» Articles » PMID: 36132649

The Role of Size and Nature in Nanoparticle Binding to a Model Lung Membrane: an Atomistic Study

Overview
Journal Nanoscale Adv
Specialty Biotechnology
Date 2022 Sep 22
PMID 36132649
Authors
Affiliations
Soon will be listed here.
Abstract

Understanding the uptake of nanoparticles (NPs) by different types of cellular membranes plays a pivotal role in the design of NPs for medical applications and in avoiding adverse effects that result in nanotoxicity. Yet, the role of key design parameters, such as the bare NP material, NP size and surface reactivity, and the nature of NP coatings, in membrane remodelling and uptake mechanisms is still very poorly understood, particularly towards the lower range of NP dimensions that are beyond the experimental imaging resolution. The same can be said about the role of a particular membrane composition. Here, we systematically employ biased and unbiased molecular dynamics simulations to calculate the binding energy for three bare materials (Ag/SiO/TiO) and three NP sizes (1/3/5 nm diameter) with a representative lung surfactant membrane, and to study their binding kinetics. The calculated binding energies show that irrespective of size, Ag nanoparticles bind very strongly to the bilayer, while the NPs made of SiO or TiO experience very low to no binding. The unbiased simulations provide insight into how the NPs and membrane affect each other in terms of the solvent-accessible surface area (SASA) of the NPs and the defect types and fluidity of the membrane. Using these systematic fine-grained results in coarsening procedures will pave the way for simulations considering NP sizes that are well beyond the membrane thickness, closer to experimental dimensions, for which different binding characteristics and more significant membrane remodelling are expected.

Citing Articles

Silicene-Based Quantum Dots Nanocomposite Coated Functional UV Protected Textiles With Antibacterial and Antioxidant Properties: A Versatile Solution for Healthcare and Everyday Protection.

Das P, Ganguly S, Marvi P, Hassan S, Sherazee M, Mahana M Adv Healthc Mater. 2025; 14(6):e2404911.

PMID: 39757484 PMC: 11874647. DOI: 10.1002/adhm.202404911.


Polydots, soft nanoparticles, at membrane interfaces.

Wijesinghe S, Junghans C, Perahia D, Grest G RSC Adv. 2023; 13(28):19227-19234.

PMID: 37377875 PMC: 10291257. DOI: 10.1039/d3ra02085a.


A Core-Shell Approach for Systematically Coarsening Nanoparticle-Membrane Interactions: Application to Silver Nanoparticles.

Singhal A, Sevink G Nanomaterials (Basel). 2022; 12(21).

PMID: 36364637 PMC: 9656456. DOI: 10.3390/nano12213859.

References
1.
Baoukina S, Tieleman D . Computer simulations of lung surfactant. Biochim Biophys Acta. 2016; 1858(10):2431-2440. DOI: 10.1016/j.bbamem.2016.02.030. View

2.
Alexander D, Forrer D, Rossi E, Lidorikis E, Agnoli S, Bernasconi G . Electronic Structure-Dependent Surface Plasmon Resonance in Single Au-Fe Nanoalloys. Nano Lett. 2019; 19(8):5754-5761. DOI: 10.1021/acs.nanolett.9b02396. View

3.
Gautier R, Bacle A, Tiberti M, Fuchs P, Vanni S, Antonny B . PackMem: A Versatile Tool to Compute and Visualize Interfacial Packing Defects in Lipid Bilayers. Biophys J. 2018; 115(3):436-444. PMC: 6084522. DOI: 10.1016/j.bpj.2018.06.025. View

4.
Pan Y, Leifert A, Ruau D, Neuss S, Bornemann J, Schmid G . Gold nanoparticles of diameter 1.4 nm trigger necrosis by oxidative stress and mitochondrial damage. Small. 2009; 5(18):2067-76. DOI: 10.1002/smll.200900466. View

5.
Das M, Dahal U, Mesele O, Liang D, Cui Q . Molecular Dynamics Simulation of Interaction between Functionalized Nanoparticles with Lipid Membranes: Analysis of Coarse-Grained Models. J Phys Chem B. 2019; 123(49):10547-10561. DOI: 10.1021/acs.jpcb.9b08259. View