通过分子动力学模拟轨迹的Jensen-Shannon分歧来预测蛋白质-连接物亲和力
Kodai Igarashi1, Masahito Ohue1
1Institute of Science Tokyo, Yokohama, Kanagawa 226-8501, Japan.
Biophysics and physicobiology
|September 3, 2025
概括
这项研究引入了一种更快的计算方法,通过使用詹森-香农分歧而不是深度学习来预测蛋白质-连接体结合性. 这种新方法减少了模拟时间,提高了药物发现的准确性.
科学领域:
- 计算化学
- 药物发现
- 分子建模
背景情况:
- 预测蛋白质与配体的结合性对于药物发现至关重要.
- 现有的方法如Yasuda等. (2022) 使用分子动力学 (MD),但在计算上昂贵.
- 缺乏实验数据可能导致对应征的误解.
研究的目的:
- 开发一个计算效率高的 Yasuda 等的替代方案. 这是一种结合性亲和度预测方法.
- 降低与MD模拟和深度学习相关的计算成本.
- 提高结合性亲和预测的可靠性,特别是没有实验数据.
主要方法:
- 用Jensen-Shannon (JS) 差异取代基于深度学习的相似性估计.
- 将分子动力学 (MD) 模拟时间缩短了一半.
- 使用AutoDock Vina进行粗结合自由能量 (ΔG) 估计,以预测相关性信号.
主要成果:
- 通过消除深度学习, 显著减少计算时间.
- 通过减半生产运行模拟时间实现了可比的准确性.
- 通过AutoDock Vina成功提出了一种预测相关性标志的方法.
结论:
- 詹森-香农分歧为结合亲和力预测提供了一个计算效率高的替代方案.
- 减少模拟时间保持预测准确度.
- 拟议的方法提高了药物发现中的结合亲和力预测的可靠性.
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