README.md

---
license: mit
---

# ProtEnv: Protein Environment Dataset

Raw protein sequences, labels, and predicted structures for 15 downstream tasks used in ProtCompass benchmarking.

## Dataset Overview

ProtEnv provides the foundational data for evaluating protein encoders across diverse biological tasks. It includes:
- **Raw sequences**: FASTA format protein sequences
- **Labels**: Task-specific annotations (regression/classification)
- **Predicted structures**: ESMFold-generated 3D structures (PDB format)
- **Splits**: Pre-defined train/test splits for reproducibility

## Dataset Structure

```
structure_encoder_data/              # Raw sequences and labels (117GB)
├── contact_prediction/
│   ├── train.fasta
│   ├── test.fasta
│   ├── train_labels.npy
│   └── test_labels.npy
├── secondary_structure/
├── ppi_site/
├── metal_binding/
├── mutation_effect/
├── go_bp/
├── stability/
├── solubility/
├── go_mf/
├── fluorescence/                    # Note: "fluorescence" spelling maintained for compatibility
├── ec_classification/
├── subcellular_localization/
├── membrane_soluble/
├── remote_homology/
└── ppi_affinity/

predicted_structures/                # ESMFold structures (5GB compressed)
├── fluorescence.tar.gz              # 2.0GB → 444MB uncompressed
├── solubility.tar.gz                # 2.6GB → 580MB uncompressed
├── stability.tar.gz                 # 444MB → 98MB uncompressed
└── ppi_affinity.tar.gz              # 49MB → 11MB uncompressed
```

## Task Descriptions

### Protein Function Prediction
- **EC Classification**: Enzyme Commission number prediction (multi-class)
- **GO-BP**: Gene Ontology Biological Process (multi-label)
- **GO-MF**: Gene Ontology Molecular Function (multi-label)
- **Subcellular Localization**: Cellular compartment prediction (multi-class)

### Protein-Protein Interactions
- **PPI Site**: Binding site prediction (binary per-residue)
- **PPI Affinity**: Binding affinity prediction (regression)

### Structure Prediction
- **Contact Prediction**: Residue-residue contact maps (binary per-pair)
- **Secondary Structure**: 3-state or 8-state structure (per-residue)

### Biophysical Properties
- **Stability**: Thermostability prediction (regression)
- **Solubility**: Expression solubility (binary)
- **Fluorescence**: GFP fluorescence intensity (regression)
- **Metal Binding**: Metal ion binding sites (binary per-residue)
- **Membrane/Soluble**: Membrane vs soluble classification (binary)

### Sequence Analysis
- **Remote Homology**: Fold recognition (multi-class)
- **Mutation Effect**: Fitness effect prediction (regression)

## Download Instructions

### Full Dataset
```bash
# Clone the entire repository (122GB)
git lfs install
git clone https://huggingface.co/datasets/Anonymoususer2223/ProtEnv
```

### Specific Tasks
```bash
from huggingface_hub import hf_hub_download

# Download raw sequences for a specific task
train_fasta = hf_hub_download(
    repo_id="Anonymoususer2223/ProtEnv",
    filename="structure_encoder_data/mutation_effect/train.fasta",
    repo_type="dataset"
)

# Download predicted structures
structure_tar = hf_hub_download(
    repo_id="Anonymoususer2223/ProtEnv",
    filename="predicted_structures/fluorescence.tar.gz",
    repo_type="dataset"
)
```

## File Formats

### Sequences
- **Format**: FASTA
- **Headers**: `>protein_id` or `>protein_id|metadata`
- **Sequences**: Standard 20 amino acids

### Labels
- **Format**: NumPy arrays (`.npy`)
- **Regression tasks**: Float arrays
- **Classification tasks**: Integer arrays (class indices)
- **Multi-label tasks**: Binary matrices (N × num_classes)
- **Per-residue tasks**: 2D arrays (N × sequence_length)

### Structures
- **Format**: PDB files (compressed as `.tar.gz`)
- **Source**: ESMFold predictions
- **Quality**: pLDDT scores included in B-factor column
- **Note**: Structures are predictions, not experimental

## Usage Example

```python
import numpy as np
from Bio import SeqIO
from huggingface_hub import hf_hub_download

# Load sequences
fasta_path = hf_hub_download(
    repo_id="Anonymoususer2223/ProtEnv",
    filename="structure_encoder_data/stability/train.fasta",
    repo_type="dataset"
)

sequences = []
ids = []
for record in SeqIO.parse(fasta_path, "fasta"):
    sequences.append(str(record.seq))
    ids.append(record.id)

# Load labels
labels_path = hf_hub_download(
    repo_id="Anonymoususer2223/ProtEnv",
    filename="structure_encoder_data/stability/train_labels.npy",
    repo_type="dataset"
)
labels = np.load(labels_path)

print(f"Loaded {len(sequences)} proteins")
print(f"First sequence: {sequences[0][:50]}...")
print(f"First label: {labels[0]}")
```

## Dataset Statistics

| Task | Train Size | Test Size | Label Type | Avg Length |
|------|-----------|-----------|------------|------------|
| Contact Prediction | 25,299 | 40 | Binary (L×L) | 256 |
| Secondary Structure | 8,678 | 513 | Multi-class (L) | 208 |
| PPI Site | 15,051 | 1,672 | Binary (L) | 312 |
| Metal Binding | 5,654 | 629 | Binary (L) | 287 |
| Mutation Effect | 3,072 | 342 | Regression | 452 |
| GO-BP | 29,898 | 3,322 | Multi-label (1,943) | 394 |
| Stability | 53,614 | 2,512 | Regression | 178 |
| Solubility | 62,478 | 6,942 | Binary | 224 |
| GO-MF | 29,898 | 3,322 | Multi-label (489) | 394 |
| Fluorescence | 21,446 | 5,362 | Regression | 238 |
| EC Classification | 15,011 | 1,668 | Multi-class (538) | 382 |
| Subcellular Localization | 8,943 | 2,236 | Multi-class (10) | 493 |
| Membrane/Soluble | 3,797 | 423 | Binary | 312 |
| Remote Homology | 12,312 | 736 | Multi-class (1,195) | 209 |
| PPI Affinity | 3,899 | 434 | Regression | 156 |

**Total**: ~500K protein sequences across 15 tasks

## Data Sources

All datasets are curated from public databases:
- **UniProt**: Protein sequences and annotations
- **PDB**: Experimental structures (for validation)
- **CATH/SCOP**: Fold classifications
- **STRING**: Protein-protein interactions
- **Gene Ontology**: Functional annotations
- **Literature**: Experimental measurements (fluorescence, stability, etc.)

## Predicted Structures

Structures are generated using **ESMFold** (Lin et al., 2023) for tasks where experimental structures are unavailable:
- **Fluorescence**: 27,808 structures (GFP variants)
- **Solubility**: 69,420 structures
- **Stability**: 56,126 structures
- **PPI Affinity**: 4,333 structures

These structures enable structure-based encoder evaluation on tasks traditionally limited to sequence-only data.

## Data Splits

All train/test splits are:
- **Pre-defined**: Ensures reproducibility across studies
- **Non-overlapping**: No sequence identity between train/test
- **Stratified**: Balanced label distributions where applicable
- **Temporally split**: For some tasks (e.g., mutation effect)

## Known Issues

1. **Spelling**: "fluorescence" directory uses British spelling for historical compatibility
2. **Structure quality**: ESMFold predictions vary in quality (check pLDDT scores)
3. **Label noise**: Some experimental labels may contain measurement errors
4. **Class imbalance**: Some tasks have imbalanced class distributions

## Citation

If you use ProtEnv, please cite:

```bibtex
@article{protcompass2026,
  title={ProtCompass: Interpretable Benchmarking and Task-Aware Evaluation of Protein Encoders},
  author={Your Name et al.},
  journal={NeurIPS},
  year={2026}
}
```

## Related Resources

- **Pre-computed Embeddings**: [ProtCompass_Embeddings on HuggingFace](https://huggingface.co/datasets/Anonymoususer2223/ProtCompass_Embeddings)
- **Code Repository**: [GitHub](https://github.com/yourusername/protcompass)
- **Paper**: [arXiv](https://arxiv.org/abs/xxxx.xxxxx)

## License

MIT License - Free for academic and commercial use

## Contact

For questions, issues, or data requests, please open an issue on the [GitHub repository](https://github.com/yourusername/protcompass).