Datasets:
File size: 3,649 Bytes
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license: cc-by-4.0
tags:
- biology
- proteins
- intrinsically-disordered-proteins
- molecular-dynamics
- CALVADOS
- trajectories
- BENDER
- IDP
pretty_name: BENDER — Biological ENsembles of Disordered proteins across kingdoms
size_categories:
- 10K<n<100K
task_categories:
- other
language:
- en
---
# BENDER — Biological ENsembles of Disordered proteins across kingdoms
Raw CALVADOS coarse-grained molecular dynamics trajectories for
**11,533 intrinsically disordered proteins** spanning 13 kingdoms of life.
Each protein folder contains:
- `<uniprot_id>.dcd` — CALVADOS Cα trajectory (200 ns+)
- `top.pdb` — topology file
---
## Folder structure
```
Kingdom.zip/
└── <uniprot_id>/
├── <uniprot_id>.dcd
└── top.pdb
```
---
## Available zip files
| File | Kingdom | Sequences |
|---|---|---|
| `Bacteria.zip` | Bacteria | 2,850 |
| `Plants.zip` | Plants | 2,480 |
| `Fungi.zip` | Fungi | 1,507 |
| `Mammals.zip` | Mammals | 1,361 |
| `Parasites_Protists.zip` | Parasites / Protists | 1,049 |
| `Viruses.zip` | Viruses | 1,025 |
| `Other_Vertebrates.zip` | Other vertebrates | 711 |
| `Insects.zip` | Insects | 289 |
| `Nematodes.zip` | Nematodes | 95 |
| `Other_Invertebrates.zip` | Other invertebrates | 77 |
| `Archaea.zip` | Archaea | 76 |
| `Algae.zip` | Algae | 10 |
---
## Loading a trajectory
```python
from huggingface_hub import hf_hub_download
import mdtraj as md
import zipfile
# Download zipped kingdom
zip_path = hf_hub_download(
repo_id="taseef/BENDER",
filename="Bacteria.zip",
repo_type="dataset"
)
# Extract specific protein
with zipfile.ZipFile(zip_path, "r") as z:
z.extract("N0AZA6/N0AZA6.dcd", path="./trajectories")
z.extract("N0AZA6/top.pdb", path="./trajectories")
# Load trajectory
traj = md.load(
"./trajectories/N0AZA6/N0AZA6.dcd",
top="./trajectories/N0AZA6/top.pdb"
)
print(traj)
```
---
## Simulation protocol
| Parameter | Value |
|---|---|
| Force field | CALVADOS-2 (Cα coarse-grained, Tesei & Lindorff-Larsen 2023) |
| Ensemble | NVT, 300 K |
| Ionic strength | 0.15 M NaCl |
| Minimum length | 200 ns |
| Long sequences (>150 res) | Extended — length scaled to residues^1.5 |
| Equilibration | First 50 % of each trajectory discarded |
| Clustering | Global CD-HIT at 90 % sequence identity across all kingdoms |
| Scope | Pure complete IDPs only — no IDR fragments, no domain context |
| Convergence | 87.4 % of sequences exceed ν fit R² ≥ 0.99 |
---
## Prediction targets
10 ensemble-level targets per sequence:
**Geometric properties**
| Target | Description |
|---|---|
| `Rg` | Radius of gyration |
| `Re` | End-to-end distance |
| `nu` | Flory scaling exponent |
| `delta` | Asphericity |
| `A0` | Flory prefactor |
**Contact network properties**
| Target | Description |
|---|---|
| `global_efficiency` | Global network efficiency |
| `fragmentation_index` | Fragmentation index |
| `avg_clustering` | Average clustering coefficient |
| `transitivity` | Network transitivity |
| `degree_assortativity` | Degree assortativity |
---
## Citation
```bibtex
@misc{taseefr_2026,
author = { taseefr },
title = { BENDER (Revision 5a8bda5) },
year = 2026,
url = { https://huggingface.co/datasets/taseef/BENDER },
doi = { 10.57967/hf/8692 },
publisher = { Hugging Face }
}
```
---
## License
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — free to use for any purpose with attribution.
*BENDER: making IDPs go supersonic.*
|