Addressing the antibody germline bias and its effect on language models for improved antibody design
Motivation: The versatile binding properties of antibodies have made them an extremely important class of biotherapeutics. However, therapeutic antibody development is a complex, expensive and time-consuming task, with the final antibody needing to not only have strong and specific binding, but also be minimally impacted by any developability issues. The success of transformer-based language models in protein sequence space and the availability of vast amounts of antibody sequences, has led to the development of many antibody-specific language models to help guide antibody discovery and design. Antibody diversity primarily arises from V(D)J recombination, mutations within the CDRs, and/or from a small number of mutations away from the germline outside the CDRs. Consequently, a significant portion of the variable domain of all natural antibody sequences remains germline. This affects the pre-training of antibody-specific language models, where this facet of the sequence data introduces a prevailing bias towards germline residues. This poses a challenge, as mutations away from the germline are often vital for generating specific and potent binding to a target, meaning that language models need be able to suggest key mutations away from germline.
Results: In this study, we explore the implications of the germline bias, examining its impact on both general-protein and antibody-specific language models. We develop and train a series of new antibody-specific language models optimised for predicting non-germline residues. We then compare our final model, AbLang-2, with current models and show how it suggests a diverse set of valid mutations with high cumulative probability. AbLang-2 is trained on both unpaired and paired data, and is freely available (https://github.com/oxpig/AbLang2.git).
Availability and implementation: AbLang2 is a python package available at https://github.com/oxpig/AbLang2.git.
AbLang is freely available and can be installed with pip.
pip install ablang2or directly from github.
pip install -U git+https://github.com/oxpig/AbLang2.gitNB: If you want to have your returned output aligned (i.e. use the argument "align=True"), you need to manually install Pandas and a version of ANARCI in the same environment. ANARCI can also be installed using bioconda; however, this version is maintained by a third party.
conda install -c bioconda anarciAbLang2 can be used in different ways and for a variety of usecases. The central building blocks are the tokenizer, AbRep, and AbLang.
- Tokenizer: Converts sequences and amino acids to tokens, and vice versa
- AbRep: Generates residue embeddings from tokens
- AbLang: Generates amino acid likelihoods from tokens
import ablang2
# Download and initialise the model
ablang = ablang2.pretrained(model_to_use='ablang2-paired', random_init=False, ncpu=1, device='cpu')
seq = [
'EVQLLESGGEVKKPGASVKVSCRASGYTFRNYGLTWVRQAPGQGLEWMGWISAYNGNTNYAQKFQGRVTLTTDTSTSTAYMELRSLRSDDTAVYFCARDVPGHGAAFMDVWGTGTTVTVSS', # The heavy chain (VH) needs to be the first element
'DIQLTQSPLSLPVTLGQPASISCRSSQSLEASDTNIYLSWFQQRPGQSPRRLIYKISNRDSGVPDRFSGSGSGTHFTLRISRVEADDVAVYYCMQGTHWPPAFGQGTKVDIK' # The light chain (VL) needs to be the second element
]
# Tokenize input sequences
seqs = [f"{seq[0]}|{seq[1]}"] # Input needs to be a list, with | used to separated the VH and VL
tokenized_seq = ablang.tokenizer(seqs, pad=True, w_extra_tkns=False, device="cpu")
# Generate rescodings
with torch.no_grad():
rescoding = ablang.AbRep(tokenized_seq).last_hidden_states
# Generate logits/likelihoods
with torch.no_grad():
likelihoods = ablang.AbLang(tokenized_seq)
We have build a wrapper for specific usecases which can be explored via a the following Jupyter notebook.
@article{Olsen2024,
title={Addressing the antibody germline bias and its effect on language models for improved antibody design},
author={Tobias H. Olsen, Iain H. Moal and Charlotte M. Deane},
journal={bioRxiv},
doi={https://doi.org/10.1101/2024.02.02.578678},
year={2024}
}