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Adv."],"published-print":{"date-parts":[[2026,5,8]]},"abstract":"<jats:p>Killer-cell immunoglobulin-like receptors (KIRs) are key determinants of natural killer cell function and are associated with the outcomes of infective, inflammatory, and neoplastic diseases. They form a polymorphic family of activating and inhibitory receptors that interact with polymorphic class I human leukocyte antigen (HLA-I) molecules. This interaction is dependent on the short peptides bound by the HLA-I molecules, including those derived from viruses and cancers. Identifying these peptides among the vast space of possible peptides based on the sequences of the interacting molecules can provide a valuable tool for developing personalized immunotherapy against infection and cancer. To address this challenge, we leveraged foundation protein language models and trained our model on available datasets for KIR-binding peptide-HLA complexes. Our tool generated excellent predictions with an area under receiver operator characteristic (AUROC) &gt;0.8 for the majority of inhibitory KIRs and performed well (AUROC &gt;0.7) for peptides generated during HIV and HCV infections. 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