揭开病毒药物点:一种基于深度学习的方法,用于识别潜在的结合部位
Petr Popov1,2, Roman Kalinin3, Pavel Buslaev4
1Tetra-d, Rheinweg 9, Schaffhausen, 8200, Switzerland.
Briefings in bioinformatics
|December 19, 2023
概括
计算方法在SARS-CoV-2尖端蛋白上确定了一个新的药物标. 当这个网站被准时,可以通过稳定状蛋白在不活跃的构造中来抑制病毒的进入,从而帮助COVID-19药物发现.
科学领域:
- 结构生物学是结构生物学.
- 计算病毒学计算病毒学.
- 药物发现 药物发现
背景情况:
- 由于COVID-19的流行,需要快速确定有效的抗病毒疗法.
- 为像SARS-CoV-2这样的病毒选择可行的药物标是药物开发中的一个重要瓶.
- 计算方法可以通过分析病毒蛋白质结构来加快潜在药物标的识别.
研究的目的:
- 开发和验证基于结构和深度学习的计算方法,用于识别病毒蛋白上脆弱的药物结合部位.
- 将这种方法应用于SARS-CoV-2尖端糖蛋白 (S),以找到新的治疗点.
- 调查针对已识别的网站以抑制病毒进入的潜力.
主要方法:
- 利用基于结构和深度学习的方法,整合了蛋白质动态,结合点可访问性,可变性和药物作用机制.
- 将该方法应用于SARS-CoV-2尖端蛋白,专注于受体结合域.
- 执行了尖端蛋白-药物复合体的分子动力学模拟,并使用基于伪型病毒的测定方法验证了结果.
主要成果:
- 在SARS-CoV-2尖端蛋白上确定了一个新的,特定于形状和寡合体的,不含甘氨酸的结合部位,位于受体结合域附近.
- 分子动力学模拟表明,与该位点结合的候选药物将平衡转移到不活跃的尖峰形状.
- 一种伪型病毒测定确定了在微分子度下抑制病毒进入的命中化合物.
结论:
- 已确定的结合部位代表了开发SARS-CoV-2抑制剂的有希望的全性标.
- 准这个部位可能会阻止与人类ACE2受体相互作用的尖端蛋白所需的构造过渡.
- 这种计算策略有效地识别了针对COVID-19药物开发的病毒蛋白的脆弱区域.
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