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arXiv · 2507.13077

Continued domain-specific pre-training of protein language models for pMHC-I binding prediction

Sergio E. Mares·Ariel Espinoza Weinberger·Nilah M. Ioannidis
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Abstract

Predicting peptide--major histocompatibility complex I (pMHC-I) binding affinity remains challenging due to extreme allelic diversity ($\sim$30,000 HLA alleles), severe data scarcity for most alleles, and noisy experimental measurements. Current methods particularly struggle with underrepresented alleles and quantitative binding prediction. We test whether domain-specific continued pre-training of protein language models is beneficial for their application to pMHC-I binding affinity prediction. Starting from ESM Cambrian (300M parameters), we perform masked-language modeling (MLM)-based continued pre-training on HLA-associated peptides (epitopes), testing two input formats: epitope sequences alone versus epitopes concatenated with HLA heavy chain sequences. We then fine-tune for functional IC$_{50}$ binding affinity prediction using only high-quality quantitative data, avoiding mass spectrometry biases that are inherited by existing methods.

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Sergio E. Mares, Ariel Espinoza Weinberger, Nilah M. Ioannidis. 2025-07-16. Continued domain-specific pre-training of protein language models for pMHC-I binding prediction. https://arxiv.org/abs/2507.13077

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