RmsdXNA:RMSD使用机器学习方法预测核酸-连接物对接姿势
Lai Heng Tan1, Chee Keong Kwoh2, Yuguang Mu3
1Interdisciplinary Graduate School, Nanyang Technological University, 61 Nanyang Drive, 637335 Singapore, Singapore.
Briefings in bioinformatics
|May 2, 2024
概括
一个新的机器学习模型,RmsdXNA,准确地预测核酸标的联结姿势. 该工具通过改善用于基于核酸的疗法的有效小分子药物的识别来增强药物发现.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习在生物信息学中的应用.
背景情况:
- 针对核酸 (NA) 的小分子药物对于调节生物过程至关重要.
- 目前的计算建模,包括分子对接和评分函数,往往难以准确预测连接体-NA结合的位置.
研究的目的:
- 开发一种机器学习模型,RmsdXNA,用于预测NA复合体中的联体对接姿势的根平均平方偏差 (RMSD).
- 为了提高核酸点的计算药物设计的准确性.
主要方法:
- 开发RmsdXNA机器学习模型,用于预测NA-ligand复合体的RMSD.
- 使用各种NA-连接体复合体 (金属复合体,) 的验证.
- 使用分子动力学模拟对RNA-小分子复合体和MALAT1进行rDock评分功能的比较分析和实验验证.
主要成果:
- RmsdXNA在预测和实际RMSD值之间显示出强烈的相关性.
- RmsdXNA在排名和识别各种NA-ligand复合体的近原生姿势方面表现优于rDock.
- 通过模拟分子动力学,RmsdXNA显示了RNA-小分子复合物的优越选能力,以及通过分子动力学模拟识别有希望的配体的更高成功率.
结论:
- RmsdXNA提供了一个准确和多功能工具,用于预测核酸复合体中的联体对接姿势.
- 开发的模型显著改善了现有的评分功能,有助于更有效的药物发现和开发针对NA的向疗法.
- RmsdXNA代码是公开的,以促进进一步的研究.
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