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恒 pH MD 模拟的最佳实践:准确性和采样。

Best Practices in Constant pH MD Simulations: Accuracy and Sampling.

机构信息

Nanoscience Center and Department of Chemistry, University of Jyväskylä, 40014 Jyväskylä, Finland.

Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.

出版信息

J Chem Theory Comput. 2022 Oct 11;18(10):6134-6147. doi: 10.1021/acs.jctc.2c00517. Epub 2022 Sep 15.

Abstract

Various approaches have been proposed to include the effect of pH in molecular dynamics (MD) simulations. Among these, the λ-dynamics approach proposed by Brooks and co-workers [Kong, X.; Brooks III, C. L. , , 2414-2423] can be performed with little computational overhead and hfor each typeence be used to routinely perform MD simulations at microsecond time scales, as shown in the accompanying paper [Aho, N. et al. , DOI: 10.1021/acs.jctc.2c00516]. At such time scales, however, the accuracy of the molecular mechanics force field and the parametrization becomes critical. Here, we address these issues and provide the community with guidelines on how to set up and perform long time scale constant pH MD simulations. We found that barriers associated with the torsions of side chains in the CHARMM36m force field are too high for reaching convergence in constant pH MD simulations on microsecond time scales. To avoid the high computational cost of extending the sampling, we propose small modifications to the force field to selectively reduce the torsional barriers. We demonstrate that with such modifications we obtain converged distributions of both protonation and torsional degrees of freedom and hence consistent p estimates, while the sampling of the overall configurational space accessible to proteins is unaffected as compared to normal MD simulations. We also show that the results of constant pH MD depend on the accuracy of the correction potentials. While these potentials are typically obtained by fitting a low-order polynomial to calculated free energy profiles, we find that higher order fits are essential to provide accurate and consistent results. By resolving problems in accuracy and sampling, the work described in this and the accompanying paper paves the way to the widespread application of constant pH MD beyond p prediction.

摘要

已经提出了各种方法来在分子动力学(MD)模拟中包含 pH 的影响。其中,Brooks 及其同事提出的 λ-动力学方法[Kong, X.; Brooks III, C. L.,, 2414-2423]可以在计算开销很小的情况下进行,并且可以用于在微秒时间尺度上常规地进行 MD 模拟,如随附论文中所示[Aho, N. et al., DOI: 10.1021/acs.jctc.2c00516]。然而,在这种时间尺度下,分子力学力场和参数化的准确性变得至关重要。在这里,我们解决了这些问题,并为社区提供了如何设置和执行长时间尺度恒 pH MD 模拟的指南。我们发现,CHARMM36m 力场中侧链扭转的势垒对于在微秒时间尺度上进行恒 pH MD 模拟达到收敛性来说太高了。为了避免扩展采样的高计算成本,我们对力场进行了小的修改,以有选择地降低扭转势垒。我们证明,通过这种修改,我们获得了质子化和扭转自由度的收敛分布,从而得到一致的 p 值估计,而与正常 MD 模拟相比,蛋白质可访问的整体构象空间的采样不受影响。我们还表明,恒 pH MD 的结果取决于校正势的准确性。虽然这些势通常通过拟合低阶多项式到计算的自由能曲线来获得,但我们发现更高阶拟合对于提供准确和一致的结果是必不可少的。通过解决准确性和采样问题,本论文和随附论文中描述的工作为在 p 值预测之外广泛应用恒 pH MD 铺平了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/302a/9558372/8d51fa64e16d/ct2c00517_0001.jpg

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