Categories
Nevin Manimala Statistics

Addressing the antibody germline bias and its effect on language models for improved antibody design

Bioinformatics. 2024 Oct 26:btae618. doi: 10.1093/bioinformatics/btae618. Online ahead of print.

ABSTRACT

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 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 design. Antibody diversity primarily arises from V(D)J recombination, mutations within the CDRs, and/or from a few non-germline mutations 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.

AVAILABILITY AND IMPLEMENTATION: AbLang-2 is trained on both unpaired and paired data, and is freely available at https://github.com/oxpig/AbLang2.git.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Journal Name online.

PMID:39460949 | DOI:10.1093/bioinformatics/btae618

By Nevin Manimala

Portfolio Website for Nevin Manimala