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license: mit
tags:
- protein
- structural-biology
- thermodynamic-stability
---
# DeepEF Datasets
Protein structure datasets used for training and evaluating [DeepEF](https://github.com/shaharec/DeepEF) —
a deep learning framework for predicting protein thermodynamic stability.
## Contents
| Path | Size | Description |
|------|------|-------------|
| `casp12_data_30/` | ~110GB | CASP12 structures with ProtT5 embeddings; per-protein `.pt` tensors split into train/test/valid-* |
| `Processed_K50_dG_datasets/` | ~2.2GB | K50 ddG mutation datasets with AlphaFold PDB models |
| `megascale_proteins.csv` | small | Megascale protein list |
## Data Format (casp12_data_30)
Each protein is a folder under `train/`, `test/`, or `valid-*/`:
```
PROTEIN_ID/
├── crd_backbone.pt # Backbone coordinates [seq_len, 4, 3]
├── ang.pt # Dihedral angles
├── mask.pt # Valid residue mask
├── seq_one_hot.pt # One-hot amino acid encoding [seq_len, 20]
├── seq.pt # Raw sequence string
├── seq_mut.pt # Mutant sequence string
├── proT5_emb.pt # ProtT5 embeddings [seq_len, 1024]
├── proT5_emb_cycle1-4.pt # Cyclic permutation embeddings
└── proT5_emb_mut.pt # Mutant ProtT5 embeddings
```
## Usage
```python
from huggingface_hub import snapshot_download, hf_hub_download
import torch
# Download a single protein
path = hf_hub_download(
repo_id="shaharec/deepef-data",
repo_type="dataset",
filename="casp12_data_30/train/1A0C_1_A/crd_backbone.pt",
)
coords = torch.load(path)
# Download an entire split (large!)
snapshot_download(
repo_id="shaharec/deepef-data",
repo_type="dataset",
allow_patterns="casp12_data_30/train/*",
local_dir="./data",
)
```
|