Systematic HLA Epitope Ranking Pan Algorithm (SHERPA)

To generate high quality training data for our MHC binding and presentation prediction models, we stably transfected alleles into HLA-null, K562 to create mono-allelic cell lines. Then, we performed immunoprecipitation using W6/32 antibody and gently eluted the peptides. Finally, they were analyzed using LC-MSMS. To validate the performance of our prediction algorithms, we also processed 12 lung and colorectal tumor tissues with the same protocol.

为构建我们的主要组织相容性复合体（Major Histocompatibility Complex, MHC）结合与抗原呈递预测模型所需的高质量训练数据集，我们将等位基因稳定转染至人类白细胞抗原（Human Leukocyte Antigen, HLA）缺失型K562细胞中，成功构建单等位基因细胞系。随后，我们使用W6/32抗体开展免疫沉淀实验，并温和洗脱得到肽段。最终通过液相色谱-串联质谱（Liquid Chromatography-Tandem Mass Spectrometry, LC-MSMS）对肽段进行分析。为验证本研究预测算法的性能，我们还采用相同实验流程处理了12例肺与结直肠肿瘤组织。