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一个ANI-2启用的开源协议可以在对接后估计带菌株

Francois Berenger¹, Koji Tsuda¹

¹Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.

Journal of computational chemistry

|October 5, 2024

概括

这项研究引入了一种新的计算协议,通过使用量子力学和蒙特卡洛方法估计连接物菌株来提高蛋白质-连接物对接的准确性. 这种方法可以显著提高虚拟选活动的命中率.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
生物信息学是一种生物信息学.

背景情况:

蛋白质-连接体对接分数通常是不精确的,因为很难用分子力学力场计算连接体的内部能量.
这种不精确会引入噪音,对潜在的候选药物的准确排名产生负面影响.

研究的目的:

开发和评估一个开源协议,用于估计接后的带应变能量.
提高虚拟选的准确性,通过根据其估计的菌株过对接联体.

主要方法:

该协议采用两种量子力学 (QM) 单点能量计算,将基于蒙特卡洛 (MC) 的双面空间的联体最小化进行三明治化.
在MC模拟中使用ANI-2x力场,QM的近似,以高效地估计应变.
基于结构的虚拟选活动在9个蛋白质标上进行,使用CCDC对带对接 (GOLD) 软件的基因优化.

主要成果:

拟议的菌株估计协议,当在对接后应用时,表明能够显著提高某些蛋白质点的命中率.
对虚拟选活动的分析表明,该方法的有效性取决于特定的蛋白质标和已知的活性/无活性分子的可用性.
该协议并不能普遍保证改善命中率,强调需要针对特定目标进行评估.

结论:

开发的开源协议提供了一种有前途的方法,通过结合连接体菌株估计来完善蛋白质-连接体对接.
菌株过可以提高虚拟选效率,但其成功取决于目标系统和数据可用性.
建议进一步评估,以确定该协议对特定药物发现项目的有用性.

关键词:

在 ANI‐2x 中,您可以使用 ANI‐2x.在LIT-PCBABA.这就是为什么MC MC MC.在QM中,QM就是QM.这就是SBVS.停靠的对接方式连接器连接器连接器压力压力压力压力

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