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
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="PRMegathon26/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