Sequence Modeling

The sequence module predicts NADPH responsiveness directly from protein sequences.

Backbone

• ESM-2 protein language model
• Pretrained on large protein sequence corpora

Training modes

• Pooled embeddings (fast)
• Residue representations (interpretability)
• Token-level training (full fine-tuning)

Architecture

• Attention pooling over residues
• Fully connected prediction head
• Supports classification and regression

Training features

• Focal loss for class imbalance
• Gradient accumulation for memory efficiency
• Mixed precision for speed

Labels

Derived from experimental data:

• Classification: strong vs weak responders
• Regression: transformed EC50 or NSS

Interpretation

The model learns sequence patterns associated with NADPH sensitivity, enabling prediction for unseen proteins.

This connects sequence information to biochemical behavior.