The authors build a framework to make protein language model (ESM-2) representations interpretable by projecting them onto protein contact graphs — networks where nodes are amino acid residues and edges represent spatial proximity. They use SoftBlobGIN, a graph neural network with differentiable clustering, to group residues into functional substructures and perform structure-aware prediction. This yields both strong performance (92.8% accuracy on enzyme classification, AUROC 0.983 on binding-site detection) and directly auditable explanations: the model recovers known active-site residues and catalytic contact patterns without any supervision on those sites.
Main takeaways:
- Combines language model embeddings (ESM-2) with graph neural networks over protein contact graphs to get structure-aware predictions.
- Differentiable "blob" clustering automatically groups residues into functional substructures; blobs containing active sites show 1.85× higher importance scores.
- GNNExplainer recovers biologically meaningful active-site residues and catalytic patterns post hoc.
- Improves binding-site detection from 0.885 AUROC (ESM-2 alone) to 0.983, showing structural explanations aren't recoverable from language-model features alone.
- Plug-and-play design: no retraining of the language model, adds only ~1.1M parameters.