Martini 3 Building Blocks for Lipid Nanoparticle Design
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články
PubMed
41407294
PubMed Central
PMC12857740
DOI
10.1021/acs.jctc.5c01207
Knihovny.cz E-zdroje
- MeSH
- lipidy * chemie MeSH
- nanočástice * chemie MeSH
- simulace molekulární dynamiky * MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- lipidy * MeSH
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.
Department of Physics Pukyong National University 48513 Busan Republic of Korea
Department of Physics University of Helsinki P O Box 64 FI 00014 Helsinki Finland
Heidelberg Institute for Theoretical Studies 69118 Heidelberg Germany
Molecular Microbiology and Structural Biochemistry CNRS and University of Lyon 69000 Lyon France
PharmCADD 1102 ho 60 Centum jungang ro Haeundae gu 48059 Busan Republic of Korea
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Kjolbye L. R., Pereira G. P., Bartocci A., Pannuzzo M., Albani S., Marchetto A., Jiménez-García B., Martin J., Rossetti G., Cecchini M., Wu S., Monticelli L., Souza P. C. T.. Towards design of drugs and delivery systems with the Martini coarse-grained model. QRB Discovery. 2022;3:e19. doi: 10.1017/qrd.2022.16. PubMed DOI PMC
Jia Y., Wang X., Li L., Li F., Zhang J., Liang X.-J.. Lipid nanoparticles optimized for targeting and release of nucleic acid. Adv. Mater. 2024;36:2305300. doi: 10.1002/adma.202305300. PubMed DOI
Mehta M., Bui T. A., Yang X., Aksoy Y., Goldys E. M., Deng W.. Lipid-based nanoparticles for drug/gene delivery: An overview of the production techniques and difficulties encountered in their industrial development. ACS Mater. Au. 2023;3:600–619. doi: 10.1021/acsmaterialsau.3c00032. PubMed DOI PMC
Cárdenas M., Campbell R. A., Yanez Arteta M., Lawrence M. J., Sebastiani F.. Review of structural design guiding the development of lipid nanoparticles for nucleic acid delivery. Curr. Opin. Colloid Interface Sci. 2023;66:101705. doi: 10.1016/j.cocis.2023.101705. DOI
Philipp J., Dabkowska A., Reiser A., Frank K., Krzysztoń R., Brummer C., Nickel B., Blanchet C. E., Sudarsan A., Ibrahim M., Johansson S., Skantze P., Skantze U., Östman S., Johansson M., Henderson N., Elvevold K., Smedsrød B., Schwierz N., Lindfors L., Rädler J. O.. pH-dependent structural transitions in cationic ionizable lipid mesophases are critical for lipid nanoparticle function. Proc. Natl. Acad. Sci. U.S.A. 2023;120:e2310491120. doi: 10.1073/pnas.2310491120. PubMed DOI PMC
Schlich M., Palomba R., Costabile G., Mizrahy S., Pannuzzo M., Peer D., Decuzzi P.. Cytosolic delivery of nucleic acids: The case of ionizable lipid nanoparticles. Bioeng. Transl. Med. 2021;6:e10213. doi: 10.1002/btm2.10213. PubMed DOI PMC
Digiacomo L., Renzi S., Quagliarini E., Pozzi D., Amenitsch H., Ferri G., Pesce L., De Lorenzi V., Matteoli G., Cardarelli F., Caracciolo G.. Investigating the mechanism of action of dna-loaded pegylated lipid nanoparticles. Nanomed. Nanotechnol. Biol. Med. 2023;53:102697. doi: 10.1016/j.nano.2023.102697. PubMed DOI
Hald Albertsen C., Kulkarni J. A., Witzigmann D., Lind M., Petersson K., Simonsen J. B.. The role of lipid components in lipid nanoparticles for vaccines and gene therapy. Adv. Drug Delivery Rev. 2022;188:114416. doi: 10.1016/j.addr.2022.114416. PubMed DOI PMC
Li W., Szoka F. C.. Lipid-based nanoparticles for nucleic acid delivery. Pharm. Res. 2007;24:438–449. doi: 10.1007/s11095-006-9180-5. PubMed DOI
Kiaie S. H., Majidi Zolbanin N., Ahmadi A., Bagherifar R., Valizadeh H., Kashanchi F., Jafari R.. Recent advances in mRNA-LNP therapeutics: immunological and pharmacological aspects. J. Nanobiotechnol. 2022;20:276. doi: 10.1186/s12951-022-01478-7. PubMed DOI PMC
Agarwal V., Kelley D. R.. The genetic and biochemical determinants of mRNA degradation rates in mammals. Genome Biol. 2022;23:245. doi: 10.1186/s13059-022-02811-x. PubMed DOI PMC
Moayedpour S., Broadbent J., Riahi S., Bailey M., V Thu H., Dobchev D., Balsubramani A., N D Santos R., Kogler-Anele L., Corrochano-Navarro A., Li S., U Montoya F., Agarwal V., Bar-Joseph Z., Jager S.. Representations of lipid nanoparticles using large language models for transfection efficiency prediction. Bioinformatics. 2024;40:btae342. doi: 10.1093/bioinformatics/btae342. PubMed DOI PMC
Chatterjee S., Kon E., Sharma P., Peer D.. Endosomal escape: A bottleneck for LNP-mediated therapeutics. Proc. Natl. Acad. Sci. U.S.A. 2024;121:e2307800120. doi: 10.1073/pnas.2307800120. PubMed DOI PMC
Yanez Arteta M., Kjellman T., Bartesaghi S., Wallin S., Wu X., Kvist A. J., Dabkowska A., Székely N., Radulescu A., Bergenholtz J., Lindfors L.. Successful reprogramming of cellular protein production through mRNA delivered by functionalized lipid nanoparticles. Proc. Natl. Acad. Sci. U.S.A. 2018;115:E3351–E3360. doi: 10.1073/pnas.1720542115. PubMed DOI PMC
Paloncýová M., Šrejber M., Čechová P., Kührová P., Zaoral F., Otyepka M.. Atomistic Insights into Organization of RNA-Loaded Lipid Nanoparticles. J. Phys. Chem. B. 2023;127:1158–1166. doi: 10.1021/acs.jpcb.2c07671. PubMed DOI
Paloncýová M., Čechová P., Šrejber M., Kührová P., Otyepka M.. Role of Ionizable Lipids in SARS-CoV-2 Vaccines As Revealed by Molecular Dynamics Simulations: From Membrane Structure to Interaction with mRNA Fragments. J. Phys. Chem. Lett. 2021;12:11199–11205. doi: 10.1021/acs.jpclett.1c03109. PubMed DOI
Ramezanpour M., Tieleman D. P.. Computational Insights into the Role of Cholesterol in Inverted Hexagonal Phase Stabilization and Endosomal Drug Release. Langmuir. 2022;38:7462–7471. doi: 10.1021/acs.langmuir.2c00430. PubMed DOI PMC
Kulkarni J. A., Darjuan M. M., Mercer J. E., Chen S., Van Der Meel R., Thewalt J. L., Tam Y. Y. C., Cullis P. R.. On the Formation and Morphology of Lipid Nanoparticles Containing Ionizable Cationic Lipids and siRNA. ACS Nano. 2018;12:4787–4795. doi: 10.1021/acsnano.8b01516. PubMed DOI
Trollmann M. F., Böckmann R. A.. mRNA lipid nanoparticle phase transition. Biophys. J. 2022;121:3927–3939. doi: 10.1016/j.bpj.2022.08.037. PubMed DOI PMC
Park S., Choi Y. K., Kim S., Lee J., Im W.. CHARMM-GUI Membrane Builder for Lipid Nanoparticles with Ionizable Cationic Lipids and PEGylated Lipids. J. Chem. Inf. Model. 2021;61:5192–5202. doi: 10.1021/acs.jcim.1c00770. PubMed DOI PMC
Cornebise M., Narayanan E., Xia Y., Acosta E., Ci L., Koch H., Milton J., Sabnis S., Salerno T., Benenato K. E.. Discovery of a Novel Amino Lipid That Improves Lipid Nanoparticle Performance through Specific Interactions with mRNA. Adv. Funct. Mater. 2022;32:2106727. doi: 10.1002/adfm.202106727. DOI
Leung A. K., Hafez I. M., Baoukina S., Belliveau N. M., Zhigaltsev I. V., Afshinmanesh E., Tieleman D. P., Hansen C. L., Hope M. J., Cullis P. R.. Lipid nanoparticles containing siRNA synthesized by microfluidic mixing exhibit an electron-dense nanostructured core. J. Phys. Chem. C. 2012;116:18440–18450. doi: 10.1021/jp303267y. PubMed DOI PMC
Bruininks B. M. H., Souza P. C. T., Ingolfsson H., Marrink S. J.. A molecular view on the escape of lipoplexed dna from the endosome. eLife. 2020;9:e52012. doi: 10.7554/elife.52012. PubMed DOI PMC
Marrink S. J., Risselada H. J., Yefimov S., Tieleman D. P., de Vries A. H.. The MARTINI force field: Coarse grained model for biomolecular simulations. J. Phys. Chem. B. 2007;111:7812–7824. doi: 10.1021/jp071097f. PubMed DOI
Marrink S. J., Monticelli L., Melo M. N., Alessandri R., Tieleman D. P., Souza P. C. T.. Two decades of martini: Better beads, broader scope. WIREs Comput Mol Sci. 2023;13:e1620. doi: 10.1002/wcms.1620. DOI
Marrink S. J., Corradi V., Souza P. C. T., Ingólfsson H. I., Tieleman D. P., Sansom M. S.. Computational modeling of realistic cell membranes. Chem. Rev. 2019;119:6184–6226. doi: 10.1021/acs.chemrev.8b00460. PubMed DOI PMC
Alessandri R., Grünewald F., Marrink S. J.. The martini model in materials science. Adv. Mater. 2021;33:2008635. doi: 10.1002/adma.202008635. PubMed DOI PMC
Machado N., Bruininks B. M., Singh P., Dos Santos L., Dal Pizzol C., Dieamant G. d. C., Kruger O., Martin A. A., Marrink S. J., Souza P. C. T.. et al. Complex nanoemulsion for vitamin delivery: droplet organization and interaction with skin membranes. Nanoscale. 2022;14:506–514. doi: 10.1039/D1NR04610A. PubMed DOI
Cao Y., Zhu J., Kou J., Tieleman D. P., Liang Q.. Unveiling interactions of tumor-targeting nanoparticles with lipid bilayers using a titratable martini model. J. Chem. Theory Comput. 2024;20:4045–4053. doi: 10.1021/acs.jctc.4c00231. PubMed DOI
Souza P. C. T., Alessandri R., Barnoud J., Thallmair S., Faustino I., Grünewald F., Patmanidis I., Abdizadeh H., Bruininks B. M., Wassenaar T. A., Kroon P. C., Melcr J., Nieto V., Corradi V., Khan H. M., Domański J., Javanainen M., Martinez-Seara H., Reuter N., Best R. B., Vattulainen I., Monticelli L., Periole X., Tieleman D. P., de Vries A. H., Marrink S. J.. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat. Methods. 2021;18(4):382–388. doi: 10.1038/s41592-021-01098-3. PubMed DOI PMC
Klamt A.. The cosmo and cosmo-rs solvation models. WIREs Comput Mol Sci. 2011;1:699–709. doi: 10.1002/wcms.56. DOI
Işık M., Bergazin T. D., Fox T., Rizzi A., Chodera J. D., Mobley D. L.. Assessing the accuracy of octanol–water partition coefficient predictions in the sampl6 part ii log p challenge. J. Comput.-Aided Mol. Des. 2020;34:335–370. doi: 10.1007/s10822-020-00295-0. PubMed DOI PMC
Bergazin T. D., Tielker N., Zhang Y., Mao J., Gunner M. R., Francisco K., Ballatore C., Kast S. M., Mobley D. L.. Evaluation of log P, pK a, and log D predictions from the SAMPL7 blind challenge. J. Comput.-Aided Mol. Des. 2021;35:771–802. doi: 10.1007/s10822-021-00397-3. PubMed DOI PMC
Sangster J.. Octanol-Water Partition Coefficients of Simple Organic Compounds. J. Phys. Chem. Ref. Data. 1989;18:1111–1229. doi: 10.1063/1.555833. DOI
Hassett K. J., Benenato K. E., Jacquinet E., Lee A., Woods A., Yuzhakov O., Himansu S., Deterling J., Geilich B. M., Ketova T., Mihai C., Lynn A., McFadyen I., Moore M. J., Senn J. J., Stanton M. G., Almarsson A. ~., Ciaramella G., Brito L. A.. “Optimization of Lipid Nanoparticles for Intramuscular Administration of mRNA Vaccines,” Molecular therapy. Nucleic Acids. 2019;15:1–11. doi: 10.1016/j.omtn.2019.01.013. PubMed DOI PMC
Sabnis S., Kumarasinghe E. S., Salerno T., Mihai C., Ketova T., Senn J. J., Lynn A., Bulychev A., McFadyen I., Chan J., Almarsson A. ~., Stanton M. G., Benenato K. E.. A Novel Amino Lipid Series for mRNA Delivery: Improved Endosomal Escape and Sustained Pharmacology and Safety in Non-human Primates. Mol. Ther. 2018;26:1509–1519. doi: 10.1016/j.ymthe.2018.03.010. PubMed DOI PMC
Klauda J. B., Venable R. M., Freites J. A., O’Connor J. W., Tobias D. J., Mondragon-Ramirez C., Vorobyov I., MacKerell A. D. J., Pastor R. W.. Update of the charmm all-atom additive force field for lipids: Validation on six lipid types. J. Phys. Chem. B. 2010;114:7830–7843. doi: 10.1021/jp101759q. PubMed DOI PMC
Semple S. C., Akinc A., Chen J., Sandhu A. P., Mui B. L., Cho C. K., Sah D. W., Stebbing D., Crosley E. J., Yaworski E., Hafez I. M., Dorkin J. R., Qin J., Lam K., Rajeev K. G., Wong K. F., Jeffs L. B., Nechev L., Eisenhardt M. L., Jayaraman M., Kazem M., Maier M. A., Srinivasulu M., Weinstein M. J., Chen Q., Alvarez R., Barros S. A., De S., Klimuk S. K., Borland T., Kosovrasti V., Cantley W. L., Tam Y. K., Manoharan M., Ciufolini M. A., Tracy M. A., De Fougerolles A., MacLachlan I., Cullis P. R., Madden T. D., Hope M. J.. Rational design of cationic lipids for siRNA delivery. Nat. Biotechnol. 2010;28(2):172–176. doi: 10.1038/nbt.1602. PubMed DOI
Carrasco M. J., Alishetty S., Alameh M. G., Said H., Wright L., Paige M., Soliman O., Weissman D., Cleveland T. E., Grishaev A., Buschmann M. D.. Ionization and structural properties of mRNA lipid nanoparticles influence expression in intramuscular and intravascular administration. Commun. Biol. 2021;4(1):956. doi: 10.1038/s42003-021-02441-2. PubMed DOI PMC
Ding F., Zhang H., Cui J., Li Q., Yang C.. Boosting ionizable lipid nanoparticle-mediated in vivo mRNA delivery through optimization of lipid amine-head groups. Biomater. Sci. 2021;9:7534–7546. doi: 10.1039/D1BM00866H. PubMed DOI
Cheng X., Lee R. J.. The role of helper lipids in lipid nanoparticles (LNPs) designed for oligonucleotide delivery. Adv. Drug Delivery Rev. 2016;99:129–137. doi: 10.1016/j.addr.2016.01.022. PubMed DOI
Patel S., Ashwanikumar N., Robinson E., Xia Y., Mihai C., Griffith J. P., Hou S., Esposito A. A., Ketova T., Welsher K., Joyal J. L., Almarsson Ö., Sahay G.. Naturally-occurring cholesterol analogues in lipid nanoparticles induce polymorphic shape and enhance intracellular delivery of mRNA. Nat. Commun. 2020;11(1):983. doi: 10.1038/s41467-020-14527-2. PubMed DOI PMC
Eygeris Y., Patel S., Jozic A., Sahay G.. Deconvoluting Lipid Nanoparticle Structure for Messenger RNA Delivery. Nano Lett. 2020;20:4543–4549. doi: 10.1021/acs.nanolett.0c01386. PubMed DOI
Zhang J., Fan H., Levorse D. A., Crocker L. S.. Interaction of cholesterol-conjugated ionizable amino lipids with biomembranes: Lipid polymorphism, structure-activity relationship, and implications for siRNA delivery. Langmuir. 2011;27:9473–9483. doi: 10.1021/la201464k. PubMed DOI
Borges-Araújo L., Borges-Araújo A. C., Ozturk T. N., Ramirez-Echemendia D. P., Fábián B., Carpenter T. S., Thallmair S., Barnoud J., Ingólfsson H. I., Hummer G., Tieleman D. P., Marrink S. J., Souza P. C. T., Melo M. N.. Martini 3 coarse-grained force field for cholesterol. J. Chem. Theory Comput. 2023;19:7387–7404. doi: 10.1021/acs.jctc.3c00547. PubMed DOI
Grünewald F., Alessandri R., Kroon P. C., Monticelli L., Souza P. C. T., Marrink S. J.. Polyply; a python suite for facilitating simulations of macromolecules and nanomaterials. Nat. Commun. 2022;13(2022):68. doi: 10.1038/s41467-021-27627-4. PubMed DOI PMC
Grünewald, F. Material design using martini: Accelerating discovery through coarse-grained simulations, Ph.D. thesis, University of Groningen, 2023.
Ibrahim M., Gilbert J., Heinz M., Nylander T., Schwierz N.. Structural insights on ionizable Dlin-MC3-DMA lipids in DOPC layers by combining accurate atomistic force fields, molecular dynamics simulations and neutron reflectivity. Nanoscale. 2023;15:11647–11656. doi: 10.1039/D3NR00987D. PubMed DOI
Jayaraman M., Ansell S. M., Mui B. L., Tam Y. K., Chen J., Du X., Butler D., Eltepu L., Matsuda S., Narayanannair J. K.. et al. Maximizing the potency of sirna lipid nanoparticles for hepatic gene silencing in vivo. Angew. Chem., Int. Ed. 2012;51:8529–8533. doi: 10.1002/anie.201203263. PubMed DOI PMC
Wassenaar T. A., Ingólfsson H. I., Böckmann R. A., Tieleman D. P., Marrink S. J.. Computational lipidomics with insane: A versatile tool for generating custom membranes for molecular simulations. J. Chem. Theory Comput. 2015;11:2144–2155. doi: 10.1021/acs.jctc.5b00209. PubMed DOI
Pedersen K. B., Ingólfsson H. I., Ramirez-Echemendia D. P., Borges-Araújo L., Andreasen M. D., Empereur-Mot C., Melcr J., Ozturk T. N., Bennett D. W., Kjølbye L. R.. et al. The martini 3 lipidome: expanded and refined parameters improve lipid phase behavior. ACS Cent. Sci. 2025;11:1598. doi: 10.1021/acscentsci.5c00755. PubMed DOI PMC
Loura L. M., de Almeida R. F., Silva L. C., Prieto M.. Fret analysis of domain formation and properties in complex membrane systems. Biochim. Biophys. Acta, Biomembr. 2009;1788:209–224. doi: 10.1016/j.bbamem.2008.10.012. PubMed DOI
Hashiba K., Sato Y., Taguchi M., Sakamoto S., Otsu A., Maeda Y., Shishido T., Murakawa M., Okazaki A., Harashima H.. Branching Ionizable Lipids Can Enhance the Stability, Fusogenicity, and Functional Delivery of mRNA. Small Science. 2023;3:2200071. doi: 10.1002/smsc.202200071. PubMed DOI PMC
Li Y., Ye Z., Yang H., Xu Q.. Tailoring combinatorial lipid nanoparticles for intracellular delivery of nucleic acids, proteins, and drugs. Acta Pharm. Sin. B. 2022;12:2624–2639. doi: 10.1016/j.apsb.2022.04.013. PubMed DOI PMC
Brader M. L., Williams S. J., Banks J. M., Hui W. H., Zhou Z. H., Jin L.. Encapsulation state of messenger RNA inside lipid nanoparticles. Biophys. J. 2021;120:2766–2770. doi: 10.1016/j.bpj.2021.03.012. PubMed DOI PMC
Poojari C. S., Scherer K. C., Hub J. S.. Free energies of membrane stalk formation from a lipidomics perspective. Nat. Commun. 2021;12(1):6594. doi: 10.1038/s41467-021-26924-2. PubMed DOI PMC
Ingólfsson H. I., Bhatia H., Zeppelin T., Bennett W. F., Carpenter K. A., Hsu P. C., Dharuman G., Bremer P. T., Schiøtt B., Lightstone F. C., Carpenter T. S.. Capturing Biologically Complex Tissue-Specific Membranes at Different Levels of Compositional Complexity. J. Phys. Chem. B. 2020;124:7819–7829. doi: 10.1021/acs.jpcb.0c03368. PubMed DOI PMC
Tarahovsky Y. S., Koynova R., MacDonald R. C.. Dna release from lipoplexes by anionic lipids: Correlation with lipid mesomorphism, interfacial curvature, and membrane fusion. Biophys. J. 2004;87:1054–1064. doi: 10.1529/biophysj.104.042895. PubMed DOI PMC
Kobayashi T., Beuchat M.-H., Chevallier J., Makino A., Mayran N., Escola J.-M., Lebrand C., Cosson P., Kobayashi T., Gruenberg J.. Separation and characterization of late endosomal membrane domains. J. Biol. Chem. 2002;277:32157–32164. doi: 10.1074/jbc.M202838200. PubMed DOI
Markin V. S., Kozlov M. M., Borovjagin V. L.. On the theory of membrane fusion. The stalk mechanism. Gen. Physiol. Biophys. 1984;3:361–377. PubMed
Chernomordik L. V., Kozlov M. M.. Mechanics of membrane fusion. Nat. Struct. Mol. Biol. 2008;15:675–683. doi: 10.1038/nsmb.1455. PubMed DOI PMC
Yanez Arteta M., Kjellman T., Bartesaghi S., Wallin S., Wu X., Kvist A. J., Dabkowska A., Székely N., Radulescu A., Bergenholtz J.. et al. Successful reprogramming of cellular protein production through mrna delivered by functionalized lipid nanoparticles. Proc. Natl. Acad. Sci. U.S.A. 2018;115:E3351–E3360. doi: 10.1073/pnas.1720542115. PubMed DOI PMC
Sebastiani F., Yanez Arteta M., Lerche M., Porcar L., Lang C., Bragg R. A., Elmore C. S., Krishnamurthy V. R., Russell R. A., Darwish T.. et al. Apolipoprotein e binding drives structural and compositional rearrangement of mrna-containing lipid nanoparticles. ACS Nano. 2021;15:6709–6722. doi: 10.1021/acsnano.0c10064. PubMed DOI PMC
Yan Y., Liu X., Wang L., Wu C., Shuai Q., Zhang Y., Liu S.. Branched hydrophobic tails in lipid nanoparticles enhance mRNA delivery for cancer immunotherapy. Biomaterials. 2023;301:122279. doi: 10.1016/j.biomaterials.2023.122279. PubMed DOI
Miao L., Lin J., Huang Y., Li L., Delcassian D., Ge Y., Shi Y., Anderson D. G.. Synergistic lipid compositions for albumin receptor mediated delivery of mRNA to the liver. Nat. Commun. 2020;11(1):2424. doi: 10.1038/s41467-020-16248-y. PubMed DOI PMC
Kasson P. M., Lindahl E., Pande V. S.. Atomic-resolution simulations predict a transition state for vesicle fusion defined by contact of a few lipid tails. PLoS Comput. Biol. 2010;6:e1000829. doi: 10.1371/journal.pcbi.1000829. PubMed DOI PMC
Viger-Gravel J., Schantz A., Pinon A. C., Rossini A. J., Schantz S., Emsley L.. Structure of Lipid Nanoparticles Containing siRNA or mRNA by Dynamic Nuclear Polarization-Enhanced NMR Spectroscopy. J. Phys. Chem. B. 2018;122:2073–2081. doi: 10.1021/acs.jpcb.7b10795. PubMed DOI
Schoenmaker L., Witzigmann D., Kulkarni J. A., Verbeke R., Kersten G., Jiskoot W., Crommelin D. J.. mRNA-lipid nanoparticle COVID-19 vaccines: Structure and stability. Int. J. Pharm. 2021;601:120586. doi: 10.1016/j.ijpharm.2021.120586. PubMed DOI PMC
Pezeshkian W., König M., Wassenaar T. A., Marrink S. J.. Backmapping triangulated surfaces to coarse-grained membrane models. Nat. Commun. 2020;11(2020):2296. doi: 10.1038/s41467-020-16094-y. PubMed DOI PMC
Martinez L., Andrade R., Birgin E. G., Martínez J. M.. PACKMOL: A package for building initial configurations for molecular dynamics simulations. J. Comput. Chem. 2009;30:2157–2164. doi: 10.1002/jcc.21224. PubMed DOI
Kent, B. R. 3D Scientific Visualization with Blender®, 2053–2571; Morgan & Claypool Publishers, 2015.
Bruininks B. M. H., Wassenaar T. A., Vattulainen I.. Unbreaking Assemblies in Molecular Simulations with Periodic Boundaries. J. Chem. Inf. Model. 2023;63:3448–3452. doi: 10.1021/acs.jcim.2c01574. PubMed DOI PMC
Bruininks, B. M. H. , GitHubBartBruininks/mdvcontainment: Robust Characterization of Inside and Outside in Periodic Spaces. github.com, https://github.com/BartBruininks/mdvcontainment. [Accessed 01 08, 2024].
Humphrey W., Dalke A., Schulten K.. VMD: Visual molecular dynamics. J. Mol. Graphics. 1996;14:33–38. doi: 10.1016/0263-7855(96)00018-5. PubMed DOI
Mui B. L., Tam Y. K., Jayaraman M., Ansell S. M., Du X., Tam Y. Y. C., Lin P. J., Chen S., Narayanannair J. K.. et al. Influence of polyethylene glycol lipid desorption rates on pharmacokinetics and pharmacodynamics of sirna lipid nanoparticles. Mol. Ther.Nucleic Acids. 2013;2:e139. doi: 10.1038/mtna.2013.66. PubMed DOI PMC
Leikin S. L., Kozlov M. M., Chernomordik L. V., Markin V. S., Chizmadzhev Y. A.. Membrane fusion: Overcoming of the hydration barrier and local restructuring. J. Theor. Biol. 1987;129:411–425. doi: 10.1016/S0022-5193(87)80021-8. PubMed DOI
Stevens J. A., Grünewald F., van Tilburg P., König M., Gilbert B., Brier T., Thornburg Z., Luthey-Schulten Z., Marrink S.. Molecular dynamics simulation of an entire cell. Front. Chem. 2023;11:1106495. doi: 10.3389/fchem.2023.1106495. PubMed DOI PMC
Andreasen M. D., Souza P. C. T., Schiøtt B., Zuzic L.. Creating coarse-grained systems with coby: Towards higher accuracy in membrane complexity. bioRxiv. 2024:604601. doi: 10.1101/2024.07.23.604601. PubMed DOI PMC
Bruininks B. M. H., Thie A. S., Souza P. C. T., Wassenaar T. A., Faraji S., Marrink S. J.. Sequential Voxel-Based Leaflet Segmentation of Complex Lipid Morphologies. J. Chem. Theory Comput. 2021;17:7873–7885. doi: 10.1021/acs.jctc.1c00446. PubMed DOI PMC
Ollila O. S., Pabst G.. Atomistic resolution structure and dynamics of lipid bilayers in simulations and experiments. Biochim. Biophys. Acta, Biomembr. 2016;1858:2512–2528. doi: 10.1016/j.bbamem.2016.01.019. PubMed DOI
Javanainen M., Heftberger P., Madsen J. J., Miettinen M. S., Pabst G., Ollila O. H. S.. Quantitative comparison against experiments reveals imperfections in force fields’ descriptions of popc–cholesterol interactions. J. Chem. Theory Comput. 2023;19:6342–6352. doi: 10.1021/acs.jctc.3c00648. PubMed DOI PMC
Lee J., Patel D. S., Ståhle J., Park S. J., Kern N. R., Kim S., Lee J., Cheng X., Valvano M. A., Holst O., Knirel Y. A., Qi Y., Jo S., Klauda J. B., Widmalm G., Im W.. CHARMM-GUI Membrane Builder for Complex Biological Membrane Simulations with Glycolipids and Lipoglycans. J. Chem. Theory Comput. 2019;15:775–786. doi: 10.1021/acs.jctc.8b01066. PubMed DOI
Dettmann L. F., Kühn O., Ahmed A. A.. Martini-based coarse-grained soil organic matter model derived from atomistic simulations. J. Chem. Theory Comput. 2024;20:5291–5305. doi: 10.1021/acs.jctc.4c00332. PubMed DOI
Bereau T., Kremer K.. Automated parametrization of the coarse-grained martini force field for small organic molecules. J. Chem. Theory Comput. 2015;11:2783–2791. doi: 10.1021/acs.jctc.5b00056. PubMed DOI
Stroh K. S., Souza P. C. T., Monticelli L., Risselada H. J.. Cgcompiler: Automated coarse-grained molecule parametrization via noise-resistant mixed-variable optimization. J. Chem. Theory Comput. 2023;19:8384–8400. doi: 10.1021/acs.jctc.3c00637. PubMed DOI PMC
Empereur-mot C., Pedersen K. B., Capelli R., Crippa M., Caruso C., Perrone M., Souza P. C. T., Marrink S. J., Pavan G. M.. Automatic optimization of lipid models in the martini force field using swarmcg. J. Chem. Inf. Model. 2023;63:3827–3838. doi: 10.1021/acs.jcim.3c00530. PubMed DOI PMC
Pereira G. P., Alessandri R., Domínguez M., Araya-Osorio R., Grünewald L., Borges-Araújo L., Wu S., Marrink S. J., Souza P. C. T., Mera-Adasme R.. Bartender: Martini 3 bonded terms via quantum mechanics-based molecular dynamics. J. Chem. Theory Comput. 2024;20:5763–5773. doi: 10.1021/acs.jctc.4c00275. PubMed DOI
Paloncýová M., Pykal M., Kührová P., Banáš P., Šponer J., Otyepka M.. Computer aided development of nucleic acid applications in nanotechnologies. Small. 2022;18:2204408. doi: 10.1002/smll.202204408. PubMed DOI
Aho N., Buslaev P., Jansen A., Bauer P., Groenhof G., Hess B.. Scalable constant ph molecular dynamics in gromacs. J. Chem. Theory Comput. 2022;18:6148–6160. doi: 10.1021/acs.jctc.2c00516. PubMed DOI PMC
Santos H. A. F., Vila-Viçosa D., Teixeira V. H., Baptista A. M., Machuqueiro M.. Constant-ph md simulations of dmpa/dmpc lipid bilayers. J. Chem. Theory Comput. 2015;11:5973–5979. doi: 10.1021/acs.jctc.5b00956. PubMed DOI
Trollmann M. F. W., Böckmann R. A.. Decoding ph-driven phase transition of lipid nanoparticles. bioRxiv. 2024:625717. doi: 10.1101/2024.11.27.625717. PubMed DOI
Trollmann M. F. W., Rossetti P., Böckmann R. A.. Constant-ph md simulations of lipids. BioRxiv. 2024:627182. doi: 10.1101/2024.12.06.627182. DOI
Grünewald F., Souza P. C. T., Abdizadeh H., Barnoud J., de Vries A. H., Marrink S. J.. Titratable Martini model for constant pH simulations. J. Chem. Phys. 2020;153:024118. doi: 10.1063/5.0014258. PubMed DOI
Koltover I., Salditt T., Radler J. O., Safinya C. R.. An inverted hexagonal phase of cationic liposome-dna complexes related to dna release and delivery. Science. 1998;281:78–81. doi: 10.1126/science.281.5373.78. PubMed DOI
König M., de Vries R., Grünewald F., Marrink S., Pezeshkian W.. Curvature-induced lipid sorting beyond the critical packing parameter. bioRxiv. 2023:571845. doi: 10.1101/2023.12.15.571845. DOI
Hub J. S.. Joint reaction coordinate for computing the free-energy landscape of pore nucleation and pore expansion in lipid membranes. J. Chem. Theory Comput. 2021;17:1229–1239. doi: 10.1021/acs.jctc.0c01134. PubMed DOI
Bennett W. D., Tieleman D. P.. Water defect and pore formation in atomistic and coarse-grained lipid membranes: Pushing the limits of coarse graining. J. Chem. Theory Comput. 2011;7:2981–2988. doi: 10.1021/ct200291v. PubMed DOI
Lee S. M., Cheng Q., Yu X., Liu S., Johnson L. T., Siegwart D. J.. A systematic study of unsaturation in lipid nanoparticles leads to improved mrna transfection in vivo. Angew. Chem., Int. Ed. 2021;60:5848–5853. doi: 10.1002/anie.202013927. PubMed DOI PMC
Hou X., Zaks T., Langer R., Dong Y.. Lipid nanoparticles for mrna delivery. Nat. Rev. Mater. 2021;6:1078–1094. doi: 10.1038/s41578-021-00358-0. PubMed DOI PMC
Kon E., Ad-El N., Hazan-Halevy I., Stotsky-Oterin L., Peer D.. Targeting cancer with mrna–lipid nanoparticles: key considerations and future prospects. Nat. Rev. Clin. Oncol. 2023;20:739–754. doi: 10.1038/s41571-023-00811-9. PubMed DOI
Abraham M. J., Murtola T., Schulz R., Páll S., Smith J. C., Hess B., Lindahl E.. Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1–2:19–25. doi: 10.1016/j.softx.2015.06.001. DOI
Gowers, R. J. ; Linke, M. ; Barnoud, J. ; Reddy, T. J. E. ; Melo, M. N. ; Seyler, S. L. ; Domanski, J. ; Dotson, D. L. ; Buchoux, S. ; Kenney, I. M. ; Beckstein, O. , MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations, Proceedings of the 15th Python in Science Conference, 2019; pp 98–105.
Michaud-Agrawal N., Denning E. J., Woolf T. B., Beckstein O.. MDAnalysis: A toolkit for the analysis of molecular dynamics simulations. J. Comput. Chem. 2011;32:2319–2327. doi: 10.1002/jcc.21787. PubMed DOI PMC
Harris C. R., Millman K. J., van der Walt S. J., Gommers R., Virtanen P., Cournapeau D., Wieser E., Taylor J., Berg S., Smith N. J., Kern R., Picus M., Hoyer S., van Kerkwijk M. H., Brett M., Haldane A., del Río J. F., Wiebe M., Peterson P., Gérard-Marchant P., Sheppard K., Reddy T., Weckesser W., Abbasi H., Gohlke C., Oliphant T. E.. Array programming with NumPy. Nature. 2020;585(7825):357–362. doi: 10.1038/s41586-020-2649-2. PubMed DOI PMC
Hunter J. D.. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 2007;9:90–95. doi: 10.1109/MCSE.2007.55. DOI
Buchoux S.. FATSLiM: a fast and robust software to analyze MD simulations of membranes. Bioinformatics. 2017;33:133–134. doi: 10.1093/bioinformatics/btw563. PubMed DOI
Humphrey W., Dalke A., Schulten K.. VMD – Visual Molecular Dynamics. J. Mol. Graphics. 1996;14:33–38. doi: 10.1016/0263-7855(96)00018-5. PubMed DOI
Stone, J. ; Gullingsrud, J. ; Grayson, P. , Schulten, K. , A system for interactive molecular dynamics simulation. In 2001 ACM Symposium on Interactive 3D Graphics, Hughes, J. F. , Séquin, C·H. , Eds.; ACM SIGGRAPH, New York, 2001, pp 191–194.
Rdkit: Open-Source Cheminformatics, https://www.rdkit.org.
Alessandri R., Barnoud J., Gertsen A. S., Patmanidis I., de Vries A. H., Souza P. C. T., Marrink S. J.. Martini 3 coarse-grained force field: Small molecules. Adv. Theory Simul. 2022;5:2100391. doi: 10.1002/adts.202100391. DOI
Alessandri, R. ; Thallmair, S. ; Herrero, C. G. ; Mera-Adasme, R. ; Marrink, S. J. ; Souza, P. C. T. . A Practical Introduction to Martini 3 and its Application to Protein-Ligand Binding Simulations. In A Practical Guide to Recent Advances in Multiscale Modeling and Simulation of Biomolecules; AIP Publishing LLC.
Dodda L. S., De Vaca I. C., Tirado-Rives J., Jorgensen W. L.. LigParGen web server: an automatic OPLS-AA parameter generator for organic ligands. Nucleic Acids Res. 2017;45:W331–W336. doi: 10.1093/nar/gkx312. PubMed DOI PMC
Dodda L. S., Vilseck J. Z., Tirado-Rives J., Jorgensen W. L.. 1.14*CM1A-LBCC: Localized Bond Charge Corrected CM1A Charges for Condensed-Phase Simulations. J. Phys. Chem. B. 2017;121:3864. doi: 10.1021/acs.jpcb.7b00272. PubMed DOI PMC
Berendsen H. J. C., Postma J. P. M., Gunsteren W. F. V., Dinola A., Haak J. R.. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 1984;81:3684–3690. doi: 10.1063/1.448118. DOI
Bussi G., Donadio D., Parrinello M.. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007;126:014101. doi: 10.1063/1.2408420. PubMed DOI
Parrinello M., Rahman A.. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 1981;52:7182–7190. doi: 10.1063/1.328693. DOI
Darden T., York D., Pedersen L.. Particle mesh Ewald: An N·log(N) method for Ewald sums in large systems. J. Chem. Phys. 1993;98:10089–10092. doi: 10.1063/1.464397. DOI
Páll S., Hess B.. A flexible algorithm for calculating pair interactions on SIMD architectures. Comput. Phys. Commun. 2013;184:2641–2650. doi: 10.1016/j.cpc.2013.06.003. DOI
Hess B., Bekker H., Berendsen H. J. C., Fraaije J. G. E. M.. LINCS: A Linear Constraint Solver for molecular simulations. J. Comput. Chem. 1997;18:1463–1472. doi: 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H. DOI
de Jong D. H., Baoukina S., Ingólfsson H. I., Marrink S. J.. Martini straight: Boosting performance using a shorter cutoff and GPUs. Comput. Phys. Commun. 2016;199:1–7. doi: 10.1016/j.cpc.2015.09.014. DOI
Bannwarth C., Ehlert S., Grimme S.. Gfn2-xtban accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions. J. Chem. Theory Comput. 2019;15:1652–1671. doi: 10.1021/acs.jctc.8b01176. PubMed DOI
Bannwarth C., Caldeweyher E., Ehlert S., Hansen A., Pracht P., Seibert J., Spicher S., Grimme S.. Extended tight-binding quantum chemistry methods. WIREs Comput Mol Sci. 2021;11:e1493. doi: 10.1002/wcms.1493. DOI
Becke A. D.. Density-functional exchange-energy approximation with correct asymptotic behavior. Phys. Rev. A. 1988;38:3098–3100. doi: 10.1103/PhysRevA.38.3098. PubMed DOI
Perdew J. P.. Density-functional approximation for the correlation energy of the inhomogeneous electron gas. Phys. Rev. B. 1986;33:8822–8824. doi: 10.1103/PhysRevB.33.8822. PubMed DOI
Weigend F., Ahlrichs R.. Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for h to rn: Design and assessment of accuracy. Phys. Chem. Chem. Phys. 2005;7:3297–3305. doi: 10.1039/b508541a. PubMed DOI
Grimme S., Antony J., Ehrlich S., Krieg H.. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 2010;132:154104. doi: 10.1063/1.3382344. PubMed DOI
Eichkorn K., Weigend F., Treutler O., Ahlrichs R.. Auxiliary basis sets for main row atoms and transition metals and their use to approximate coulomb potentials. Theor. Chem. Acc. 1997;97:119–124. doi: 10.1007/s002140050244. DOI
Balasubramani S., Chen G., Coriani S., Diedenhofen M., Frank M., Franzke Y., Furche F., Grotjahn R., Harding M., Hättig C., Hellweg A., Helmich-Paris B., Holzer C., Huniar U., Kaupp M., Marefat Khah A., Karbalaei Khani S., Müller T., Mack F., Nguyen B., Parker S., Perlt E., Rappoport D., Reiter K., Roy S., Rückert M., Schmitz G., Sierka M., Tapavicza E., Tew D., van Wüllen C., Voora V., Weigend F., Wodyński A., Yu J.. Turbomole: Modular program suite for ab initio quantum-chemical and condensed-matter simulations. J. Chem. Phys. 2020;152:184107. doi: 10.1063/5.0004635. PubMed DOI PMC
Kirkwood J. G.. Statistical Mechanics of Fluid Mixtures. J. Chem. Phys. 1935;3:300–313. doi: 10.1063/1.1749657. DOI
MacCallum J. L., Tieleman D. P.. Structures of neat and hydrated 1-octanol from computer simulations. J. Am. Chem. Soc. 2002;124:15085–15093. doi: 10.1021/ja027422o. PubMed DOI
DeBolt S. E., Kollman P. A.. Investigation of Structure, Dynamics, and Solvation in 1-Octanol and Its Water-Saturated Solution: Molecular Dynamics and Free-Energy Perturbation Studies. J. Am. Chem. Soc. 1995;117:5316–5340. doi: 10.1021/ja00124a015. DOI
Goga N., Rzepiela A. J., de Vries A. H., Marrink S. J., Berendsen H. J.. Efficient algorithms for langevin and DPD dynamics. J. Chem. Theory Comput. 2012;8:3637–3649. doi: 10.1021/ct3000876. PubMed DOI
Shirts M. R., Chodera J. D.. Statistically optimal analysis of samples from multiple equilibrium states. J. Chem. Phys. 2008;129:124105. doi: 10.1063/1.2978177. PubMed DOI PMC
Lim J. B., Rogaski B., Klauda J. B.. Update of the cholesterol force field parameters in charmm. J. Phys. Chem. B. 2012;116:203–210. doi: 10.1021/jp207925m. PubMed DOI
Marrink S. J., de Vries A. H., Harroun T. A., Katsaras J., Wassall S. R.. Cholesterol shows preference for the interior of polyunsaturated lipid membranes. J. Am. Chem. Soc. 2008;130:10–11. doi: 10.1021/ja076641c. PubMed DOI
Melo M. N., Ingólfsson H. I., Marrink S. J.. Parameters for Martini sterols and hopanoids based on a virtual-site description. J. Chem. Phys. 2015;143:243152. doi: 10.1063/1.4937783. PubMed DOI
Zhang Y., Maginn E. J.. A comparison of methods for melting point calculation using molecular dynamics simulations. J. Chem. Phys. 2012;136:144116. doi: 10.1063/1.3702587. PubMed DOI
Tsanai M., Frederix P. J. M., Schroer C. F. E., Souza P. C. T., Marrink S. J.. Coacervate formation studied by explicit solvent coarse-grain molecular dynamics with the martini model. Chem. Sci. 2021;12:8521–8530. doi: 10.1039/D1SC00374G. PubMed DOI PMC
Ingólfsson H. I., Rizuan A., Liu X., Mohanty P., Souza P. C., Marrink S. J., Bowers M. T., Mittal J., Berry J.. Multiscale simulations reveal tdp-43 molecular-level interactions driving condensation. Biophys. J. 2023;122:4370–4381. doi: 10.1016/j.bpj.2023.10.016. PubMed DOI PMC
Uusitalo J. J., Ingólfsson H. I., Akhshi P., Tieleman D. P., Marrink S. J.. Martini coarse-grained force field: Extension to dna. J. Chem. Theory Comput. 2015;11:3932–3945. doi: 10.1021/acs.jctc.5b00286. PubMed DOI
Uusitalo J. J., Ingólfsson H. I., Marrink S. J., Faustino I.. Martini coarse-grained force field: Extension to rna. Biophys. J. 2017;113:246–256. doi: 10.1016/j.bpj.2017.05.043. PubMed DOI PMC
Grünewald F., Punt M. H., Jefferys E. E., Vainikka P. A., König M., Virtanen V., Meyer T. A., Pezeshkian W., Gormley A. J., Karonen M., Sansom M. S. P., Souza P. C. T., Marrink S. J.. Martini 3 coarse-grained force field for carbohydrates. J. Chem. Theory Comput. 2022;18:7555–7569. doi: 10.1021/acs.jctc.2c00757. PubMed DOI PMC
Hub J. S., Awasthi N.. Probing a continuous polar defect: A reaction coordinate for pore formation in lipid membranes. J. Chem. Theory Comput. 2017;13:2352–2366. doi: 10.1021/acs.jctc.7b00106. PubMed DOI
Awasthi, N. ; Hub, J. S. , Free-energy calculations of pore formation in lipid membranes. In Biomembrane Simulations; CRC Press, 2019; pp 109–124.
Kumar S., Rosenberg J. M., Bouzida D., Swendsen R. H., Kollman P. A.. THE weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J. Comput. Chem. 1992;13:1011–1021. doi: 10.1002/jcc.540130812. DOI
Bruininks, B. M. H. ; Souza, P. C. T. ; Marrink, S. J. . A Practical View of the Martini Force Field; Springer New York: New York, NY, 2019; pp 105–127. PubMed
Quemener E., Corvellec M.. SIDUSthe solution for extreme deduplication of an operating system. Linux J. 2013;2013:3. doi: 10.5555/2555789.2555792. DOI