标题： 蛋白质语言模型的持续领域特定预训练用于pMHC-I结合预测 显示英文标题

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

摘要： 由于极端的等位基因多样性（$\sim$30,000 HLA等位基因）、大多数等位基因的数据稀缺性以及噪声实验测量，预测肽-主要组织相容性复合体I类（pMHC-I）结合亲和力仍然具有挑战性。 当前方法在处理代表性不足的等位基因和定量结合预测方面尤其困难。 我们测试了蛋白质语言模型的领域特定持续预训练是否有助于其在pMHC-I结合亲和力预测中的应用。 从ESM Cambrian（300M参数）开始，我们在与HLA相关的肽（表位）上进行基于掩码语言建模（MLM）的持续预训练，测试两种输入格式：仅表位序列与表位序列与HLA重链序列拼接。 然后，我们仅使用高质量的定量数据对功能IC$_{50}$结合亲和力预测进行微调，避免现有方法继承的质量谱分析偏差。 显示英文摘要

摘要： 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.