISCES: a protein sequence culling server},
  author={Wang, Guoying and Dunbrack, Roland L. Jr.},
  journal={Bioinformatics},
  volume={19},
  number={12},
  pages={1589--1591},
  year={2003},
  publisher={Oxford University Press}
}
```

*Recurated for Hugging Face by Akshaya Narayanasamy akshayanarayanasamy[at]gmail.com.*

## Uses

- Non-redundant protein chain datasets for ML and statistical analysis  
- Benchmarking protein structure prediction or homology modeling  
- Studying evolutionary relationships at chosen sequence identity thresholds  
- High-quality training sets filtered by resolution and R-factor  
- Structure-based ML datasets for protein modeling  

## Dataset structure

| Item | Description |
|------|-------------|
| **Main CSV** | `curated_csv/cullpdb_combined_chains.csv` — full chain table |
| **Subsets** | `curated_csv/subsets/*.csv` — 242 files (same columns) |
| **Index** | `curated_csv/cullpdb_list_fasta_index.csv` |

Subset paths: `curated_csv/dataset_metadata.json` (keys `data_paths`, `subset_paths`).

### Columns (chain CSVs)

| Column | Description |
|--------|-------------|
| **pdb_chain** | PDB chain ID (e.g. 1ABC_A) |
| **pdb** | PDB ID (first 4 chars) |
| **chain** | Chain ID |
| **sequence** | Amino acid sequence (one-letter) |
| **len** | Sequence length |
| **method** | Experimental method (e.g. XRAY, NMR) |
| **resolution** | Resolution in Å |
| **rfac** | R-factor |
| **freerfac** | Free R-factor |
| **pc** | Sequence identity cutoff % for this subset |
| **no_breaks** | Whether chain has no breaks (yes/no) |
| **R** | R-factor cutoff for this subset |
| **source_list** | Subset list basename (curation parameters) |

## Usage

```python
from huggingface_hub import hf_hub_download
import pandas as pd

path = hf_hub_download(
    repo_id="RosettaCommons/PISCES-CulledPDB",
    filename="curated_csv/cullpdb_combined_chains.csv",
    repo_type="dataset"
)
df = pd.read_csv(path)
```

## File naming convention

Subset filenames follow:

`cullpdb_pc{pc}_res{res_min}-{res_max}[_noBrks]_len40-10000_R{R}_{methods}_d2026_01_26_chains{N}.csv`

| Parameter | Meaning |
|-----------|---------|
| **pc** | Percent sequence identity cutoff (15, 20, …, 95) |
| **res** | Resolution range in Å (e.g. 0.0-1.0, 0.0-2.5) |
| **noBrks** | Optional: exclude chains with breaks |
| **R** | R-factor cutoff (0.2, 0.25, 0.3, 1.0) |
| **methods** | Xray, Xray+EM, or Xray+Nmr+EM |
| **N** | Number of chains in the list |

## License

Apache-2.0