GRATCR: Epitope-Specific T Cell Receptor Sequence Generation With Data-Efficient Pre-Trained Models

IF 6.8 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-02-10 DOI:10.1109/JBHI.2024.3514089
Zhenghong Zhou;Junwei Chen;Shenggeng Lin;Liang Hong;Dong-Qing Wei;Yi Xiong
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Abstract

T cell receptors (TCRs) play a crucial role in numerous immunotherapies targeting tumor cells. However, their acquisition and optimization present significant challenges, involving laborious and time-consuming wet lab experimental resource. Deep generative models have demonstrated remarkable capabilities in functional protein sequence generation, offering a promising solution for enhancing the acquisition of specific TCR sequences. Here, we propose GRATCR, a framework incorporates two pre-trained modules through a novel “grafting” strategy, to de-novo generate TCR sequences targeting specific epitopes. Experimental results demonstrate that TCRs generated by GRATCR exhibit higher specificity toward desired epitopes and are more biologically functional compared with the state-of-the-art model, by using significantly fewer training data. Additionally, the generated sequences display novelty compared to natural sequences, and the interpretability evaluation further confirmed that the model is capable of capturing important binding patterns.
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GRATCR:表位特异性T细胞受体序列生成与数据高效的预训练模型。
T细胞受体(TCRs)在许多针对肿瘤细胞的免疫治疗中起着至关重要的作用。然而,它们的获取和优化面临着巨大的挑战,涉及费力和耗时的湿实验室实验资源。深度生成模型在功能蛋白序列生成方面表现出卓越的能力,为增强特定TCR序列的获取提供了一个有希望的解决方案。在这里,我们提出了GRATCR,这是一个通过新颖的“嫁接”策略结合两个预训练模块的框架,以重新生成针对特定表位的TCR序列。实验结果表明,通过使用更少的训练数据,与最先进的模型相比,由GRATCR生成的tcr对所需的表位具有更高的特异性,并且具有更强的生物学功能。此外,生成的序列与自然序列相比具有新颖性,可解释性评估进一步证实了该模型能够捕获重要的绑定模式。
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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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