journal article Sep 01, 2025

In silico self-assembly and complexation dynamics of cationic lipids with DNA nanocages to enhance lipofection

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Abstract
DNA nanostructures are promising materials for drug delivery due to their unique topology, shape, size control, biocompatibility, structural stability, and blood-brain-barrier penetration capability. However, their cellular permeability is hindered by strong electrostatic repulsion from negatively charged cellular membranes, posing a significant obstacle to the use of DNA nanostructures as a drug delivery vehicle. Recent experimental studies have shown enhanced cellular uptake for the conjugate binary mixtures of DNA Tetrahedron (TDN) with cationic lipid N-[1-(2,3-dioleyloxy)propyl]-N,N,N-trimethylammonium chloride (DOTMA) compared to TDN alone. However, the cationic DOTMA lipid binding mechanism with the TDN nucleotides is still elusive. Using fully atomistic MD simulations, we aim to understand the molecular interactions that drive the formation and stability of the TDN-DOTMA binary complexes in a physiological environment. Our results uncovered that lipid concentration plays a crucial role in the energetics of the TDN-DOTMA association. We also report that distinct time scales are associated with the self-assembly of cationic DOTMA lipids first, followed by the complexation of self-assembled DOTMA lipid clusters with the TDN nucleotides, where electrostatics, hydrophobicity, and hydrogen bonding are the key interactions that drive the formation and stability of these complexes. Our results provide molecular insights into TDN-DOTMA interactions, highlighting the lipid self-assembly dynamics, complex stability, and morphology, paving the way for the better rational design of cationic lipid-functionalized DNA nanostructures for efficient drug delivery and transfection.
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References
55
[1]
Chem. Commun. 10.1039/b402293a
[2]
Nat. Nanotechnol. (2016) 10.1038/nnano.2016.150
[3]
Nature (1991) 10.1038/350631a0
[4]
Nat. Commun. (2011) 10.1038/ncomms1337
[5]
Nat. Commun. (2011) 10.1038/ncomms1535
[6]
Nanoscale (2017) 10.1039/c6nr08036g
[7]
Phys. Chem. Chem. Phys. (2015) 10.1039/c4cp04547e
[8]
ACS Nano (2016) 10.1021/acsnano.6b03360
[9]
[10]
Nanoscale (2024) 10.1039/d4nr00612g
[11]
Nanoscale Adv. (2022) 10.1039/d1na00753j
[12]
Int. J. Biol. Macromol. (2024) 10.1016/j.ijbiomac.2023.128703
[13]
Cancer Sci. (2020) 10.1111/cas.14548
[14]
ACS Nano (2022) 10.1021/acsnano.2c01382
[15]
ACS Cent. Sci. (2018) 10.1021/acscentsci.8b00383
[16]
Proc. Natl. Acad. Sci. U.S.A. (1989) 10.1073/pnas.86.16.6077
[17]
Biophys. J. (2023) 10.1016/j.bpj.2023.01.031
[18]
Gene Ther. (2000) 10.1038/sj.gt.3301153
[19]
Nanoscale (2023) 10.1039/d2nr05749b
[20]
ACS Appl. Nano Mater. (2023) 10.1021/acsanm.3c02029
[21]
Proc. Natl. Acad. Sci. U.S.A. (1987) 10.1073/pnas.84.21.7413
[22]
Pharm. Res. (2023) 10.1007/s11095-022-03460-2
[23]
Soft Matter (2020) 10.1039/d0sm00736f
[24]
Soft Matter (2022) 10.1039/d2sm00403h
[25]
Int. J. Pharm. (2025) 10.1016/j.ijpharm.2025.125324
[26]
Biomacromolecules (2024) 10.1021/acs.biomac.4c00192
[27]
J. Chem. Inf. Model. (2016) 10.1021/acs.jcim.5b00586
[28]
Comparison of simple potential functions for simulating liquid water

William L. Jorgensen, Jayaraman Chandrasekhar, Jeffry D. Madura et al.

The Journal of Chemical Physics 1983 10.1063/1.445869
[29]
Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K

Pekka Mark, Lennart Nilsson

The Journal of Physical Chemistry A 2001 10.1021/jp003020w
[30]
[31]
Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald

Romelia Salomon-Ferrer, Andreas W. Götz, Duncan Poole et al.

Journal of Chemical Theory and Computation 2013 10.1021/ct400314y
[32]
Biopolymers (2013) 10.1002/bip.22331
[33]
Parmbsc1: a refined force field for DNA simulations

Ivan Ivani, Pablo D Dans, Agnes Noy et al.

Nature Methods 2016 10.1038/nmeth.3658
[34]
J. Chem. Theory Comput. (2014) 10.1021/ct400751u
[35]
Development and testing of a general amber force field

Junmei Wang, Romain M. Wolf, James W. Caldwell et al.

Journal of Computational Chemistry 2004 10.1002/jcc.20035
[36]
A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model

Christopher I. Bayly, Piotr Cieplak, Wendy Cornell et al.

The Journal of Physical Chemistry 1993 10.1021/j100142a004
[37]
Molecular dynamics with coupling to an external bath

H. J. C. Berendsen, J. P. M. Postma, W. F. van Gunsteren et al.

The Journal of Chemical Physics 1984 10.1063/1.448118
[38]
A Leap-frog Algorithm for Stochastic Dynamics

W. F. van Gunsteren, H. J. C. Berendsen

Molecular Simulation 1988 10.1080/08927028808080941
[40]
Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems

Tom Darden, Darrin York, Lee G. Pedersen

The Journal of Chemical Physics 1993 10.1063/1.464397
[41]
J. Chem. Phys. (2009) 10.1063/1.3149788
[42]
Nanoscale (2024) 10.1039/d4nr01104j
[43]
J. Chem. Phys. (1998) 10.1063/1.477127
[44]
Langmuir (2002) 10.1021/la0111203
[45]
AT vs GC binding of protamine-template: A microscopic understanding through molecular dynamics and binding free energies

Sandip Mandal, Khadka B. Chhetri, Yun Hee Jang et al.

The Journal of Chemical Physics 2025 10.1063/5.0272245
[46]
[47]
VMD: Visual molecular dynamics

William Humphrey, Andrew Dalke, Klaus Schulten

Journal of Molecular Graphics 1996 10.1016/0263-7855(96)00018-5
[48]
PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data

Daniel R. Roe, Thomas E. Cheatham

Journal of Chemical Theory and Computation 2013 10.1021/ct400341p
[49]
Matplotlib: A 2D Graphics Environment

John D. Hunter

Computing in Science & Engineering 2007 10.1109/mcse.2007.55
[50]
Imaging α-Hemolysin with Molecular Dynamics: Ionic Conductance, Osmotic Permeability, and the Electrostatic Potential Map

Aleksij Aksimentiev, Klaus Schulten

Biophysical Journal 2005 10.1529/biophysj.104.058727

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Details
Published
Sep 01, 2025
Vol/Issue
20(5)
Funding
Science and Engineering Research Board Award: CRG/2021/003659
Cite This Article
Sandip Mandal, Dhiraj Bhatia, Prabal K. Maiti (2025). In silico self-assembly and complexation dynamics of cationic lipids with DNA nanocages to enhance lipofection. Biointerphases, 20(5). https://doi.org/10.1116/6.0004756
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