Department of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States.
School of Computational Sciences, Korea Institute for Advanced Study, Seoul 02455, Republic of Korea.
J Chem Inf Model. 2021 Oct 25;61(10):5192-5202. doi: 10.1021/acs.jcim.1c00770. Epub 2021 Sep 21.
A lipid nanoparticle (LNP) formulation is a state-of-the-art delivery system for genetic drugs such as DNA, messenger RNA, and small interfering RNA, which is successfully applied to COVID-19 vaccines and gains tremendous interest in therapeutic applications. Despite its importance, a molecular-level understanding of the LNP structures and dynamics is still lacking, which makes rational LNP design almost impossible. In this work, we present an extension of CHARMM-GUI to model and simulate all-atom LNPs with various (ionizable) cationic lipids and PEGylated lipids (PEG-lipids). These new lipid types can be mixed with any existing lipid types with or without a biomolecule of interest, and the generated systems can be simulated using various molecular dynamics engines. As a first illustration, we considered model LNP membranes with DLin-KC2-DMA (KC2) or DLin-MC3-DMA (MC3) without PEG-lipids. The results from these model membranes are consistent with those from the two previous studies, albeit with mild accumulation of neutral MC3 in the bilayer center. To demonstrate 's capability of building a realistic LNP patch, we generated KC2- or MC3-containing LNP membranes with high concentrations of cholesterol and ionizable cationic lipids together with 2 mol % PEG-lipids. We observe that PEG-chains are flexible, which can be more preferentially extended laterally in the presence of cationic lipids due to the attractive interactions between their head groups and PEG oxygen. The presence of PEG-lipids also relaxes the lateral packing in LNP membranes, and the area compressibility modulus () of LNP membranes with cationic lipids fit into typical of fluid-phase membranes. Interestingly, the interactions between PEG oxygen and the head group of ionizable cationic lipids induce a negative curvature. We hope that this LNP capability in can be useful to better characterize various LNPs with or without genetic drugs for rational LNP design.
脂质纳米颗粒（LNP）制剂是一种用于 DNA、信使 RNA 和小干扰 RNA 等遗传药物的最新递药系统，已成功应用于 COVID-19 疫苗，并在治疗应用中引起了极大关注。尽管其很重要，但对 LNP 结构和动力学的分子水平理解仍然缺乏，这使得合理的 LNP 设计几乎不可能。在这项工作中，我们对 CHARMM-GUI 进行了扩展，以模拟和模拟具有各种（可离子化）阳离子脂质和聚乙二醇化脂质（PEG 脂质）的全原子 LNP。这些新的脂质类型可以与任何现有的脂质类型混合，无论是否含有感兴趣的生物分子，并且可以使用各种分子动力学引擎对生成的系统进行模拟。作为第一个说明，我们考虑了没有 PEG 脂质的 DLin-KC2-DMA（KC2）或 DLin-MC3-DMA（MC3）的模型 LNP 膜。这些模型膜的结果与之前的两项研究一致，尽管中性 MC3 在双层中心略有积累。为了展示 的构建真实 LNP 斑块的能力，我们生成了含有 KC2 或 MC3 的 LNP 膜，其中含有高浓度的胆固醇和可离子化的阳离子脂质以及 2 mol%的 PEG 脂质。我们观察到 PEG 链是柔性的，由于其头部基团与 PEG 氧之间的吸引力相互作用，它们在存在阳离子脂质时可以更优先地侧向延伸。PEG 脂质的存在也会放松 LNP 膜的侧向堆积，并且具有阳离子脂质的 LNP 膜的面积压缩模量（）适合于典型的流体相膜。有趣的是，PEG 氧与可离子化阳离子脂质的头部基团之间的相互作用会产生负曲率。我们希望 中的这种 LNP 能力对于更好地表征具有或不具有遗传药物的各种 LNP 以进行合理的 LNP 设计将是有用的。
