| ---
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| license: mit
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| ---
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|
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| # ProtCompass Embeddings
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| Pre-computed protein embeddings from 70+ encoders across 13 downstream tasks.
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| ## Dataset Structure
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| ```
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| embeddings/
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| ├── secondary_structure/ # CB513 dataset (29 GB)
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| ├── mutation_effect/ # ProteinGym DMS assays (4.5 GB)
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| ├── contact_prediction/ # ProteinNet (2.9 GB)
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| ├── stability/ # TAPE stability (1.6 GB)
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| ├── ppi_site/ # PPI site prediction (1.4 GB)
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| ├── fluorescence/ # GFP fluorescence (841 MB)
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| ├── metal_binding/ # Metal binding sites (570 MB)
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| ├── go_bp/ # GO Biological Process (214 MB)
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| ├── go_mf/ # GO Molecular Function (68 MB)
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| ├── remote_homology/ # SCOPe fold classification (20 MB)
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| ├── ec_classification/ # Enzyme classification (18 MB)
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| ├── membrane_soluble/ # Membrane/soluble (17 MB)
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| └── subcellular_localization/ # Subcellular location (17 MB)
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| ```
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|
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| ## File Format
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| Each encoder directory contains:
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| - `train_embeddings.npy`: Training set embeddings (N × D)
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| - `test_embeddings.npy`: Test set embeddings (M × D)
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| - `train_labels.npy`: Training labels
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| - `test_labels.npy`: Test labels
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| - `train_ids.txt`: Protein IDs for training set
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| - `test_ids.txt`: Protein IDs for test set
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| - `meta.json`: Metadata (encoder name, dimensions, dataset info)
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|
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| ## Usage
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|
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| ```python
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| import numpy as np
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| from huggingface_hub import hf_hub_download
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| # Download specific encoder embeddings
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| train_emb = np.load(hf_hub_download(
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| repo_id="Anonymoususer2223/ProtCompass_Embeddings",
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| filename="embeddings/mutation_effect/esm2/train_embeddings.npy",
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| repo_type="dataset"
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| ))
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| test_emb = np.load(hf_hub_download(
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| repo_id="Anonymoususer2223/ProtCompass_Embeddings",
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| filename="embeddings/mutation_effect/esm2/test_embeddings.npy",
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| repo_type="dataset"
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| ))
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| # Use for downstream tasks
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| from sklearn.linear_model import Ridge
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| model = Ridge()
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| model.fit(train_emb, train_labels)
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| score = model.score(test_emb, test_labels)
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| ```
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|
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| ## Encoders Included
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| ### Sequence Encoders (8)
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| ESM2 (650M, 150M, 35M), ESM1b, ESM3, ProtTrans, ProST-T5, ProteinBERT-BFD, Ankh
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|
|
| ### Structure Encoders (50+)
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| GearNet, GCPNet, EGNN, GVP, IPA, TFN, SchNet, DimeNet, MACE, CDConv, ProteinMPNN, PottsMP, dMaSIF
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|
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| ### Multimodal Encoders (5)
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| SaProt, ESM-IF, FoldVision
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|
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| ### Baselines
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| Random, Length, Torsion, One-hot, BLOSUM
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|
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| ## Dataset Statistics
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| - **Total size**: 41 GB
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| - **Total encoders**: 70+
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| - **Total tasks**: 13
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| - **Total proteins**: ~500K across all tasks
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|
|
| ## Citation
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| If you use these embeddings, please cite:
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|
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| ```bibtex
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| @article{protcompass2026,
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| title={ProtCompass: Systematic Evaluation of Protein Structure Encoders},
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| author={Your Name et al.},
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| journal={NeurIPS},
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| year={2026}
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| }
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| ```
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|
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| ## License
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| MIT License
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|
|
| ## Contact
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| For questions or issues, please open an issue on the repository.
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|
