Datasets:

Anonymoususer2223
/

ProtEnv

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

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

Specific Tasks

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

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

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

Citation

If you use ProtEnv, please cite:

@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
Code Repository: GitHub
Paper: arXiv

License

MIT License - Free for academic and commercial use

Contact

For questions, issues, or data requests, please open an issue on the GitHub repository.