Atomistic simulation of the coupled adsorption and unfolding of protein GB1 on the polystyrenes nanoparticle surface

Xiao, Hu; Bin, Huang; Ge, Yufeng; Kang, Wei; Gong, Sheng; Pan, Hai; Cao, Yi; Wang, Jun; Jian, Zhang; Wang, Wei

doi:10.1007/s11433-017-9124-3

Cited by 12 publications

(15 citation statements)

References 58 publications

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“…With the enormous rise in computational power and the number of molecular simulation methods in the past decades, atomistic modeling is increasingly becoming the method of choice. [1][2][3][4][5][6][7][8][9] Applications range from the study and prevention of corrosion [10][11][12] to protein folding, 13 unfolding, 14 and self-assembly. 15 For many applications, machine learning force fields (MLFFs) are becoming the method of choice, as they can potentially reproduce any functional form of interatomic and intermolecular interactions, leading to reliable descriptions of potential energy surfaces (PESs) of arbitrary complexity.…”

Section: Introductionmentioning

confidence: 99%

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning

Fonseca

Poltavsky

Vassilev-Galindo

et al. 2021

The Journal of Chemical Physics

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The training set of atomic configurations is key to the performance of any Machine Learning Force Field (MLFF) and, as such, the training set selection determines the applicability of the MLFF model for predictive molecular simulations. However, most atomistic reference datasets are inhomogeneously distributed across configurational space (CS), and thus, choosing the training set randomly or according to the probability distribution of the data leads to models whose accuracy is mainly defined by the most common close-to-equilibrium configurations in the reference data. In this work, we combine unsupervised and supervised ML methods to bypass the inherent bias of the data for common configurations, effectively widening the applicability range of the MLFF to the fullest capabilities of the dataset. To achieve this goal, we first cluster the CS into subregions similar in terms of geometry and energetics. We iteratively test a given MLFF performance on each subregion and fill the training set of the model with the representatives of the most inaccurate parts of the CS. The proposed approach has been applied to a set of small organic molecules and alanine tetrapeptide, demonstrating an up to twofold decrease in the root mean squared errors for force predictions on non-equilibrium geometries of these molecules. Furthermore, our ML models demonstrate superior stability over the default training approaches, allowing reliable study of processes involving highly out-of-equilibrium molecular configurations. These results hold for both kernel-based methods (sGDML and GAP/SOAP models) and deep neural networks (SchNet model).

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Section: Introductionmentioning

confidence: 99%

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning

Fonseca

Poltavsky

Vassilev-Galindo

et al. 2021

The Journal of Chemical Physics

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“…Although the PrG binding to functionalized gold surfaces has been thoroughly investigated, the interaction mode of this protein with other nanostructures remains unclear 38 . Furthermore, the investigation of the PrG interaction with hydrophobic surfaces is very essential 39,40 . Therefore, in the present study, experimental and molecular dynamics (MD) simulation studies have been both applied to discover the adsorption mechanism and the structural changes occurring in the PrG upon binding to an SWCNT and Gra nanostructure surfaces.…”

Section: Introductionmentioning

confidence: 99%

An investigation into non-covalent functionalization of a single-walled carbon nanotube and a graphene sheet with protein G:A combined experimental and molecular dynamics study

Ebrahim-Habibi

Ghobeh

Mahyari

et al. 2019

Sci Rep

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Investigation of non-covalent interaction of hydrophobic surfaces with the protein G (PrG) is necessary due to their frequent utilization in immunosensors and ELISA. It has been confirmed that surfaces, including carbonous-nanostructures (CNS) could orient proteins for a better activation. Herein, PrG interaction with single-walled carbon nanotube (SWCNT) and graphene (Gra) nanostructures was studied by employing experimental and MD simulation techniques. It is confirmed that the PrG could adequately interact with both SWCNT and Gra and therefore fine dispersion for them was achieved in the media. Results indicated that even though SWCNT was loaded with more content of PrG in comparison with the Gra, the adsorption of the PrG on Gra did not induce significant changes in the IgG tendency. Several orientations of the PrG were adopted in the presence of SWCNT or Gra; however, SWCNT could block the PrG-FcR. Moreover, it was confirmed that SWCNT reduced the α-helical structure content in the PrG. Reduction of α-helical structure of the PrG and improper orientation of the PrG-SWCNT could remarkably decrease the PrG tendency to the Fc of the IgG. Importantly, the Gra could appropriately orient the PrG by both exposing the PrG-FcR and also by blocking the fragment of the PrG that had tendency to interact with Fab in IgG.

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“…1; reviewed in [1–7]; for the corresponding mean-field kinetic models and typical molecular dynamics simulations, see Refs. [13–17] and [18–21], respectively). The presence of PC influences the interaction between NPs and may reduce the driving force for aggregation [7, 22].…”

Section: Introductionmentioning

confidence: 99%

Nanoparticles without and with protein corona: van der Waals and hydration interaction

Zhdanov

2019

J Biol Phys

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The van der Waals (vdW) interaction between nanoparticles (NPs) in general, and especially between metal NPs, may be appreciable, and may result in nanoparticle aggregation. In biofluids, NPs become rapidly surrounded by a protein corona (PC). Here, the vdW and hydration interaction of NPs with and without PC are compared in detail. The focus is on two widely used types of NPs fabricated of SiO 2 and Au and possessing weak and strong vdW interactions, respectively. For SiO 2 , the presence of PC increases the vdW interaction, but it remains relatively weak and insufficient for aggregation. For Au, the presence of PC decreases the vdW interaction, and in the case of small NPs (≤ 40 nm in diameter) it may become insufficient for aggregation as well while the larger NPs can aggregate.

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Atomistic simulation of the coupled adsorption and unfolding of protein GB1 on the polystyrenes nanoparticle surface

Cited by 12 publications

References 58 publications

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning

An investigation into non-covalent functionalization of a single-walled carbon nanotube and a graphene sheet with protein G:A combined experimental and molecular dynamics study

Nanoparticles without and with protein corona: van der Waals and hydration interaction

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