license: mit
tags:
- biology
- protein
- enzyme
- ec-number
- interpretability
BioBlobs
The BioBlobs model behind the M-CSA results of Unsupervised discovery of Functional Substructures (Xin Wang, Kaiwen Shi and Carlos Oliver, NeurIPS 2026). Paper · Code
The model predicts third-level Enzyme Commission (EC) classes, 242 in total, from frozen ESM2-T30 residue embeddings and the protein structure. It splits each protein into 8 spatially local blobs and quantizes their embeddings with residual vector quantization. The blobs it relies on most point to candidate catalytic sites, although training uses only protein-level EC labels.
Files
| File | Contents |
|---|---|
model.safetensors |
model weights (59 MB) |
config.yaml |
model configuration |
classes.json |
EC class names and their indices |
training_log.jsonl |
training log |
Use
git clone https://github.com/OliverLaboratory/BioBlobs.git
cd BioBlobs
uv sync && source .venv/bin/activate
hf download xwang112358/BioBlobs --local-dir bioblobs-mcsa-rvq-k8
python inference.py --model bioblobs-mcsa-rvq-k8 --pdb-dir my_pdbs --out results
For each protein, the output lists the top EC classes, the blobs ranked by importance score with
their residue numbers, and per-residue scores. The repository's docs/PREDICT.md describes the
options and outputs, and docs/REPRODUCE.md shows how to retrain the model and evaluate it on
M-CSA.
Training data
Swiss-Prot enzymes from the EC-Bench training set, with AlphaFold DB v6 structures. Training keeps proteins of up to 1,000 residues with a complete third-level EC number, split into training and validation sets at 30% sequence identity with MMseqs2. The GitHub repository ships the training and validation ids.
Limitations
- The model only knows the 242 EC classes in its training data.
- Class probabilities are softmax scores, not calibrated confidence.
- A blob that overlaps annotated catalytic residues is evidence for, not proof of, its role in catalysis.
Citation
@inproceedings{wang2026bioblobs,
title = {Unsupervised discovery of Functional Substructures},
author = {Wang, Xin and Shi, Kaiwen and Oliver, Carlos},
booktitle = {Advances in Neural Information Processing Systems},
year = {2026}
}
License
MIT