---
language: en
license: mit
library_name: pytorch
tags:
  - protein-protein-interaction
  - transformer
  - esm
  - classification
---

# T-FC Protein-Protein Interaction Predictor

## Model Description
T-FC is a sequence-based protein–protein interaction (PPI) predictor that uses a Transformer backbone with a last-layer MLP. It produces a probability score for whether two proteins interact. This is an ablation of [TUnA](https://huggingface.co/yk0/tuna-r-tuna)

## Intended Use
- Rapid inference for candidate PPI scoring.
- These weights can be loaded in from the [TUnA-R](github.com/young-su-ko/TUnA-R) repository on Github.

## Training Data
Trained on the Bernett dataset (https://academic.oup.com/bib/article/25/2/bbae076/7621029) composed of Human PPI interactions.

## Model Architecture
Backbone: `tuna.models._transformer.Transformer`
- `protein_dim`: 640
- `hid_dim`: 64
- `ff_dim`: 256
- `n_layers`: 1
- `n_heads`: 8
- `dropout`: 0.2
- `llgp`: False
- `use_spectral_norm`: False
- `out_targets`: 1


## Evaluation Results (Test Set)
- AUROC: 0.69
- MCC: 0.27
- Accuracy: 0.63
- AUPRC: 0.68
- Precision: 0.62

## Citation
If you use this model, please cite:
- TUnA: https://academic.oup.com/bib/article/25/5/bbae359/7720609
- Bernett dataset: https://academic.oup.com/bib/article/25/2/bbae076/7621029

## License
MIT