纳米技术
核糖核酸
化学
材料科学
生物物理学
生物化学
生物
基因
作者
Guan Wang,Mengtong Wu,Juanjuan Ye,Yazhou Xu,Yuxiao Chen,Caoyun Ju,Xiao Xu,Can Zhang
出处
期刊:ACS Nano
[American Chemical Society]
日期:2025-08-05
卷期号:19 (32): 29416-29429
标识
DOI:10.1021/acsnano.5c07140
摘要
Lipid nanoparticles (LNPs) currently serve as a leading platform for RNA delivery. In this field, endosomal escape of LNPs is a key challenge for efficient RNA therapies. Most current strategies focus on designing ionizable lipids to enhance interactions with endosomal membranes, promoting membrane fusion and RNA release. However, existing methods still rely heavily on time-consuming high-throughput screening, and no effective guidelines for rationally designing ionizable lipid structures have been established. In this study, we propose a lipid structure-based strategy for guiding the LNP formulation. We recommend using lipids with asymmetric hydrocarbon tails, exemplified by L-Ada, which consists of a long oleate chain and a short adamantane group. Through extensive all-atom molecular dynamics simulations, we demonstrate that these asymmetric molecules act as membrane-disrupting agents by inducing lipid back-folding, generating packing defects on the membrane surface that facilitate membrane fusion. To counterbalance the reduced membrane rigidity from significant asymmetry, we propose a formulation combining symmetric and asymmetric-tailed lipids. Our results show that the backfolding effect induced by adamantane aggregation can be effectively controlled by the lipid structure and composition. The optimized formulation, consisting of 20% L-Ada and 80% symmetric lipid L-Ste, achieves a favorable balance between packing defects and membrane rigidity, which is also validated by membrane fusion experiments. A simplified thermodynamic model is further proposed to explain these effects and provides specific guidelines for the design of those lipids. In summary, this study presents LNPs incorporating asymmetrically tailed lipids, demonstrating enhanced membrane fusion capabilities and providing a crucial foundation for the optimization of future LNP formulations.
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