Antibody immunogenicity prediction and optimization with ImmunoSeq

Therapeutic antibody development faces persistent immunogenicity challenges from anti-drug antibodies (ADA). Identifying peptide fragments presented by major histocompatibility complex is the central challenge in predicting immunogenicity. Here, we presented ImmunoSeq, an interpretable and applicable method for immunogenicity prediction. ImmunoSeq addresses this by deploying complementary k-mer (k=8-12) peptide libraries: a positive library of immunologically safe peptides from fragmented human proteins/antibodies, and a negative library of murine antibody fragments capturing evolutionary-selected immunogenic triggers. For candidate antibodies, we generate all possible k-mer peptides and compute hit rate by summing positive hits (+1.0) and negative hits (-0.2 penalty) normalized against total peptide number. Higher hit rate predicts lower ADA risk, with residue-level resolution enabling precise localization of immunogenic hotspots. ImmunoSeq demonstrated superior ADA correlation and humanness classification accuracy compared to deep learning models, while accurately predicts ADA reductions in humanization, enabling sufficient sequence optimization for humanness. By leveraging dual-library discrimination principles of self/non-self-peptide, ImmunoSeq provides a robust, interpretable solution for immunogenicity prediction and sequence optimization.