--- 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.dcd` — CALVADOS Cα trajectory (200 ns+) - `top.pdb` — topology file --- ## Folder structure ``` Kingdom.zip/ └── / ├── .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.*