分子表面互补性的紧评估增强了神经网络辅助的关键结合残留物的预测
Greta Grassmann1,2, Lorenzo Di Rienzo2, Giancarlo Ruocco2,3
1Department of Biochemical Sciences "Alessandro Rossi Fanelli", Sapienza University of Rome, P.Le A. Moro 5, Rome 00185, Italy.
Journal of chemical information and modeling
|February 21, 2025
概括
我们开发了CIRNet,一种新的神经网络,通过识别核心相互作用残留物来预测蛋白质-蛋白质相互作用. 这种方法提高了蛋白质对接的准确性和效率,这对于了解细胞过程和疾病至关重要.
科学领域:
- 计算生物学 计算生物学
- 生物物理学的生物物理.
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白质与蛋白质的相互作用对细胞功能至关重要,并与疾病有关.
- 这些相互作用的准确预测受到对接协议的性能挑战,这些协议依赖于识别正确的绑定区域.
- 开发计算高效的方法来预测蛋白质相互作用是必不可少的.
研究的目的:
- 通过紧的表面建模,提出一种用于识别蛋白质结合区域的综合协议.
- 引入一种新的神经网络架构,即核心交互残留网络 (CIRNet),用于预测核心交互残留.
- 通过过预测的姿势来增强蛋白质对接算法.
主要方法:
- 使用直角多项式来识别形状/静电互补性的蛋白质表面斑块的紧建模.
- 纳入水友性和疏水性贡献来定义新的约束矩阵.
- 核心交互残留网络 (CIRNet) 神经网络架构的开发和应用.
主要成果:
- 在平衡数据集上识别核心相互作用残留物时,CIRNet实现了约0.87的接收器运行特征曲线下的面积 (ROC AUC).
- 在盲目搜索中,CIRNet区分了与诱相互作用的核心残留物,其ROC AUC为0.72.
- 将CIRNet应用于来自对接服务器的顶级模型,通过将平均RMSD减少到原始状态,显著改善了对接结果.
结论:
- 开发的协议和CIRNet提供了一个计算效率高,准确的方法来预测蛋白质与蛋白质相互作用的地点.
- CIRNet提高了现有的蛋白质对接算法的性能,解决了计算生物学中的一个关键挑战.
- 该方法在识别和重新缩放由对接服务器生成的质量差的姿势方面表现出卓越的性能.
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