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半自动化优化 CHARMM36 脂类力场以包含长程色散的显式处理。

Semi-automated Optimization of the CHARMM36 Lipid Force Field to Include Explicit Treatment of Long-Range Dispersion.

机构信息

Biophysics Graduate Program, University of Maryland, College Park, Maryland 20742, United States.

Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States.

出版信息

J Chem Theory Comput. 2021 Mar 9;17(3):1562-1580. doi: 10.1021/acs.jctc.0c01326. Epub 2021 Feb 23.

Abstract

The development of the CHARMM lipid force field (FF) can be traced back to the early 1990s with its current version denoted CHARMM36 (C36). The parametrization of C36 utilized high-level quantum mechanical data and free energy calculations of model compounds before parameters were manually adjusted to yield agreement with experimental properties of lipid bilayers. While such manual fine-tuning of FF parameters is based on intuition and trial-and-error, automated methods can identify beneficial modifications of the parameters via their sensitivities and thereby guide the optimization process. This work introduces a semi-automated approach to reparametrize the CHARMM lipid FF with consistent inclusion of long-range dispersion through the Lennard-Jones particle-mesh Ewald (LJ-PME) approach. The optimization method is based on thermodynamic reweighting with regularization with respect to the C36 set. Two independent optimizations with different topology restrictions are presented. Targets of the optimizations are primarily liquid crystalline phase properties of lipid bilayers and the compression isotherm of monolayers. Pair correlation functions between water and lipid functional groups in aqueous solution are also included to address headgroup hydration. While the physics of the reweighting strategy itself is well-understood, applying it to heterogeneous, complex anisotropic systems poses additional challenges. These were overcome through careful selection of target properties and reweighting settings allowing for the successful incorporation of the explicit treatment of long-range dispersion, and we denote the newly optimized lipid force field as C36/LJ-PME. The current implementation of the optimization protocol will facilitate the future development of the CHARMM and related lipid force fields.

摘要

CHARMM 脂质力场(FF)的发展可以追溯到 20 世纪 90 年代初,其当前版本为 CHARMM36(C36)。C36 的参数化利用了模型化合物的高级量子力学数据和自由能计算,然后手动调整参数以与脂质双层的实验性质一致。虽然这种对 FF 参数的手动微调是基于直觉和反复试验,但自动化方法可以通过其敏感性来识别参数的有益修改,从而指导优化过程。这项工作引入了一种半自动方法来重新参数化 CHARMM 脂质 FF,通过 Lennard-Jones 粒子网格 Ewald(LJ-PME)方法一致地包含长程色散。优化方法基于热力学再加权,并对 C36 集进行正则化。提出了两种具有不同拓扑限制的独立优化。优化的目标主要是脂质双层的液晶相性质和单层的压缩等温线。还包括水和脂质官能团在水溶液中的对相关函数,以解决头基水化问题。虽然再加权策略本身的物理学已经很好理解,但将其应用于异构、复杂各向异性系统还存在额外的挑战。通过仔细选择目标性质和再加权设置,克服了这些挑战,从而成功地纳入了长程色散的显式处理,我们将新优化的脂质力场表示为 C36/LJ-PME。当前实施的优化协议将促进 CHARMM 和相关脂质力场的未来发展。

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