使用多实例学习与对接结构来预测蛋白质 - 配体结合的亲和力
Hyojin Kim1, Heesung Shim2, Aditya Ranganath1
1Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, United States.
Frontiers in pharmacology
|January 20, 2025
概括
这项研究引入了一种新的深度学习方法,用于使用多个分子对接姿势预测蛋白质-连接体结合亲和力. 这种方法通过不需要共晶结构来增强药物发现,使其适用于更广泛的蛋白质标.
科学领域:
- 计算化学计算化学
- 结构生物学 结构生物学
- 机器学习 机器学习
背景情况:
- 深度学习模型在预测药物发现的蛋白质-连接体结合亲和力方面表现有前途.
- 目前的方法通常依赖于共同晶体结构,这些结构并不总是可用.
- 来自分子对接的不准确预测结构可以降低机器学习模型的准确性.
研究的目的:
- 开发一种新的基于结构的推理方法来预测结合亲和力.
- 为了克服在结合亲和力预测中需要共同晶体结构的局限性.
- 为了利用多个分子对接姿势来提高预测准确度.
主要方法:
- 开发了一种新的基于结构的推理方法.
- 该方法利用每个复合体的多个分子对接姿势.
- 使用多实例学习与注意网络来预测绑定亲和力.
主要成果:
- 拟议的方法使用PDBbind和SARS-CoV-2主要蛋白酶数据集进行了验证.
- 结合亲和力预测性能与最先进的方法相竞争.
- 该方法使用对接姿势而不是共同晶体结构证明了有效性.
结论:
- 开发的方法可以在不需要共晶结构的情况下进行结合亲和力预测.
- 这种方法显著扩大了绑定亲和力预测模型的适用性.
- 该方法为药物发现中的虚拟高通量查提供了有价值的工具.
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