Dataset Information

Role of Ionizable Lipids in SARS-CoV-2 Vaccines As Revealed by Molecular Dynamics Simulations: From Membrane Structure to Interaction with mRNA Fragments.

ABSTRACT: Recent advances in RNA-based medicine have provided new opportunities for the global current challenge, i.e., the COVID-19 pandemic. Novel vaccines are based on a messenger RNA (mRNA) motif with a lipid nanoparticle (LNP) vector, consisting of high content of unique pH-sensitive ionizable lipids (ILs). Here we provide molecular insights into the role of the ILs and lipid mixtures used in current mRNA vaccines. We observed that the lipid mixtures adopted a nonlamellar organization, with ILs separating into a very disordered, pH-sensitive phase. We describe structural differences of the two ILs leading to their different congregation, with implications for the vaccine stability. Finally, as RNA interacts preferentially with IL-rich phases located at the regions with high curvature of lipid phase, local changes in RNA flexibility and base pairing are induced by lipids. A proper atomistic understanding of RNA-lipid interactions may enable rational tailoring of LNP composition for efficient RNA delivery.

SUBMITTER: Paloncyova M

PROVIDER: S-EPMC8609914 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Role of Ionizable Lipids in SARS-CoV-2 Vaccines As Revealed by Molecular Dynamics Simulations: From Membrane Structure to Interaction with mRNA Fragments.

Paloncýová Markéta M Čechová Petra P Šrejber Martin M Kührová Petra P Otyepka Michal M

The journal of physical chemistry letters 20211111 45

Recent advances in RNA-based medicine have provided new opportunities for the global current challenge, i.e., the COVID-19 pandemic. Novel vaccines are based on a messenger RNA (mRNA) motif with a lipid nanoparticle (LNP) vector, consisting of high content of unique pH-sensitive ionizable lipids (ILs). Here we provide molecular insights into the role of the ILs and lipid mixtures used in current mRNA vaccines. We observed that the lipid mixtures adopted a nonlamellar organization, with ILs separ ...[more]

PMID: 34761943

Similar Datasets

Project description:Lipid nanoparticles (LNPs) have emerged as an effective and promising technology for messenger RNA (mRNA) delivery, offering a potential solution to physiological barriers and providing an alternative approach to gene therapy without the drawbacks associated with viral delivery. However, efficiently delivering mRNA remains a significant challenge in nucleic acid-based therapies due to the limitations of current LNP platforms in achieving optimal endosomal escape and mRNA release, which largely relies on finding a suitable ionizable lipid. Additionally, the synthesis of these ionizable lipids involves multiple chemical reactions, often making the process time-consuming and difficult to translate. In this study, we employed a facile, catalyst-free, and versatile one-pot multicomponent reaction (MCR) to develop a library of ionizable lipids featuring a pharmacologically significant tetrahydropyrimidine (THP) backbone, tailored for enhanced mRNA delivery. A library of 26 THP ionizable lipids was systematically synthesized in just 3 h and formulated with luciferase mRNA for initial in vitro screening. The THP LNPs exhibited tunable particle sizes, favorable ζ-potentials, and high encapsulation efficiencies. Among them, THP1 demonstrated the highest transfection efficiency both in vitro and in vivo after intramuscular administration, comparable to DLin-MC3-DMA (MC3), a conventional benchmark. Further optimization of THP1 with phospholipids significantly enhanced intramuscular mRNA delivery and showed sustained protein expression in vivo for up to 5 days. More importantly, it demonstrated successful intravenous delivery in a dose-dependent manner with minimal toxicity, as indicated by hematological, histopathological, and proinflammatory cytokine assessments. Furthermore, THP1 LNPs also demonstrated the ability to edit genes in specific liver tissues in a tdTomato transgenic mouse model, highlighting their precision and utility in targeted therapeutic applications. These findings position THP1 LNPs as promising candidates for advancing mRNA-based therapies, with significant implications for clinical translation in vaccine delivery and CRISPR/Cas9-mediated gene editing in the liver.

Project description: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 Membrane Builder 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 Membrane Builder'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 (KA) of LNP membranes with cationic lipids fit into typical KA 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 Membrane Builder can be useful to better characterize various LNPs with or without genetic drugs for rational LNP design.

Project description:A lipid nanoparticle (LNP) formulation is a state-of-the-art delivery system for genetic drugs such as DNA, mRNA, and siRNA, 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 a rational LNP design almost impossible. In this work, we present an extension of CHARMM-GUI Membrane Builder 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 Membrane Builder â€™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 ( KA ) of LNP membranes with cationic lipids fit into typical KA of fluid-phase membranes. Interestingly, the interactions between PEG oxygen and head group of ionizable cationic lipids induce a negative curvature. We hope that this LNP capability in Membrane Builder can be useful to better characterize various LNPs with or without genetic drugs for a rational LNP design.

Dataset Information

Role of Ionizable Lipids in SARS-CoV-2 Vaccines As Revealed by Molecular Dynamics Simulations: From Membrane Structure to Interaction with mRNA Fragments.

Publications

Role of Ionizable Lipids in SARS-CoV-2 Vaccines As Revealed by Molecular Dynamics Simulations: From Membrane Structure to Interaction with mRNA Fragments.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets