RNA-联体分子对接:进步和挑战
Yuanzhe Zhou1, Yangwei Jiang1, Shi-Jie Chen1
1Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA.
计算方法通过模拟RNA-小分子相互作用来加速药物发现. 本综述涵盖了先进的对接和评分技术,包括深度学习,用于预测RNA-连接体结合和疗效.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 分子建模分子建模
背景情况:
- 虚拟查加速了候选药物的选择.
- 对RNA-小分子相互作用的计算建模对于RNA向药物发现至关重要.
- 目前的RNA-连接体结合模型主要使用对接和得分方法.
研究的目的:
- 提供RNA-接对接的计算方法的概述.
- 讨论最近开发的方法的优缺点.
- 为了突出RNA-连接体结合预测的挑战.
主要方法:
- 审查对接和得分方法.
- 热力学和运动模型的讨论.
- 包括深度学习方法.
主要成果:
- 准确的对接和评分必须解决连接体和RNA的灵活性,结合点采样和姿势评分.
- 由于金属离子效应等因素,对RNA-连接体结合的预测变得复杂.
- 热力学和动力学模型在预测结合姿势和亲和力方面取得了成功.
- 深度学习为RNA-小分子结合预测提供了新的工具.
结论:
- 计算方法对于推进RNA向药物发现至关重要.
- 解决灵活性,采样,评分和特定RNA挑战是关键.
- 新兴的深度学习技术显示了改进RNA-配体相互作用预测的前景.
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