README.md

---
license: mit
tags:
  - protein
  - binding-affinity
  - deep-learning
  - esm
  - pytorch
language:
  - en
---

# 🧬 Protein Binding Affinity Predictor

Dual-head model for predicting protein-protein binding affinity (ΔG) and mutation effects (ΔΔG).

## Model Performance

| Metric | Validation Score |
|--------|-----------------|
| dG Pearson | 0.51 |
| ddG Pearson | 0.70 |
| Sum PCC | 1.21 |

## Architecture

- **Backbone**: ESM-600M (frozen embeddings)
- **Pooling**: Sliced-Wasserstein Embedding (SWE)
- **Heads**: Dual-head (dG + ddG)
- **Input**: Protein sequences (1153-dim = 1152 ESM + 1 mutation channel)

## Usage

```python
from huggingface_hub import hf_hub_download
import torch

# Download checkpoint
ckpt = hf_hub_download(repo_id="supanthadey1/protein-binding-affinity", filename="best_model_checkpoint.pt")
checkpoint = torch.load(ckpt, map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
```

## Predictions

- **ΔG (kcal/mol)**: Binding free energy. More negative = stronger binding.
- **ΔΔG (kcal/mol)**: Mutation effect. Negative = stabilizing, Positive = destabilizing.

## Training Data

Trained on multiple datasets including SKEMPI, BindingGym, PDBbind, and others.

## Citation

```
If you are using Seq2Bind / TwinPeaks please cite both papers as:

Ma, X., Dey, S., Zelinski, C., Li, Q. and Chowdhury, R., 2025. Seq2Bind webserver for binding site prediction from sequences using fine-tuned protein language models. NAR Genomics and Bioinformatics, 7(4), p.lqaf154.

Dey, S. and Chowdhury, R., 2025. Twin Peaks: Dual-Head Architecture for Structure-Free Prediction of Protein-Protein Binding Affinity and Mutation Effects. arXiv preprint arXiv:2509.22950.
```