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巨正则蒙特卡罗和深度学习辅助增强采样,以表征Mg2+的分布以及德鲁德极化力场对扭曲核酶折叠态稳定性的影响。

Grand canonical Monte Carlo and deep learning assisted enhanced sampling to characterize the distribution of Mg2+ and influence of the Drude polarizable force field on the stability of folded states of the twister ribozyme.

作者信息

Baral Prabin, Sengul Mert Y, MacKerell Alexander D

机构信息

Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, USA.

出版信息

J Chem Phys. 2024 Dec 14;161(22). doi: 10.1063/5.0241246.

Abstract

Molecular dynamics simulations are crucial for understanding the structural and dynamical behavior of biomolecular systems, including the impact of their environment. However, there is a gap between the time scale of these simulations and that of real-world experiments. To address this problem, various enhanced simulation methods have been developed. In addition, there has been a significant advancement of the force fields used for simulations associated with the explicit treatment of electronic polarizability. In this study, we apply oscillating chemical potential grand canonical Monte Carlo and machine learning methods to determine reaction coordinates combined with metadynamics simulations to explore the role of Mg2+ distribution and electronic polarizability in the context of the classical Drude oscillator polarizable force field on the stability of the twister ribozyme. The introduction of electronic polarizability along with the details of the distribution of Mg2+ significantly stabilizes the simulations with respect to sampling the crystallographic conformation. The introduction of electronic polarizability leads to increased stability over that obtained with the additive CHARMM36 FF reported in a previous study, allowing for a distribution of a wider range of ions to stabilize twister. Specific interactions contributing to stabilization are identified, including both those observed in the crystal structures and additional experimentally unobserved interactions. Interactions of Mg2+ with the bases are indicated to make important contributions to stabilization. Notably, the presence of specific interactions between the Mg2+ ions and bases or the non-bridging phosphate oxygens (NBPOs) leads to enhanced dipole moments of all three moieties. Mg2+-NBPO interactions led to enhanced dipoles of the phosphates but, interestingly, not in all the participating ions. The present results further indicate the importance of electronic polarizability in stabilizing RNA in molecular simulations and the complicated nature of the relationship of Mg2+-RNA interactions with the polarization response of the bases and phosphates.

摘要

分子动力学模拟对于理解生物分子系统的结构和动力学行为至关重要,包括其环境的影响。然而,这些模拟的时间尺度与实际实验的时间尺度之间存在差距。为了解决这个问题,已经开发了各种增强模拟方法。此外,与电子极化率的显式处理相关的用于模拟的力场也有了显著进展。在本研究中,我们应用振荡化学势巨正则蒙特卡罗方法和机器学习方法来确定反应坐标,并结合元动力学模拟,以探索在经典德鲁德振子可极化力场背景下Mg2+分布和电子极化率对扭曲核酶稳定性的作用。引入电子极化率以及Mg2+分布的细节,相对于采样晶体学构象而言,显著稳定了模拟。引入电子极化率导致比先前研究中报道的加性CHARMM36力场获得的稳定性更高,从而允许更广泛范围的离子分布来稳定扭曲核酶。确定了有助于稳定的特定相互作用,包括在晶体结构中观察到的那些以及其他实验未观察到的相互作用。表明Mg2+与碱基的相互作用对稳定有重要贡献。值得注意的是,Mg2+离子与碱基或非桥连磷酸氧(NBPOs)之间特定相互作用的存在导致所有三个部分的偶极矩增强。Mg2+-NBPO相互作用导致磷酸盐的偶极增强,但有趣的是,并非在所有参与离子中都是如此。本研究结果进一步表明电子极化率在分子模拟中稳定RNA的重要性,以及Mg2+-RNA相互作用与碱基和磷酸盐极化响应关系的复杂性。

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