README.md

metadata

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

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.