Improved prediction of bilayer and monolayer properties using a refined BMW-MARTINI force field
Virginia Miguel1, Maria A Perillo1, Marcos A Villarreal2
1Instituto de Investigaciones Biológicas y Tecnológicas (IIBYT), CONICET - Cátedra de Química Biológica, Departamento de Química-ICTA, Facultad de Ciencias Exactas Físicas, Universidad Nacional de Córdoba, Argentina.
Abstract:
Coarse-grained (CG) models allow enlarging the size and time scales that are reachable by atomistic molecular dynamics simulations. A CG force field (FF) for lipids and amino acids that possesses a polarizable water model has been developed following the MARTINI parametrization strategy, the BMW-MARTINI [1]. We tested the BMW-MARTINI FF capability to describe some structural and thermodynamical properties of lipid monolayers and bilayers. We found that, since the surface tension values of oil/water interfaces calculated with the model are not correct, compression isotherms of lipid monolayers present artifacts. Also, this FF predicts DPPC and DAPC bilayers to remain in the Lα phase at temperatures as low as 283K, contrary to the expected from their experimental Tm values. Finally, simulations at constant temperature of bilayers of saturated lipids belonging to PC homologous, showed an increase in the mean molecular area (Mma) upon increasing the chain length, inversely to the experimental observation. We refined BMW-MARTINI FF by modifying as few parameters as possible in order to bring simulated and experimental measurements closer. We have also modified structural parameters of the lipid geometry that do not have direct influence in global properties of the bilayer membranes or monolayers, but serve to approach the obtained CG geometry to atomistic reference values. The refined FF is able to better reproduce phase transition temperatures and Mma for saturated PC bilayers than BMW-MARTINI and MARTINI FF. Finally, the simulated surface pressure-Mma isotherms of PC monolayers resemble the experimental ones and eliminate serious artifacts of previous models.
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