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Cited by 10Polymer Surface Interaction StudiesProteins in Food SystemsCorrosion Behavior and Inhibition

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Multiscale modelling of biomolecular corona formation on metallic surfaces

Open access
Amini, Parinaz Mosaddeghi, Rouse, Ian, Subbotina, Julia, Lobaskin, Vladimir·2024·Beilstein Journal of Nanotechnology
DOI 10.3762/bjnano.15.21PMC PMC10877083

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Abstract

In the realm of food industry, the choice of non-consumable materials used plays a crucial role in ensuring consumer safety and product quality. Aluminum is widely used in food packaging and food processing applications, including dairy products. However, the interaction between aluminum and milk content requires further investigation to understand its implications. In this work, we present the results of multiscale modelling of the interaction between various surfaces, that is (100), (110), and (111), of fcc aluminum with the most abundant milk proteins and lactose. Our approach combines atomistic molecular dynamics, a coarse-grained model of protein adsorption, and kinetic Monte Carlo simulations to predict the protein corona composition in the deposited milk layer on aluminum surfaces. We consider a simplified model of milk, which is composed of the six most abundant milk proteins found in natural cow milk and lactose, which is the most abundant sugar found in dairy. Through our study, we ranked selected proteins and lactose adsorption affinities based on their corresponding interaction strength with aluminum surfaces and predicted the content of the naturally forming biomolecular corona. Our comprehensive investigation sheds light on the implications of aluminum in food processing and packaging, particularly concerning its interaction with the most abundant milk proteins and lactose. By employing a multiscale modelling approach, we simulated the interaction between metallic aluminum surfaces and the proteins and lactose, considering different crystallographic orientations. The results of our study provide valuable insights into the mechanisms of lactose and protein deposition on aluminum surfaces, which can aid in the general understanding of protein corona formation.

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Crossref · 10.3762/bjnano.15.21

Cites

45 of 45 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Schematic representation of the life cycle of dairy products, showcasing potential sources and pathways of contamination. It features the stages of grazing, collection, processing, and packaging. The relevant processes include surface fouling and milk contamination during food transformation as well as the formation of a protein corona on surfaces and nanoparticles after packaging. The figure was created with BioRender.com, https://biorender.com/. This content is not subject to CC BY 4.0.
Figure 1 Schematic representation of the life cycle of dairy products, showcasing potential sources and pathways of contamination. It features the stages of grazing, collection, processing, and packaging. The relevant processes include surface fouling and milk contamination during food transformation as well as the formation of a protein corona on surfaces and nanoparticles after packaging. The figure was created with BioRender.com, https://biorender.com/. This content is not subject to CC BY 4.0.CC BY · PubMed Central
Figure 2 A chart of the main factors determining the structure of the bionano interface. The quantitative model comprises three essential aspects, that is, surface properties of the nanomaterial, the characteristics of the surrounding medium, and the biological factors at play.
Figure 2 A chart of the main factors determining the structure of the bionano interface. The quantitative model comprises three essential aspects, that is, surface properties of the nanomaterial, the characteristics of the surrounding medium, and the biological factors at play.CC BY · PubMed Central
Figure 3 A dataflow scheme of the multiscale modelling approach implemented in this study, including an all-atom model of surface and AAs, a CG UnitedAtom model for the entire protein-surface interaction, and a CG competitive adsorption model. The figure provides an overview of input and output data at each scale.
Figure 3 A dataflow scheme of the multiscale modelling approach implemented in this study, including an all-atom model of surface and AAs, a CG UnitedAtom model for the entire protein-surface interaction, and a CG competitive adsorption model. The figure provides an overview of input and output data at each scale.CC BY · PubMed Central
Figure 4 Adsorption free energy profiles of SCAs on three aluminum fcc slabs as a function of the surface separation distance (SSD). These profiles were calculated using all-atom AWT-MetaD. The vertical lines indicate the positions of water and ion layers. (a) Al(100), (b) Al(110), and (c) Al(111).
Figure 4 Adsorption free energy profiles of SCAs on three aluminum fcc slabs as a function of the surface separation distance (SSD). These profiles were calculated using all-atom AWT-MetaD. The vertical lines indicate the positions of water and ion layers. (a) Al(100), (b) Al(110), and (c) Al(111).CC BY · PubMed Central
Figure 5 Minimum energy of adsorption (kBT) for each SCA on three Al fcc slabs obtained through all-atom simulations: (a) Al(100), (b) Al(110), and (c) Al(111). Notably, Al(111) exhibits a stronger binding affinity than Al(100) and Al(110).
Figure 5 Minimum energy of adsorption (kBT) for each SCA on three Al fcc slabs obtained through all-atom simulations: (a) Al(100), (b) Al(110), and (c) Al(111). Notably, Al(111) exhibits a stronger binding affinity than Al(100) and Al(110).CC BY · PubMed Central
Figure 6 The interaction potential of glucose with the three Al surfaces predicted using the PMFPredictor Toolkit. The solid lines give the ensemble average of ten versions of the model while the shaded regions indicate the 95% confidence intervals.
Figure 6 The interaction potential of glucose with the three Al surfaces predicted using the PMFPredictor Toolkit. The solid lines give the ensemble average of ten versions of the model while the shaded regions indicate the 95% confidence intervals.CC BY · PubMed Central
Figure 7 Adsorption energy heatmaps obtained from the UnitedAtom model and corresponding 3D representations of the interactions of (a) AS1C, (b) AS2C, (c) BC, (d) BLAC, (e) ALAC, and (f) BSA with Al(110) in the preferred orientations. The figure highlights the closest AAs to the surface of the material.
Figure 7 Adsorption energy heatmaps obtained from the UnitedAtom model and corresponding 3D representations of the interactions of (a) AS1C, (b) AS2C, (c) BC, (d) BLAC, (e) ALAC, and (f) BSA with Al(110) in the preferred orientations. The figure highlights the closest AAs to the surface of the material.CC BY · PubMed Central
Figure 8 Mass abundance of proteins on Al and Fe surfaces (100, 110, and 111) using the original KMC approach without molecular displacements and a NP radius of 80 nm.
Figure 8 Mass abundance of proteins on Al and Fe surfaces (100, 110, and 111) using the original KMC approach without molecular displacements and a NP radius of 80 nm.CC BY · PubMed Central
Figure 9 Mass abundance of proteins on Al and Fe surfaces (100, 110, and 111) using the KMC model with molecule displacement and a NP radius of 80 nm.
Figure 9 Mass abundance of proteins on Al and Fe surfaces (100, 110, and 111) using the KMC model with molecule displacement and a NP radius of 80 nm.CC BY · PubMed Central

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Citations

Cites

Show all 45 references

Research works printed but not linked: 2

  1. Nabrzyski, 1994, Rocz. Panstw. Zakl. Hig., 45, 1181
  2. 10.1039/d1cp01116b · DOI

Metadata

Authors
Amini, P. M., Rouse, I., Subbotina, J., & Lobaskin, V.
Affiliations
University College Dublin (IE)
Corresponding author
Amini, Parinaz Mosaddeghi
ORCID
Venue
Beilstein Journal of Nanotechnology
Publisher
Beilstein Institut
Published
2024-02-13
Volume
15
Pages
215-229
ISSN
2190-4286 (ISSN-L)
DOI
10.3762/bjnano.15.21
PMC
PMC10877083
OpenAlex
W4391779980
Cited by
10
References
45
Citations by year
2026: 4 · 2025: 2 · 2024: 4
FWCI
0.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Beilstein Journal of Nanotechnology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Research Repository UCD (University College Dublin) (submittedVersion, cc-by)
Funders
  • European Commission (101092741, HORIZON2020, 814572)
  • Science Foundation Ireland (16/IA/4506)
  • Irish Centre for High-End Computing
Topics
  • Physical Sciences › Materials Science › Surfaces, Coatings and Films › Polymer Surface Interaction Studies
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Proteins in Food Systems
  • Physical Sciences › Materials Science › Materials Chemistry › Corrosion Behavior and Inhibition
Keywords
protein corona, biomolecular corona, aluminum surfaces, milk protein, protein adsorption, food packaging, dairy products, multiscale modeling, atomistic molecular dynamics, coarse-grained protein models, kinetic Monte Carlo

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CoChat. (2026). Citation check: Multiscale modelling of biomolecular corona formation on metallic surfaces. https://app.cochat.ai/papers/10.3762/bjnano.15.21
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Amini, P. M., Rouse, I., Subbotina, J., & Lobaskin, V. (2024). Multiscale modelling of biomolecular corona formation on metallic surfaces. Beilstein Journal of Nanotechnology. https://doi.org/10.3762/bjnano.15.21

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.