| --- |
| license: mit |
| tags: |
| - rna |
| - llps |
| - phase-separation |
| - rna-fm |
| - biology |
| library_name: pytorch |
| --- |
| |
| # RNAPhaseek |
|
|
| Predicts the probability that an RNA sequence undergoes **protein-free |
| liquid–liquid phase separation (LLPS)**, and powers a de-novo generator |
| for new LLPS-prone RNAs. |
|
|
| ## Try it (no install) |
|
|
| Open the Colab notebook from the project's GitHub repo for one-click |
| scoring and de-novo design. |
|
|
| ## What's in this repo |
|
|
| - `final_model.pt` — RNA-FM + FEGSTrans adapter + 38-dim biophysics + MLP head, 426 MB |
| - `norm_stats.npz` — biophysics feature mean/std (must accompany the checkpoint) |
|
|
| ## Architecture |
|
|
| Three streams fused in a single MLP head: |
|
|
| 1. **RNA-FM backbone** (`multimolecule/rnafm`, 640-dim, last 2 layers fine-tuned) |
| 2. **FEGSTrans adapter** that pools backbone embeddings with a structural FEGS bias |
| 3. **38 biophysical features** (MFE, GC%, G4-potential, self-complementarity, etc.) |
|
|
| Trained on a strict protein-free RNA-LLPS corpus (positives + negatives + |
| structural hard negatives) plus matched training pairs that teach the model |
| the free-vs-sequestered G-tract distinction — closing the structure-specificity |
| blind spot of earlier training recipes. |
|
|
| ## Headline numbers |
|
|
| | Metric | Value | |
| |---|---| |
| | 5-fold cluster-grouped CV AUROC | **0.88** | |
| | Structural-specificity AUROC | **0.90** | |
| | Non-yeast generalization AUROC | **0.80** | |
| | Matched-pair accuracy (held-out) | **1.00** | |
|
|
| ## Programmatic use |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| |
| model_path = hf_hub_download(repo_id="quercuscode/rnaphaseek", filename="final_model.pt") |
| norm_path = hf_hub_download(repo_id="quercuscode/rnaphaseek", filename="norm_stats.npz") |
| |
| # then load with the project code (see the GitHub repo): |
| from rnaphaseek import RNAPhaseekScorer, read_fasta |
| scorer = RNAPhaseekScorer(model_path=model_path, norm_path=norm_path) |
| probs = scorer.score(["GGGAGGGAGGGAGGGUUUUUUUUUUUUUUU"]) |
| print(probs) |
| ``` |
|
|
| ## Citation |
|
|
| If you use RNAPhaseek, please cite the accompanying manuscript (Cheraghali et al.). |
|
|
| ## License |
|
|
| MIT for the code; weights released for academic use under the same license. |
|
|
