Dna Lipoplexes: Prediction of Phase Architectures Using Cg Simulations and Experimental Validation

Abstract

DNA lipoplexes are important mediators of transfection that offer a safer, although less efficient alternative to their viral counterparts. Thus improving the efficacy of DNA lipoplexes is essential for their exploitation in nanomedicine. Experimentally, it has been shown that the architecture of DNA lipoplexes is linked to their biological efficacy. Therefore the ability to predict the architectures of compositions of DNA lipoplexes would be highly desirable. However, prediction of the phase behaviour of such systems is difficult, largely owing to a complex interplay of intermolecular forces. Molecular dynamics simulations provide a potential strategy for predicting phase behaviour, but traditional, atomistic methods are not applicable to large DNA-lipid systems. Here, we present coarse-grained simulations of the lyotropic phase transitions of DNA lipoplexes as a function of lipid composition and water content. Our coarse-grained model of DNA uses a ∼ 4 to 1 mapping of atoms to particles and is compatible with existing coarse-grained models of biomolecules. With the appropriate balance of water content and lipid composition, we are able to capture the transition from the originally lamellar phase to the inverse hexagonal phase. Our simulation results show an inverse hexagonal phase with a calculated d-spacing of 6.2 nm for a DOPE-DNA system. Together with the disorder of the hexagonal phase, this d-spacing increases with increasing cationic lipid (DOTAP) content, in agreement with experimental data obtained by SAXS and polarizing light microscopy. Our simulations have provide insights into the rearrangements that occur to effect the transition to the inverse hexagonal phase; this level of detail is difficult to obtain using experimental methods alone. Furthermore, our simulations have highlighted the increasingly important role of coarse-grained simulation methods for the design of novel DNA lipoplexes and applications in synthetic biology, in general.