可扩展性
计算机科学
纳米技术
钥匙(锁)
数据科学
生化工程
工作(物理)
药物发现
系统工程
融合
传感器融合
纳米颗粒
化学
计算生物学
药物输送
作者
Lisbeth R. Kjølbye,Mariana Valério,Markéta Paloncýová,Luís Borges-Araújo,Roberto Pestana-Nobles,Fabian Grünewald,Bart M. H. Bruininks,Rocío Araya‐Osorio,Martin Šrejber,Raúl Mera‐Adasme,Luca Monticelli,Siewert J. Marrink,Michal Otyepka,Sangwook Wu,Paulo C. T. Souza
标识
DOI:10.1021/acs.jctc.5c01207
摘要
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 lipid library, introducing over one hundred ionizable lipid models, natural sterols, and PEGylated lipids, covering the key components of LNP formulations. This expanded library brings an essential toolset to simulate LNPs at Martini coarse-grained resolution. We furthermore introduce initial protocols for screening fusion efficacy across lipid formulations and for constructing full LNPs and show how these tools can provide new insights into the LNP structure, dynamics, and efficiency. Altogether, this work introduces a practical and scalable approach for advancing the mechanistic understanding of LNPs and guiding their future development.
科研通智能强力驱动
Strongly Powered by AbleSci AI