metadata
viewer: true
license: cc-by-4.0
split:
path: >-
curated_csv/subsets/cullpdb_pc15.0_res0.0-1.0_len40-10000_R0.2_Xray_d2026_01_26_chains281.csv
task_categories:
- other
tags:
- biology
- protein
- structure
- PDB
- PISCES
- CullPDB
- sequence
- curation
language: en
size_categories:
- n>1M
PISCES-CulledPDB
Curated protein chain tables from PISCES/CullPDB: one row per chain with sequence and metadata.
Dataset: PRMegathon26/PISCES-CulledPDB
Dataset viewer shows a single subset (debug mode).
Summary
| Item | Description |
|---|---|
| Main CSV | curated_csv/cullpdb_combined_chains.csv — N/A chains |
| Subsets | curated_csv/subsets/*.csv — 242 files (same columns) |
| Index | curated_csv/cullpdb_list_fasta_index.csv — 242 rows |
Full list of 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 (per structure) |
| rfac | R-factor |
| freerfac | Free R-factor |
| pc | Sequence identity cutoff % used for this subset |
| no_breaks | Whether chain has no breaks (yes/no) |
| R | R-factor cutoff used for this subset |
| source_list | Subset list basename (identifies curation parameters) |
Index CSV columns
| Column | Description |
|---|---|
| list_basename | Subset list basename |
| fasta_basename | Corresponding FASTA basename |
| list_path | Full path to list file |
| fasta_path | Full path to FASTA file |
| n_chains | Number of chains in this subset |
| pc | Sequence identity cutoff % |
| resolution | Resolution range (e.g. 0.0-2.0) |
| no_breaks | yes/no |
| R | R-factor cutoff |
| Nmethods | Experiment types (e.g. Xray, Xray+EM) |
Usage
Python
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)
Filter (filter_chains_csv.py)
From repo root after clone/download:
python src/filter_chains_csv.py --input curated_csv/cullpdb_combined_chains.csv --output my_filtered.csv --pc 20 --resolution-max 2.0 --no-breaks yes --R 0.25
Options: --pc, --pc-min, --pc-max, --resolution-min, --resolution-max, --no-breaks, --R, --method, --len-min, --len-max.
License
cc-by-4.0