通过比较复杂结构建模,通过蛋白质 - 配体结合相互作用的高质量数据集
Xuelian Li1,2, Cheng Shen2, Hui Zhu2,3
1National Institute of Biological Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.
Journal of chemical information and modeling
|January 5, 2024
概括
结合网 (BindingNet) 是一个新的数据集,包含69,816个模拟的蛋白质 - 连接体复合体,通过提供结构洞察和改进机器学习模型来预测结合亲和力来帮助药物设计.
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:
- 高质量的蛋白质连接体复杂结构对于理解非共价结合和基于结构的药物设计至关重要.
- 与广的化学空间相比,实验确定的结构是有限的,这阻碍了全面的分析.
研究的目的:
- 为了解决实验性蛋白质 - 连接体复杂结构的稀缺性.
- 构建一个大规模的,高质量的数据集,用实验性的结合亲缘关系数据建模的蛋白质-连接体复合体.
- 为研究蛋白质-连接体相互作用,结构-活性关系和评估计算方法提供资源.
主要方法:
- 用比较复杂结构建模来构建BindingNet数据集.
- 该数据集被策划为包含69,816个模拟的蛋白质 - 连接体复合体,具有实验性的结合亲和数据.
- 该数据集旨在进行视觉检查,解释结构-活动关系,并评估机器学习模型.
主要成果:
- 绑定网提供了对蛋白质 - 连接体相互作用和结构 - 活动关系的宝贵见解.
- 在BindingNet上训练的机器学习模型与在PDBbind.com上训练的模型相比,显示偏差较小.
- 该研究讨论了改进BindingNet的策略及其在基准分子对接和结合自由能计算中的使用.
结论:
- 绑定网络补充了PDBbind等现有数据集,为蛋白质-连接体结合研究提供了充足且公正的资源.
- 该数据集有助于评估和改进用于药物设计和结合性亲缘关系预测的计算工具.
- 绑定网络是免费可用的,促进结构生物学和计算化学的进一步研究.
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