Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo.

Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo.
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DOI:
10.1007/s10822-022-00479-w
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发表时间:
2022-10
影响因子:
3.5
通讯作者:
--
中科院分区:
生物学3区
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--
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水在介导蛋白质-配体相互作用中起重要作用。配体结合或修饰后的水重排可能非常缓慢,并且超出分子动力学(MD)模拟中使用的典型时间尺度。因此,在MD模拟中缓慢的水运动的不充分的采样常常损害配体结合自由能计算的准确性的准确性。以前的研究表明,巨正则蒙特卡罗(GCMC)优于正常的MD模拟水样,因此GCMC已被应用于帮助提高配体结合自由能计算的准确性。然而,在先前的工作中,我们观察到蛋白质和/或配体运动损害了GCMC在水再水化中的表现,这表明需要更多的工作来改进这种方法来处理水采样。在这项工作中,我们应用GCMC在21个蛋白质配体系统,以评估GCMC的性能,再水化埋水网站。虽然我们的研究结果表明,GCMC可以迅速再水化所有选定的水网站为大多数系统,它失败了5个系统。在大多数失败的系统中,我们观察到蛋白质/配体运动,这发生在没有水的情况下,联合收割机关闭水网站和阻止瞬时GCMC水插入移动。对于这5个失败的系统,我们都扩展了我们的GCMC模拟,并测试了一种新的技术命名为巨正则非平衡候选蒙特卡罗(GCNCMC)。GCNCMC将GCMC与非平衡候选蒙特卡罗(NCMC)抽样技术相结合,以提高成功插入/删除水的概率。我们的研究结果表明,GCNCMC和扩展的GCMC可以再水化所有目标水网站的五个有问题的系统和GCNCMC是更有效的比GCMC在两个三个系统。在一个系统中,只有GCNCMC可以对所有目标水站点进行再水化,而GCMC则失败。GCNCMC和GCMC在一个系统中都失败。这项工作表明,这种新的GCNCMC方法是有希望的水再水化,特别是当蛋白质/配体运动可能会阻止水插入/去除。
Water plays an important role in mediating protein-ligand interactions. Water rearrangement upon a ligand binding or modification can be very slow and beyond typical timescales used in molecular dynamics (MD) simulations. Thus, inadequate sampling of slow water motions in MD simulations often impairs the accuracy of the accuracy of ligand binding free energy calculations. Previous studies suggest grand canonical Monte Carlo (GCMC) outperforms normal MD simulations for water sampling, thus GCMC has been applied to help improve the accuracy of ligand binding free energy calculations. However, in prior work we observed protein and/or ligand motions impaired how well GCMC performs at water rehydration, suggesting more work is needed to improve this method to handle water sampling. In this work, we applied GCMC in 21 protein-ligand systems to assess the performance of GCMC for rehydrating buried water sites. While our results show that GCMC can rapidly rehydrate all selected water sites for most systems, it fails in 5 systems. In most failed systems, we observe protein/ligand motions, which occur in the absence of water, combine to close water sites and block instantaneous GCMC water insertion moves. For these 5 failed systems, we both extended our GCMC simulations and tested a new technique named grand canonical nonequilibrium candidate Monte Carlo (GCNCMC). GCNCMC combines GCMC with the nonequilibrium candidate Monte Carlo (NCMC) sampling technique to improve the probability of a successful water insertion/deletion. Our results show that GCNCMC and extended GCMC can rehydrate all target water sites for three of the five problematic systems and GCNCMC is more efficient than GCMC in two out of the three systems. In one system, only GCNCMC can rehydrate all target water sites, while GCMC fails. Both GCNCMC and GCMC fail in one system. This work suggests this new GCNCMC method is promising for water rehydration especially when protein/ligand motions may block water insertion/removal.
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