Lipid nanoparticles (LNPs) represent a promising platform for advanced drug and gene delivery, yet optimizing these particles for specific cargos and cell targets poses a complex, multifaceted challenge. Furthermore, there is a pressing need for a more comprehensive understanding of the underlying technology. Experimental studies are costly and often provide low-resolution information. Molecular dynamics (MD) simulations allow us to study these particles at a higher resolution, enhancing our understanding. However, studying these systems at atomic resolutions is both challenging and computationally expensive, as well as time-consuming. Coarse-grained (CG) models, such as Martini 3, are positioned as promising tools for studying LNPs. To enable CG-MD studies of LNPs, accurate and validated models of their components are needed. Here, we present a substantial extension of the Martini 3 library of lipids, covering the most important LNP components, including over a hundred of ionizable lipid (IL) models, along with natural occurring sterol models and PEGylated lipid models. We furthermore present initial protocols for screening fusion efficacy across different lipid formulations and for constructing whole LNPs at CG resolution, enabling future studies of these nanoparticles.

中文翻译：

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Martini 用于脂质纳米颗粒设计的 3 个构建模块


脂质纳米颗粒 （LNP） 代表了一个很有前途的先进药物和基因递送平台，但针对特定货物和细胞靶标优化这些颗粒是一项复杂的多方面挑战。此外，迫切需要更全面地了解底层技术。实验研究成本高昂，并且通常提供低分辨率信息。分子动力学 （MD） 模拟使我们能够以更高的分辨率研究这些粒子，从而增强我们的理解。然而，在原子分辨率下研究这些系统既具有挑战性，又计算成本高昂，而且耗时。粗粒度 （CG） 模型，例如 Martini 3，被定位为研究 LNP 的有前途的工具。为了对 LNP 进行 CG-MD 研究，需要对其组件进行准确且经过验证的模型。在这里，我们介绍了 Martini 3 脂质库的大量扩展，涵盖了最重要的 LNP 成分，包括 100 多种可电离脂质 （IL） 模型，以及天然存在的甾醇模型和聚乙二醇化脂质模型。我们还提出了用于筛选不同脂质制剂的融合功效和以 CG 分辨率构建整个 LNP 的初始方案，从而能够对这些纳米颗粒进行未来的研究。