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
- Reference: Wang, G., & Dunbrack, R. L. Jr. (2003). Bioinformatics 19(12), 1589–1591.
Citation
@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
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