从序列到响应:人工智能引导的核酸纳米颗粒的预测 免疫识别
M Brittany Johnson1, Sankalp Jain2, Jessica McMillan Shea3
1Department of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC, 28223, USA.
Small (Weinheim an der Bergstrasse, Germany)
|October 28, 2025
概括
一个新的深度学习框架预测核酸纳米粒子 (NANP) 序列的免疫反应. 这加快了用于药物输送的NANPs的开发速度,因为它使得快速的in silico免疫分析成为可能.
科学领域:
- 生物技术是生物技术.
- 纳米医学是一种纳米医学.
- 计算生物学 计算生物学
背景情况:
- 核酸纳米粒子 (NANPs) 对药物输送具有前景,但需要对其免疫反应进行快速评估以进行临床转化.
- 根据NANPs的序列预测NANPs的免疫刺激作用对于高效的设计和开发至关重要.
研究的目的:
- 开发一种深度学习框架,仅基于其序列来预测人类微质细胞中NANPs诱导的细胞因子反应 (IFN-β和IL-6).
- 使用基于注意力的神经网络,为NANPs推进基于序列的定量结构-活动关系 (QSAR) 建模.
主要方法:
- 采用了基于变压器的深度学习架构,在176个不同的NANP上进行了训练,并进行了实验性表征.
- 使用系统性链换来增强训练数据.
- 该模型预测了干扰素-β (IFN-β) 和互白素-6 (IL-6) 的产生.
主要成果:
- 该模型在交叉验证中获得了高预测性能 (R2 = 0.96-0.97,RMSE ≤ 0.01).
- 在外部测试组中证明了强大的通用性 (IFN-β的R2 = 0.91;IL-6的0.85).
- 该框架消除了手动特征工程的需要,同时保持了生物解释性.
结论:
- 开发的深度学习框架能够快速,基于NANP诱导的免疫反应的序列预测.
- 这种方法有助于合理设计和优化治疗应用的NANPs.
- 更新后的AI-cell网络平台为社区提供了NANPs的in silico免疫分析.
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