亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

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

化学 纳米颗粒 钥匙(锁) 纳米技术 材料科学 计算机科学 计算机安全
作者
Giulio Tesei,Ya‐Wen Hsiao,Aleksandra P. Dabkowska,Gunnar Grönberg,Marianna Yanez Arteta,David Ulkoski,David J. Bray,Martin Trulsson,Johan Ulander,Mikael Lund,Lennart Lindfors
出处
期刊:Proceedings of the National Academy of Sciences of the United States of America [National Academy of Sciences]
卷期号:121 (2) 被引量:32
标识
DOI:10.1073/pnas.2311700120
摘要

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 pKa 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 pKa 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 13C-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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI6.4应助纪年采纳,获得20
1秒前
充电宝应助wang采纳,获得10
4秒前
碳酸芙兰发布了新的文献求助10
5秒前
盛夏发布了新的文献求助10
5秒前
7秒前
碳酸芙兰完成签到,获得积分10
10秒前
平淡纲发布了新的文献求助10
11秒前
kepler完成签到,获得积分10
13秒前
17秒前
老实映易完成签到,获得积分20
17秒前
18秒前
老实映易发布了新的文献求助10
20秒前
zhou发布了新的文献求助10
25秒前
qianyixingchen完成签到 ,获得积分10
27秒前
天天快乐应助w1x2123采纳,获得10
33秒前
唐七完成签到,获得积分10
34秒前
clhoxvpze完成签到 ,获得积分10
39秒前
无花果应助赵海棠采纳,获得10
43秒前
w1x2123完成签到,获得积分10
44秒前
庆庆完成签到 ,获得积分10
45秒前
47秒前
48秒前
51秒前
横空完成签到,获得积分10
52秒前
打打应助HY2024采纳,获得10
52秒前
shenyihui完成签到,获得积分10
55秒前
赵海棠发布了新的文献求助10
55秒前
共享精神应助wdcpszd采纳,获得30
56秒前
英俊的铭应助科研通管家采纳,获得10
56秒前
盘菜应助科研通管家采纳,获得10
56秒前
SciGPT应助科研通管家采纳,获得10
56秒前
Orange应助科研通管家采纳,获得10
56秒前
May完成签到,获得积分10
59秒前
1分钟前
研究生完成签到 ,获得积分10
1分钟前
1分钟前
AX完成签到,获得积分10
1分钟前
1分钟前
1分钟前
1分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
CLSI M100 Performance Standards for Antimicrobial Susceptibility Testing 36th edition 400
Cancer Targets: Novel Therapies and Emerging Research Directions (Part 1) 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6361987
求助须知:如何正确求助?哪些是违规求助? 8175670
关于积分的说明 17223868
捐赠科研通 5416734
什么是DOI,文献DOI怎么找? 2866520
邀请新用户注册赠送积分活动 1843754
关于科研通互助平台的介绍 1691516