README.md

---
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",
)
```