基于计算机模型：预测 HLA Ⅰ类限制新抗原的内在特征。

In Silico: Predicting Intrinsic Features of HLA Class-I Restricted Neoantigens.

机构信息

Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Centre for Biomedical Engineering, School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Haidian District, Beijing, People's Republic of China.

Department of Plant Genetics and Breeding, State Key Laboratory of Plant Physiology and Biochemistry & National Maize Improvement Center, China Agricultural University, Haidian District, Beijing, People's Republic of China.

出版信息

Methods Mol Biol. 2024;2809:245-261. doi: 10.1007/978-1-0716-3874-3_16.

DOI:10.1007/978-1-0716-3874-3_16

PMID:38907902

Abstract

Mutation-containing immunogenic peptides from tumor cells, also named as neoantigens, have various amino acid descriptors and physical-chemical properties characterized intrinsic features, which are useful in prioritizing the immunogenicity potentials of neoantigens and predicting patients' survival. Here, we describe a glioma neoantigen intrinsic feature database, GNIFdb, that hosts computationally predicted HLA-I restricted neoantigens of gliomas, their intrinsic features, and the tools for calculating intrinsic features and predicting overall survival of gliomas. We illustrate the application of GNIFdb in searching for possible neoantigen candidates from ATF6 that plays important roles in tumor growth and resistance to radiotherapy in glioblastoma. We also demonstrate the application of intrinsic feature associated tools in GNIFdb to predict the overall survival of primary IDH wild-type glioblastoma.

摘要

肿瘤细胞中的含突变免疫原性肽，也称为新抗原，具有多种氨基酸描述符和物理化学性质特征，这些特征有助于确定新抗原的免疫原性潜力，并预测患者的生存情况。在这里，我们描述了一个脑肿瘤新抗原内在特征数据库 GNIFdb，该数据库包含计算预测的脑肿瘤 HLA-I 限制新抗原、它们的内在特征以及计算内在特征和预测脑肿瘤总生存率的工具。我们举例说明了 GNIFdb 在从 ATF6 中搜索可能的新抗原候选物中的应用，ATF6 在肿瘤生长和胶质母细胞瘤对放疗的抵抗中发挥重要作用。我们还展示了 GNIFdb 中内在特征相关工具在预测原发性 IDH 野生型脑胶质瘤总生存率方面的应用。