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README.md

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 ---
 license: mit
-language:
-- en
 ---
-This dataset provides the predicted three-dimensional structures corresponding to the Tasks Assessing Protein Embeddings (TAPE) benchmark. Specifically, it encompasses the structural data generated for the stability, fluorescence, and protein-protein interaction (PPI) affinity tasks.
 
-The reference for the TAPE benchmark is:
+# 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
-@inproceedings{tape2019,
-author = {Rao, Roshan and Bhattacharya, Nicholas and Thomas, Neil and Duan, Yan and Chen, Xi and Canny, John and Abbeel, Pieter and Song, Yun S},
-title = {Evaluating Protein Transfer Learning with TAPE},
-booktitle = {Advances in Neural Information Processing Systems}
-year = {2019}
+@article{protcompass2026,
+  title={ProtCompass: Interpretable Benchmarking and Task-Aware Evaluation of Protein Encoders},
+  author={Your Name et al.},
+  journal={NeurIPS},
+  year={2026}
 }
+```
 
-@article{sarkisyan2016,
-  title={Local fitness landscape of the green fluorescent protein},
-  author={Sarkisyan, Karen S and Bolotin, Dmitry A and Meer, Margarita V and Usmanova, Dinara R and Mishin, Alexander S and Sharonov, George V and Ivankov, Dmitry N and Bozhanova, Nina G and Baranov, Mikhail S and Soylemez, Onuralp and others},
-  journal={Nature},
-  volume={533},
-  number={7603},
-  pages={397},
-  year={2016},
-  publisher={Nature Publishing Group}
-}
+## Related Resources
 
-@article{proteinnet,
-  title={ProteinNet: a standardized data set for machine learning of protein structure},
-  author={AlQuraishi, Mohammed},
-  journal={arXiv preprint arXiv:1902.00249},
-  year={2019}
-}
+- **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)
 
-@article{rocklin2017,
-  title={Global analysis of protein folding using massively parallel design, synthesis, and testing},
-  author={Rocklin, Gabriel J and Chidyausiku, Tamuka M and Goreshnik, Inna and Ford, Alex and Houliston, Scott and Lemak, Alexander and Carter, Lauren and Ravichandran, Rashmi and Mulligan, Vikram K and Chevalier, Aaron and others},
-  journal={Science},
-  volume={357},
-  number={6347},
-  pages={168--175},
-  year={2017},
-  publisher={American Association for the Advancement of Science}
-}
+## 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).

fluroscence/fluroscence.tar.gz

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stability/esmfold_predictions.log

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-Starting predictions for training set...
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-Using 2 GPUs
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-/home/tianyu/miniconda3/envs/esmfold/lib/python3.9/site-packages/lightning_fabric/__init__.py:29: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
-  __import__("pkg_resources").declare_namespace(__name__)
-/home/tianyu/miniconda3/envs/esmfold/lib/python3.9/site-packages/lightning_fabric/__init__.py:29: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
-  __import__("pkg_resources").declare_namespace(__name__)
-/home/tianyu/miniconda3/envs/esmfold/lib/python3.9/site-packages/openfold/utils/tensor_utils.py:92: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:306.)
-  return data[ranges]
-/home/tianyu/miniconda3/envs/esmfold/lib/python3.9/site-packages/openfold/utils/tensor_utils.py:92: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:306.)
-  return data[ranges]
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-Total sequences: 12851
-Using 2 GPUs
-~6425 sequences per GPU
-/home/tianyu/miniconda3/envs/esmfold/lib/python3.9/site-packages/lightning_fabric/__init__.py:29: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
-  __import__("pkg_resources").declare_namespace(__name__)
-/home/tianyu/miniconda3/envs/esmfold/lib/python3.9/site-packages/lightning_fabric/__init__.py:29: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
-  __import__("pkg_resources").declare_namespace(__name__)
-/home/tianyu/miniconda3/envs/esmfold/lib/python3.9/site-packages/openfold/utils/tensor_utils.py:92: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:306.)
-  return data[ranges]
-/home/tianyu/miniconda3/envs/esmfold/lib/python3.9/site-packages/openfold/utils/tensor_utils.py:92: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:306.)
-  return data[ranges]
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