3D旋转等价神经网络对于预测蛋白质-连接物结合 afinities 的相关性
Gaili Li1, Yongna Yuan2, Ruisheng Zhang3
1School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China.
Interdisciplinary sciences, computational life sciences
|August 14, 2025
概括
我们开发了PLAe,这是一个新的神经网络模型,用于预测蛋白质-连接体结合亲缘关系. PLAe准确地捕捉了分子旋转对称性和原子间相互作用,以提高预测性能.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物信息学 结构生物信息学
- 机器学习在药物发现中的作用
背景情况:
- 蛋白质对于生物功能至关重要,它们的相互作用是由连接体结合动态调节的.
- 了解蛋白质 - 配体相互作用对于药物发现和开发至关重要.
- 准确预测结合亲和力仍然是计算化学的一个挑战.
研究的目的:
- 介绍PLAe (三维 (3D) 旋转等价神经网络),一种用于预测蛋白质-连接体结合亲缘关系的新方法.
- 为了利用旋转等差和分子对称性来提高预测准确性.
- 建立一个新的基准来预测蛋白质 - 连接体结合亲缘关系.
主要方法:
- 为原子间距离和e3nn网络利用球体波对角特征进行协同作用的辐射基函数 (RBF).
- 采用克莱布什-戈登系数来整合角和原子属性.
- 结合注意力机制来完善亲和预测.
主要成果:
- PLAe模型有效地捕捉了分子旋转对称性和原子间相互作用.
- 整合RBFs,e3nn和Clebsch-Gordan系数提高了模型处理复杂分子细节的能力.
- 注意力机制进一步提高了结合亲和力预测的精度.
结论:
- PLAe提供了一种复杂而整合性的方法来预测蛋白质-连接体结合亲缘关系.
- 该方法通过准确地利用详细的分子特征来设定新的基准.
- 这种模式有可能显著推进药物发现和个性化医疗.
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