3D-ΔΔG:基于蛋白质3D结构的突变后蛋白质-蛋白质结合亲和力变化的双通道预测模型
Yuxiang Wang1, Yibo Zhu1, Xiumin Shi1
1School of Information and Electronics, Beijing Institute of Technology, Beijing, China.
Proteins
|May 15, 2025
概括
预测蛋白质-蛋白质结合亲和力 (ΔΔG) 的变化对于了解疾病至关重要. 一个新的深度学习模型,3D-ΔΔG,使用序列和结构数据准确地预测单个和多个突变的ΔΔG.
科学领域:
- 计算生物学 计算生物学
- 生物物理学的生物物理.
- 分子生物学分子生物学
背景情况:
- 蛋白质与蛋白质的相互作用是细胞功能和疾病机制的基础.
- 氨基酸突变可以改变蛋白质结合亲和力 (ΔΔG),影响生物过程.
- 目前的 ΔΔG 预测方法有局限性,特别是对于多点突变.
研究的目的:
- 开发一种先进的预测器,用于蛋白质-蛋白质结合亲和力 (ΔΔG) 的变化.
- 解决处理复杂,多点突变的现有方法的局限性.
- 整合蛋白质序列和3D结构信息,以改善 ΔΔG 预测.
主要方法:
- 开发了一个双通道深度学习模型,3D-ΔΔG.
- 使用预训练的蛋白质语言模型进行侧链序列编码.
- 采用图形注意力网络来表示蛋白质的结构.
- 通过双通道处理模块集成的序列和结构特征.
主要成果:
- 3D-ΔΔG在单点突变数据集上表现出明显的改进,而不是最先进的模型.
- 该模型在混合突变数据集 (SKEMPIv1,SKEMPIv2) 上显示出卓越的性能.
- 在预测和实验确定的ΔΔG值之间观察到很高的一致性.
结论:
- 3D-ΔΔG通过整合序列和结构数据,有效地捕获突变诱导的变化.
- 该模型显示出作为蛋白质设计和工程的有效预选工具的承诺.
- 这种方法可以在复杂的突变场景中预测蛋白质结合亲和力变化.
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