在深度学习中利用氨基酸对定位编码来预测蛋白质-质结合的亲和力
Wei Liu1, Wenhui Tian2, Theam Soon Lim1
1Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden 11800, Malaysia.
Journal of chemical information and modeling
|October 22, 2025
概括
这项研究引入了氨基酸对定位编码 (AAPPE),这是一个新的深度学习方法,用于预测蛋白质 - 连接体结合亲和力. AAPPE有效地捕捉相互作用而不需要连接体姿势,加速药物发现.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 预测蛋白质 - 配体结合亲和力对药物发现至关重要.
- 当前的方法在有效捕捉复杂的相互作用方面面临着挑战.
- 准确的预测需要整合结构和化学信息.
研究的目的:
- 开发一种新的深度学习框架,用于预测蛋白质 - 配体结合亲和力.
- 整合氨基酸和连接体特征之间的空间关系.
- 创建一个无姿势和计算效率高的预测模型.
主要方法:
- 引入了氨基酸对位置编码 (AAPPE) 框架.
- 蛋白质口袋中的氨基酸的综合空间关系与连接物分子指纹.
- 将生物相关原子的对距离编码为固定位置范围.
- 开发了一个3124维的特征集,独立于连接体结合的位置.
主要成果:
- 在CASF-2016基准上取得了强大的预测性能 (MAE = 0.99,RMSE = 1.28,R = 0.82).
- 除研究证实了生物知情原子选择的意义.
- 证明了计算效率和无姿势预测能力.
结论:
- AAPPE为基于结构的药物设计提供了一种可概括和可解释的方法.
- 该方法提供了一种实用的工具,用于优先考虑蛋白质 - 连接体复合体中的相互作用.
- 这种框架可以通过改进结合性亲缘关系预测来加速药物发现管道.
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