解码器TCR:用于TCR-pMHC相互作用的组合预训练和引解码
bioRxiv : the preprint server for biology
|February 12, 2026
概括
我们开发了DecoderTCR,这是一个用于预测T细胞受体与-MHC复合体相互作用的计算框架. 该模型在预测绑定和识别方面表现强,即使数据有限.
科学领域:
- 计算免疫学计算免疫学
- 生物信息学是一种生物信息学.
- 机器学习用于免疫学.
背景情况:
- 建模T细胞受体 (TCR) 和-MHC (pMHC) 相互作用至关重要,但由于对联数据有限,具有挑战性.
- 不配对的TCR和pMHC序列数据丰富,为新的建模方法提供了机会.
研究的目的:
- 引入 DecoderTCR,这是一个面具语言模型框架,用于 TCR-pMHC 识别建模.
- 通过利用配对和不配对的序列数据来解决数据稀疏性.
- 改进TCR-pMHC结合和表位特异性识别的零射击预测能力.
主要方法:
- 在完善跨链依赖之前,使用边际数据实施了组成的持续预培训课程.
- 开发了一种以代透为导向的精制 (Iterative Entropy-Guided Refinement,IEGR),是一种非自行回归的解码算法,用于高效的上下文解析.
- 利用掩盖语言建模来从序列数据中学习表示.
主要成果:
- 实现了0.96 AUROC,用于零射击的pMHC结合预测.
- 在表位特异性TCR识别方面达到0.76 AUROC,接近监督基线性能,没有表位特异性培训.
- 学习的表示可以在没有协调监督的情况下恢复结构联系,并生成具有现实的重组统计数据的序列.
结论:
- 解码器TCR有效地模拟了TCR-pMHC相互作用,并通过稀疏的数据实现了高预测性能.
- 存在预测生成差距,表明虽然歧视很强,但可靠的序列生成仍然是一个公开的挑战.
- 该框架展示了掩面语言模型在计算免疫学中的潜力,以了解免疫识别.
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