Baron Riccardo, de Vries Alex H, Hünenberger Philippe H, van Gunsteren Wilfred F
Laboratorium für Physikalische Chemie, ETH-Zürich, CH-8093 Zürich, Switzerland.
J Phys Chem B. 2006 Apr 27;110(16):8464-73. doi: 10.1021/jp055888y.
Molecular liquids can be modeled at different levels of spatial resolution. In atomic-level (AL) models, all (heavy) atoms can be explicitly simulated. In coarse-grained (CG) models, particles (beads) that represent groups of covalently bound atoms are used as elementary units. Ideally, a CG model should reproduce the thermodynamic and structural properties of the corresponding AL model after mapping to the lower-resolution scale. In the present work, two such models are investigated: (i) the classical GROMOS atomic-level model; (ii) a CG model recently proposed by Marrink et al., which maps approximately four non-hydrogen atoms to one bead [J. Phys. Chem. B 2004, 108, 750]. The study is restricted to n-alkanes whose aliphatic fragments are abundantly found in lipids of biological interest. Additionally, cis-9-octadecene is included, as a template chain of the lipid dioleoylphosphatidylcholine (DOPC). The two representations of molecules in the liquid phase are compared in terms of average molecular structures, extent of configurational space sampled, and single-molecule entropies. An approximate method is used to estimate the rotational contributions to the absolute configurational entropy. Good correspondence between the AL and CG representations is found. The loss in configurational entropy due to the reduction in degrees of freedom upon coarse-graining of the model is estimated.
分子液体可以在不同的空间分辨率水平上进行建模。在原子水平(AL)模型中,可以明确模拟所有(重)原子。在粗粒度(CG)模型中,代表共价键合原子基团的粒子(珠子)被用作基本单元。理想情况下,CG模型在映射到较低分辨率尺度后,应重现相应AL模型的热力学和结构性质。在本工作中,研究了两种这样的模型:(i)经典的GROMOS原子水平模型;(ii)Marrink等人最近提出的一种CG模型,该模型将大约四个非氢原子映射到一个珠子[《物理化学杂志B》2004年,108卷,750页]。该研究仅限于在具有生物学意义的脂质中大量存在脂肪族片段的正构烷烃。此外,还包括顺式-9-十八碳烯,作为脂质二油酰磷脂酰胆碱(DOPC)的模板链。从平均分子结构、采样的构型空间范围和单分子熵方面比较了液相中分子的两种表示形式。使用一种近似方法来估计对绝对构型熵的转动贡献。发现AL和CG表示之间有良好的对应关系。估计了由于模型粗粒化导致自由度减少而引起的构型熵损失。