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Updated: May 30, 2025

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TPepRet:一种深度学习模型,用于表征T细胞受体-抗原结合模式

Meng Wang¹, Wei Fan², Tianrui Wu¹

¹School of Computer Science and Engineering, Central South University, Changsha 410083, China.

Bioinformatics (Oxford, England)

|January 29, 2025

概括

通过集成后续挖掘和语义分析,TPepRet准确地解读T细胞受体 (TCR) 和结合关系. 这种新型模型的性能优于现有的工具,推进了癌症免疫疗法和疫苗设计.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

T细胞受体 (TCRs) 对于适应性免疫至关重要,识别癌症免疫疗法,疫苗设计和自身免疫性疾病管理的.
准确地描述TCR-结合是必不可少的,但由于序列数据中被忽视的定向语义,在计算上具有挑战性.

研究的目的:

开发一种创新的计算模型TPepRet,能够准确地解读TCR和之间的语义绑定关系.
通过结合定向语义和高级序列分析来解决现有工具的局限性.

主要方法:

TPepRet将后续挖掘与语义集成结合在一起,使用双向门式反复单元 (BiGRU) 网络和大型语言模型框架.
该模型分析了双向序列依赖性和全球序列语义,以全面了解TCR-相互作用.

主要成果:

TPepRet在各种数据集和具有挑战性的场景中表现出卓越的性能,包括结偏好分析和T细胞克隆扩张特征.
该模型成功地在复杂的环境中识别了真结合物,评估了关键结合点,并与大规模表达数据进行了验证.
评估包括查SARS-CoV-2TCRs,突出显示TPepRet的广泛适用性和准确性.

结论:

TPepRet显著优于现有的计算工具来分析TCR-结合.
该模型为了解TCR-相互作用提供了一种强大的新方法,有可能在免疫治疗和疫苗开发中推进临床应用.

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