Beilstein Arch. 2023, 202345. https://doi.org/10.3762/bxiv.2023.45.v1
Published 17 Oct 2023
Background: 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.
Results: In this work, we present the results of multiscale modeling of the interaction between various surfaces (100,110,111) of FCC aluminum with the most abundant milk proteins and lactose. Our approach combines atomistic molecular dynamics, a coarse grained United Atom (UA) model, and kinetic Monte Carlo (KMC) simulations to predict the protein corona composition in the deposited milk layer on aluminum surfaces. We consider a simplified model of the milk, which was 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.
Conclusion: 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 modeling 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 proteins deposition on aluminum surfaces, which can aid in the general understanding of protein corona formation.
Keywords: bionano interface, multiscale modeling, milk protein, lactose, aluminum, protein corona, all atomistic, coarse grained model, kmc model
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Mosaddeghi Amini, P.; Rouse, I.; Subbotina, J.; Lobaskin, V. Beilstein Arch. 2023, 202345. doi:10.3762/bxiv.2023.45.v1
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© 2023 Mosaddeghi Amini et al.; licensee Beilstein-Institut.
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