BCR-V-BERT

BCR-V-BERT: Antibody language model based on BERT fused V gene

License

Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) (see License file). This software is not to be used for commerical purposes.

Commercial users/for profit organisations can obtain a license from Cambridge Enterprise.

Overview

Official repository for BCR-V-BERT, an antibody-specific transformer language model pre-trained separately on 372M antibody heavy sequences and 3M antibody light sequences. The model can embed the CDR3 or CDR sequences of light and heavy chain antibodies.

Setup

Environment

To download our code, we recommend creating a clean conda environment with python v3.9, and you will also need to install PyTorch v2.0.0.

To use BCR-V-BERT, you can clone this repository and install the package locally:

$ git clone [email protected]/ZhangLabTJU/BCR-V_BERT/main
$ python setup.py install

Available models

Usage

BCR-V-BERT can be used in different ways and for a variety of usecases.

• Embeddings: Generates sequence embeddings. The output is a list of embedding tensors, where each tensor is the embedding for the corresponding sequence. Each embedding has dimension [(Length + 2) x 768].

from BCR_V_BERT import BCR_V_BERT_Runner

cdrh_model = BCR_V_BERT_Runner(model = 'cdrh')
cdrh3_model = BCR_V_BERT_Runner(model = 'cdrh3')

cdrh1_seq = 'GFTISDYW'
cdrh2_seq = 'ITPAGGYT'
cdrh3_seq = 'ARFVFFLPYAMDY'
vgene = 'IGHV1-52'

# Embeddings
cdrh_embeddings = cdrh_model.embed([cdrh1_seq+'|'+cdrh2_seq+'|'+cdrh3_seq],[vgene])
cdrh3_embeddings = cdrh3_model.embed([cdrh3_seq],[vgene])

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