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Lipid shape and packing are key for optimal design of pH-sensitive mRNA lipid nanoparticles.

Giulio TeseiYa-Wen HsiaoAleksandra DabkowskaGunnar GrönbergMarianna Yanez ArtetaDavid UlkoskiDavid J BrayMartin TrulssonJohan UlanderMikael LundLennart Lindfors
Published in: Proceedings of the National Academy of Sciences of the United States of America (2024)
The ionizable-lipid component of RNA-containing nanoparticles controls the pH-dependent behavior necessary for an efficient delivery of the cargo-the so-called endosomal escape. However, it is still an empirical exercise to identify optimally performing lipids. Here, we study two well-known ionizable lipids, DLin-MC3-DMA and DLin-DMA using a combination of experiments, multiscale computer simulations, and electrostatic theory. All-atom molecular dynamics simulations, and experimentally measured polar headgroup p K a values, are used to develop a coarse-grained representation of the lipids, which enables the investigation of the pH-dependent behavior of lipid nanoparticles (LNPs) through Monte Carlo simulations, in the absence and presence of RNA molecules. Our results show that the charge state of the lipids is determined by the interplay between lipid shape and headgroup chemistry, providing an explanation for the similar pH-dependent ionization state observed for lipids with headgroup p K a values about one-pH-unit apart. The pH dependence of lipid ionization is significantly influenced by the presence of RNA, whereby charge neutrality is achieved by imparting a finite and constant charge per lipid at intermediate pH values. The simulation results are experimentally supported by measurements of α-carbon 13 C-NMR chemical shifts for eGFP mRNA LNPs of both DLin-MC3-DMA and DLin-DMA at various pH conditions. Further, we evaluate the applicability of a mean-field Poisson-Boltzmann theory to capture these phenomena.
Keyphrases
  • fatty acid
  • molecular dynamics simulations
  • molecular dynamics
  • monte carlo
  • magnetic resonance
  • physical activity
  • mass spectrometry
  • binding protein
  • ionic liquid
  • solid state
  • neural network