ablang2 0.1.1

AbLang2: An antibody-specific language model focusing on NGL prediction.

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AbLang-2

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.

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Install AbLang2

AbLang is freely available and can be installed with pip.

    pip install ablang2

or directly from github.

    pip install -U git+https://github.com/oxpig/AbLang2.git

NB: 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 anarci

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AbLang2 usecases

AbLang2 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.

Citation

@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}
}