Antigen-Aware Antibody Design (A3D)

Summary

This repo contains code for training a T5 transformer on a SAbDab dataset to generate Fv-fragment of antibody given the linear epitope sequence of the target antigen.

Epitope is represented as amino acid sequence of linear epitope segment (includes most of the contacts residues)

Model architecture: vanilla transformer encoder-decoder mapping linear epitope sequence to VH+VL sequence:

Model performance is measured by aligning the generated sequence to the native antibody from antibody-antigen complex and calculating the percentage of matched amino acids in different regions: FRs, CDRs, paratope

Example:

                                     ..X.XX.* *              * ......
Reference: -VQLQQSGAELVK-PGASVKLSCTASGFNIKDTYMYWVKQRPEQGLEWIGRIDPANGDT...
            |||.||||| || ||||||.||.|||......|||||.|.|.|||||.|||.||||||
Generated: QVQLVQSGAE-VKKPGASVKVSCKASGYTFTSYYMYWVRQAPGQGLEWMGRIGPANGDT...
FR Score: 0.7755, CDR Score: 0.6818, Paratope Score: 0.7273

Notation:
  `.` - CDR
  `X` - Contact in CDR
  `*` - Contact outside CDR

Table below summarizes amino acid reconstruction accuracy across the validation set for CDRs and paratope regions for VH and VL:

	CDR	Paratope
VH	44.9	46.0
VL	50.8	43.9

Usage

Create conda environment

conda env create -f environment.yaml
conda activate a3d

Inference

Refer to inference.ipynb on how to run inference with a trained model.

Training

1. Obtain and preprocess SAbDab dataset

Visit SAbDab website

Download an archive of all structures to data/ directory and extract it. You will also need summary tsv file, place it in data/ as well.

NB: of course you can place data elsewhere, but in this case you'll need to adjust the arguments of the preprocessing script

Run preprocessing:

python scripts/process_sabdab.py

2. Run training

Training arguments are configured with Hydra, for details look into conf/train.yaml.

Run training script:

python scripts/train.py

Track training with Aim:

cd logs/a3d
aim up