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

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.