EpiMII:集成结构和图形神经网络用于MHC-II表皮和新抗原设计
bioRxiv : the preprint server for biology
|July 14, 2025
概括
新的人工智能模型EpiMII准确地设计了用于癌症免疫治疗的MHC-II表位. 这种方法增强T细胞激活,有望减少瘤大小,促进疫苗开发.
科学领域:
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- MHC-II新抗原对于免疫疗法至关重要,但准确的预测仍然具有挑战性.
- 现有的新抗原预测工具的准确性有限,导致体内免疫性低.
- 来自瘤DNA突变的功能性新抗原是罕见的.
研究的目的:
- 开发一个准确和高效的计算模型来设计MHC-II表征.
- 为了改善免疫新抗原的预测,用于癌症免疫疗法.
- 为了利用表位的结构特征进行序列预测.
主要方法:
- 开发EpiMII,用于MHC-II表位设计的图形神经网络模型.
- 为模型培训构建142,934个MHC-II表位结构的大数据集.
- 评估EpiMII的性能与现有工具 (如ProteinMPNN) 相比.
主要成果:
- 对于已知的MHC-II表位,EpiMII比ProteinMPNN提高了4.2倍,其序列恢复率为78.0%,为已知的MHC-II表位.
- 来自肝细胞癌的设计新抗原在体外激活了CD4+ T细胞,诱导IFN-γ和TNF-α的分泌.
- 一个设计的表位显著减少了小鼠的瘤体积,证明了体内疗效.
结论:
- EpiMII提供了一种新且高效的方法,用于识别和设计MHC-II表位/新抗原.
- 该模型预测功能表位体的能力对癌症疫苗开发具有重大潜力.
- 这种方法可以克服当前新抗原预测工具的局限性,提高免疫治疗结果.
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