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

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 |

📊 Per-sequence data

All 10 ensemble-level properties precomputed for every sequence are available as a single CSV — no need to download trajectories:

👉 bender_v2.csv

import pandas as pd

df = pd.read_csv("https://anonymous.4open.science/r/IDP-Project-17EA/bender_v2.csv")
print(df.shape)   # (11533, ...)
print(df.columns.tolist())

---

Citation

If you use BENDER in your work, please cite both the paper and the dataset.

📄 Paper

Velasquez, J. & Rahman, T. (2026). BENDER: A Cross-taxon IDP Simulation Database Reveals Conserved Sequence-Ensemble Laws Across the Tree of Life. bioRxiv. doi: 10.64898/2026.08.18.745604

@article{velasquez2026bender,
  title={BENDER: A Cross-taxon IDP Simulation Database Reveals Conserved Sequence-Ensemble Laws Across the Tree of Life},
  author={Velasquez, Juan and Rahman, Taseef},
  journal={bioRxiv},
  pages={2026--08},
  year={2026},
  publisher={Cold Spring Harbor Laboratory}
}

🤗 Dataset

@misc{taseefr_2026,
    author       = { Taseef Rahman },
    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 — free to use for any purpose with attribution. BENDER: making IDPs go supersonic.