Maciejewski Arkadiusz, Pasenkiewicz-Gierula Marta, Cramariuc Oana, Vattulainen Ilpo, Rog Tomasz
Department of Physics, Tampere University of Technology , PO Box 692, FI-33101 Tampere, Finland.
J Phys Chem B. 2014 May 1;118(17):4571-81. doi: 10.1021/jp5016627. Epub 2014 Apr 18.
We report parametrization of dipalmitoyl-phosphatidylcholine (DPPC) in the framework of the Optimized Parameters for Liquid Simulations all-atom (OPLS-AA) force field. We chose DPPC as it is one of the most studied phospholipid species and thus has plenty of experimental data necessary for model validation, and it is also one of the highly important and abundant lipid types, e.g., in lung surfactant. Overall, PCs have not been previously parametrized in the OPLS-AA force field; thus, there is a need to derive its bonding and nonbonding parameters for both the polar and nonpolar parts of the molecule. In the present study, we determined the parameters for torsion angles in the phosphatidylcholine and glycerol moieties and in the acyl chains, as well the partial atomic charges. In these calculations, we used three methods: (1) Hartree-Fock (HF), (2) second order Møller-Plesset perturbation theory (MP2), and (3) density functional theory (DFT). We also tested the effect of the polar environment by using the polarizable continuum model (PCM), and for acyl chains the van der Waals parameters were also adjusted. In effect, six parameter sets were generated and tested on a DPPC bilayer. Out of these six sets, only one was found to be able to satisfactorily reproduce experimental data for the lipid bilayer. The successful DPPC model was obtained from MP2 calculations in an implicit polar environment (PCM).
我们报告了在液体模拟全原子优化参数(OPLS-AA)力场框架下对二棕榈酰磷脂酰胆碱(DPPC)的参数化。我们选择DPPC是因为它是研究最多的磷脂种类之一,因此有大量模型验证所需的实验数据,并且它也是非常重要且丰富的脂质类型之一,例如在肺表面活性剂中。总体而言,此前尚未在OPLS-AA力场中对磷脂酰胆碱进行参数化;因此,需要推导其分子极性和非极性部分的键合和非键合参数。在本研究中,我们确定了磷脂酰胆碱和甘油部分以及酰基链中扭转角的参数,以及部分原子电荷。在这些计算中,我们使用了三种方法:(1)哈特里-福克(HF)方法,(2)二阶莫勒-普列斯塞特微扰理论(MP2),以及(3)密度泛函理论(DFT)。我们还通过使用极化连续介质模型(PCM)测试了极性环境的影响,并且对于酰基链,还调整了范德华参数。实际上,生成了六组参数并在DPPC双层上进行了测试。在这六组参数中,只有一组能够令人满意地重现脂质双层的实验数据。成功的DPPC模型是通过在隐式极性环境(PCM)中的MP2计算获得的。