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ContVAR

ContVAR trains a graph-based metric learning model for single amino acid variants (SAVs). It uses triplets from the same protein:

  • anchor: wild-type structure
  • positive: benign variant
  • negative: pathogenic variant

The model learns to pull benign variants closer to the anchor and push pathogenic variants farther away.

Installation

pip install -e .
pip install torch torch-geometric graphein wandb biopython h5py scikit-learn matplotlib tqdm pandas numpy networkx MDAnalysis

Expected data layout

Local defaults assume this repository layout:

ContVAR/
|- starter.py
|- embeddings_variable.h5 (not needed if graphs for dms dataset already exist)
|- local_splits/
|  |- dms_protein_split.json
|  |- phase0_protein_split_removed_graphless.json
|- protein_triplets_data/
|  |- originals/
|  |- positives/
|  |- negatives/
|  ` - processed/
|-
`- semantic_similarity/
   |- semantic_similarity_swissprot_filtered_low0.2_high0.8_mf.tsv
   |- semantic_similarity_swissprot_filtered_low0.2_high0.8_bp.tsv
   `- semantic_similarity_swissprot_filtered_low0.2_high0.8_cc.tsv
`- a directory of prebuilt GO `.pt` graphs


## Starter CLI

`starter.py` is the main entry point for local runs.
It centralizes the runtime paths in one place through the `STARTER_PATHS` block near the top of the file.

### 1. Review or edit the default paths

Open [starter.py](starter.py) and update `STARTER_PATHS` if you want machine-specific defaults in one place.

The main workflow is:

```bash
python starter.py

A normal run automatically:

  • saves the phase-0 checkpoints (models)
  • saves the final stage checkpoints (models)
  • exports the learned global ContVAR embeddings to H5
  • generates the t-SNE visualizations

2. Run a DMS-only training job

If STARTER_PATHS["go_prebuilt_graph_root"] is left as None, the starter script automatically disables GO phase-0 pretraining and runs encoder DMS training only.

python starter.py

3. Run full training with GO phase-0 enabled

Set STARTER_PATHS["go_prebuilt_graph_root"] in starter.py, then run:

python starter.py

Common CLI options

  • --force: rebuild processed protein graphs from scratch

Output files

By default, local runs write:

  • model_best_loss.pt
  • model_last.pt
  • exports/dms_variant_contvar_embeddings.h5
  • model_phase0_best_loss.pt when GO phase-0 is enabled
  • model_phase0_last.pt when GO phase-0 is enabled
  • exports/phase0_contvar_embeddings.h5 when go_prebuilt_graph_root is configured
  • visualizations/

Notes

  • GO phase-0 requires all of the following: STARTER_PATHS["go_prebuilt_graph_root"], the GO TSV directory, and the GO split JSON.
  • Encoder DMS training uses protein_triplets_data, embeddings_variable.h5, and the DMS split JSON.
  • The exported H5 files contain the learned global graph embedding, not the local mutation-position embedding.
  • The DMS export writes one embedding per variant file covered by local_splits/dms_protein_split.json across all families in the dataset.
  • The training loop now reads checkpoint paths from configuration, and starter.py is the intended single place to edit local file paths.