Department of Chemistry , The University of Chicago , Chicago , Illinois 60637 , United States.
J Chem Theory Comput. 2019 Mar 12;15(3):2087-2100. doi: 10.1021/acs.jctc.8b01033. Epub 2019 Feb 15.
Despite the central role of lipids in many biophysical functions, the molecular mechanisms that dictate macroscopic lipid behavior remain elusive to both experimental and computational approaches. As such, there has been much interest in the development of low-resolution, implicit-solvent coarse-grained (CG) models to dynamically simulate biologically relevant spatiotemporal scales with molecular fidelity. However, in the absence of solvent, a key challenge for CG models is to faithfully emulate solvent-mediated forces, which include both hydrophilic and hydrophobic interactions that drive lipid aggregation and self-assembly. In this work, we provide a new methodological framework to incorporate semiexplicit solvent effects through the use of virtual CG particles, which represent structural features of the solvent-lipid interface. To do so, we leverage two systematic coarse-graining approaches, multiscale coarse-graining (MS-CG) and relative entropy minimization (REM), in a hybrid fashion to construct our virtual-site CG (VCG) models. As a proof-of-concept, we focus our efforts on two lipid species, 1,2-dioleoyl- sn-glycero-3-phosphocholine (DOPC) and 1,2-dipalmitoyl- sn-glycero-3-phosphocholine (DPPC), which adopt a liquid-disordered and gel phase, respectively, at room temperature. Through our analysis, we also present, to our knowledge, the first direct comparison between the MS-CG and REM methods for a complex biomolecule and highlight each of their strengths and weaknesses. We further demonstrate that VCG models recapitulate the rich biophysics of lipids, which enable self-assembly, morphological diversity, and multiple phases. Our findings suggest that the VCG framework is a powerful approach for investigation into macromolecular biophysics.
尽管脂质在许多生物物理功能中起着核心作用,但决定宏观脂质行为的分子机制仍然难以通过实验和计算方法来确定。因此,人们非常关注开发低分辨率、隐溶剂粗粒化 (CG) 模型,以便以分子保真度动态模拟具有生物学相关性的时空尺度。然而,在没有溶剂的情况下,CG 模型面临的一个关键挑战是忠实地模拟溶剂介导的力,其中包括亲水和疏水相互作用,这些相互作用驱动脂质聚集和自组装。在这项工作中,我们提供了一种新的方法框架,通过使用虚拟 CG 粒子来纳入半显式溶剂效应,虚拟 CG 粒子代表溶剂-脂质界面的结构特征。为此,我们以混合方式利用两种系统的粗粒化方法,即多尺度粗粒化 (MS-CG) 和相对熵最小化 (REM),来构建我们的虚拟位点 CG (VCG) 模型。作为概念验证,我们专注于两种脂质物种,1,2-二油酰基-sn-甘油-3-磷酸胆碱 (DOPC) 和 1,2-二月桂酰基-sn-甘油-3-磷酸胆碱 (DPPC),它们在室温下分别采用无序液体相和凝胶相。通过我们的分析,我们还首次展示了 MS-CG 和 REM 方法在复杂生物分子上的直接比较,突出了它们各自的优缺点。我们进一步证明,VCG 模型再现了脂质的丰富生物物理学特性,这些特性使脂质能够自组装、形态多样化和存在多种相。我们的研究结果表明,VCG 框架是研究大分子生物物理学的有力方法。