AI Article Synopsis

  • Early evaluation of antibody off-target binding is crucial to reduce risks like quick clearance and toxicity, with the baculovirus particle (BVP) binding assay commonly used for this purpose.
  • This study introduces a new set of three deep learning models that utilize protein language models to predict outcomes in BVP assays and a complementary bovine serum albumin binding assay.
  • The models effectively predicted assay results for various antibody types, marking a significant advancement in computational methods for antibody development.

Article Abstract

Background: Early assessment of antibody off-target binding is essential for mitigating developability risks such as fast clearance, reduced efficacy, toxicity, and immunogenicity. The baculovirus particle (BVP) binding assay has been widely utilized to evaluate polyreactivity of antibodies. As a complementary approach, computational prediction of polyreactivity is desirable for counter-screening antibodies from discovery campaigns. However, there is a lack of such models.

Methods: Herein, we present the development of an ensemble of three deep learning models based on two pan-protein foundational protein language models (ESM2 and ProtT5) and an antibody-specific protein language model (PLM) (Antiberty). These models were trained in a transfer learning network to predict the outcomes in the BVP assay and the bovine serum albumin binding assay, which was developed as a complement to the BVP assay. The training was conducted on a large dataset of antibody sequences augmented with experimental conditions, which were collected through a highly efficient application system.

Results: The resulting models demonstrated robust performance on canonical mAbs (monospecific with heavy and light chain), bispecific Abs, and single-domain Fc (VHH-Fc). PLMs outperformed a model built using molecular descriptors calculated from AlphaFold 2 predicted structures. Embeddings from the antibody-specific and foundational PLMs resulted in similar performance.

Conclusion: To our knowledge, this represents the first application of PLMs to predict assay data on bispecifics and VHH-Fcs.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11259759PMC
http://dx.doi.org/10.1093/abt/tbae012DOI Listing

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