CoDock-Ligand:结合了基于模板的对接和基于CNN的评分,用于对接结合预测.
Mingwei Pang1, Wangqiu He1, Xufeng Lu2
1Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, Jiangsu, China.
BMC bioinformatics
|November 23, 2023
概括
CoDock-Ligand方法结合了基于模板的建模和GNINA评分函数,以准确地预测联体结合. 这种混合方法在CASP15竞赛中的大多数目标中取得了成功的预测.
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
- 药物发现 药物发现
背景情况:
- 准确的带结合预测对于分子对接至关重要.
- 整合基于模板的建模与精确的评分功能至关重要.
研究的目的:
- 引入CoDock-Ligand对接方法,用于对接体结合的预测.
- 为了评估CoDock-Ligand在CASP15竞赛中的表现.
主要方法:
- CoDock-Ligand将基于模板的建模与GNINA评分函数 (基于卷积神经网络的函数) 结合起来.
- 该方法被应用于CASP15.15中的带结合预测任务.
主要成果:
- 在前5个提交中,21个CASP15目标中的14个实现了成功的预测.
- 对于4个额外的目标,获得了部分成功的预测.
- 该方法准确地预测了复杂的H1114标,包括56个金属共因子和小分子的大多数配体的结合.
结论:
- 混合的CoDock-Ligand对接方案有效地解决了连接物结合预测的挑战.
- 受体蛋白中的形态变化可能会影响预测的准确性,突出显示未来改进的领域.
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