计算生物学
自身抗体
表位
生物
利用
范围(计算机科学)
自我容忍
免疫系统
翻译(生物学)
剧目
翻译后修饰
抗原
癌症
免疫学
自身免疫
计算机科学
神经科学
抗体
遗传学
生物化学
物理
计算机安全
酶
信使核糖核酸
基因
声学
程序设计语言
作者
Kristin J. Lastwika,Paul D. Lampe
标识
DOI:10.1016/j.copbio.2023.103056
摘要
Autoantibodies (AAb) are an immunological resource ripe for exploitation in cancer detection and treatment. Key to this translation is a better understanding of the self-epitope that AAb target in tumor tissue, but do not bind to in normal tissue. Posttranslational modifications (PTMs) on self-proteins are known to break tolerance in many autoimmune diseases and have also recently been described in cancer. This scope of possible autoantigens is quite broad and new high-dimensional and -throughput technologies to probe this repertoire will be necessary to fully exploit their potential. Here, we discuss the strengths and weaknesses of existing high-throughput platforms to detect AAb, review the current methods for characterizing immunogenic PTMs, describe the main challenges to identifying disease-relevant antigens and suggest the properties of future technologies that may be able to address these challenges. We conclude that exploiting the evolutionary power of the immune system to distinguish between self and nonself has great potential to be translated into antibody-based clinical applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI