School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China.
Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China.
Brief Bioinform. 2023 May 19;24(3). doi: 10.1093/bib/bbad116.
Major histocompatibility complex (MHC) class II molecules play a pivotal role in antigen presentation and CD4+ T cell response. Accurate prediction of the immunogenicity of MHC class II-associated antigens is critical for vaccine design and cancer immunotherapies. However, current computational methods are limited by insufficient training data and algorithmic constraints, and the rules that govern which peptides are truly recognized by existing T cell receptors remain poorly understood. Here, we build a transfer learning-based, long short-term memory model named 'TLimmuno2' to predict whether epitope-MHC class II complex can elicit T cell response. Through leveraging binding affinity data, TLimmuno2 shows superior performance compared with existing models on independent validation datasets. TLimmuno2 can find real immunogenic neoantigen in real-world cancer immunotherapy data. The identification of significant MHC class II neoantigen-mediated immunoediting signal in the cancer genome atlas pan-cancer dataset further suggests the robustness of TLimmuno2 in identifying really immunogenic neoantigens that are undergoing negative selection during cancer evolution. Overall, TLimmuno2 is a powerful tool for the immunogenicity prediction of MHC class II presented epitopes and could promote the development of personalized immunotherapies.
主要组织相容性复合体（MHC）Ⅱ类分子在抗原呈递和 CD4+T 细胞反应中发挥关键作用。准确预测 MHCⅡ类相关抗原的免疫原性对于疫苗设计和癌症免疫疗法至关重要。然而，目前的计算方法受到训练数据不足和算法限制的限制，并且对于哪些肽真正被现有的 T 细胞受体识别的规则仍然知之甚少。在这里，我们构建了一个基于迁移学习的长短期记忆模型，名为“TLimmuno2”，用于预测表位-MHCⅡ类复合物是否能引发 T 细胞反应。通过利用结合亲和力数据，TLimmuno2 在独立验证数据集上的表现优于现有模型。TLimmuno2 可以在真实的癌症免疫治疗数据中找到真正的免疫原性新抗原。在癌症基因组图谱泛癌数据集上发现 MHCⅡ类新抗原介导的免疫编辑信号具有统计学意义，进一步表明 TLimmuno2 在识别真正免疫原性新抗原方面具有稳健性，这些新抗原在癌症进化过程中正在经历负选择。总的来说，TLimmuno2 是一种用于预测 MHCⅡ类呈递表位免疫原性的强大工具，并可以促进个性化免疫疗法的发展。
