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bioRxiv · 10.1101/2025.02.11.637677

ImmunoMatch learns and predicts cognate pairing of heavy and light immunoglobulin chains

Guo, D.·Dunn-Walters, D. K.·Fraternali, F.·Ng, J. C.
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Abstract

The development of stable antibodies formed by compatible heavy (H) and light (L) chain pairs is crucial in both the in vivo maturation of antibody-producing cells and the ex vivo designs of therapeutic antibodies. We present here a novel machine learning framework, ImmunoMatch, for deciphering the molecular rules governing the pairing of antibody chains. Fine-tuned on an antibody-specific language model, ImmunoMatch learns from paired H and L sequences from single human B cells to distinguish cognate H-L pairs and randomly paired sequences. We find that the predictive performance of ImmunoMatch can be augmented by training separate models on the two types of antibody L chains in humans,{kappa} and{lambda} , in line with the in vivo mechanism of B cell development in the bone marrow. Using ImmunoMatch, we illustrate that refinement of H-L chain pairing is a hallmark of B cell maturation in both healthy and disease conditions. We find further that ImmunoMatch is sensitive to sequence differences at the H-L interface. ImmunoMatch focusses on H-L chain pairing as a specific, under-explored problem in antibody developability, and facilitates the computational assessment and modelling of stably assembled immunoglobulins towards large-scale optimisation of efficacious antibody therapeutics.

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Guo, D., Dunn-Walters, D. K., Fraternali, F., Ng, J. C.. 2025-02-15. ImmunoMatch learns and predicts cognate pairing of heavy and light immunoglobulin chains. https://doi.org/10.1101/2025.02.11.637677

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