---
license: mit
pipeline_tag: feature-extraction
---

# AbAffinity

This repository contains the model presented in the paper [AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2](https://huggingface.co/papers/2603.04480).

**GitHub Repository**: [ucrbioinfo/AbAffinity](https://github.com/ucrbioinfo/AbAffinity)

## **Overview**

`AbAffinity` is a Large Language Model designed to predict the binding affinity of scFv antibody sequences against the SARS-CoV-2 HR2 peptide. It takes the antibody heavy and light chain sequences as input and predicts the binding affinity against a peptide common to all SARS-CoV-2 variants.

## **Key Features**

- **Predict Binding Affinity**: Given the input antibody sequence, predict binding affinity.
- **Antibody Representation**: Provides both residue-level and sequence-level embeddings (representations) of the antibody.
- **Attention Contact Map**: Generates residue-residue attention maps for the input antibody sequence.

## **Installation**

You can install `AbAffinity` from Hugging Face: 

```bash
pip install git+https://huggingface.co/faisalashraf/abaffinity
```

You can also install it in a local folder:

```bash
git lfs install
git clone https://huggingface.co/faisalashraf/abaffinity
cd abaffinity 
pip install .
```

## **Usage**

Here's a quick example to get started:

```python
from abaffinity import AbAffinity

# Example usage
abmodel=AbAffinity() 


# The model takes complete scFv sequences as input. Heavy and Light chain are connected with a linker sequence. 
# Use make_scFv() method from the model to get the complete scFv sequence from heavy chain and light chain sequence.

heavy_seq = 'EVQLVESGAEVKKPGASVKVSCKASGYTFTSYGISWVRQAPGQGLEWMGWISAYNGNTNYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARVGRGVIDHWGQGTLVTVSS' 
light_seq = 'SSELTQDPAVSVALGQTVRITCEGDSLDYYYANWYQQKPGQAPILVIYGKNNRPSGIADRFSGSNSGDTSSLIITGAQAEDEADYYCSSRDSSGFEVTFGAGTKLTVL'

scFv_seq = abmodel.make_scFv(heavy_seq, light_seq) 
print(scFv_seq)  # Output: EVQLVESGAEVKKPGASVKVSCKASGYTFTSYGISWVRQAPGQGLEWMGWISAYNGNTNYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARVGRGVIDHWGQGTLVTVSSGGGGSGGGGSGGGGSSSELTQDPAVSVALGQTVRITCEGDSLDYYYANWYQQKPGQAPILVIYGKNNRPSGIADRFSGSNSGDTSSLIITGAQAEDEADYYCSSRDSSGFEVTFGAGTKLTVL

# Use `get_affinity()` method to get the predicted binding affinity of the antibody sequence. 
pred_affinity = abmodel.get_affinity(scFv_seq)
print(pred_affinity) # Output: tensor([3.1595]) 


# Use `get_embeddings()` method to get the embeddings for input sequences. 
# Use `mode='res'` to get residue wise embeddings, and `mode='seq'` will give sequence embedding. 

res_emb = abmodel.get_embeddings(scFv_seq, mode='res')
print(res_emb.shape)  # Output: torch.Size([258, 1280])
 
seq_emb = abmodel.get_embeddings(scFv_seq, mode='seq')
print(seq_emb.shape) # Output: torch.Size([1280]) 

# Use `get_contact_map()` method to get the contact maps of the given antibody sequence. 
# Use `mode='VH-VL'` if you want to plot the contacts for heavy chain and light chain separately, and `mode='scFv'` to plot single contacts for the entire scFv sequence. 

contacts = abmodel.get_contact_map(scFv_seq, mode = 'scFv')
print(contacts.shape) # Output: contact map figure, (240, 240)
```

## **License**

This project is licensed under the MIT License.

## **Acknowledgments**

If you find this work useful, please cite:

```
@article{ashraf2024large,
  title={A Large Language Model Guides the Affinity Maturation of Variant Antibodies Generated by Combinatorial Optimization},
  author={Ashraf, Faisal Bin and Zhang, Zihao and Paco, Karen and Mendivil, Mariana P and Lay, Jordan A and Ray, Animesh and Lonardi, Stefano},
  journal={bioRxiv},
  pages={2024--12},
  year={2024},
  publisher={Cold Spring Harbor Laboratory}
}

@article{ashraf2026abaffinity,
  title={AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2},
  author={Ashraf, Faisal Bin and Ray, Animesh and Lonardi, Stefano},
  journal={arXiv preprint arXiv:2603.04480},
  year={2026}
}
```