The binding interaction of protein on a charged surface using Poisson–Boltzmann equation: lysozyme adsorption onto SBA-15

Gama, Marlon de Souza; Barreto, Amaro Gomes; Tavares, Frederico W.

doi:10.1007/s10450-021-00344-6

Cited by 5 publications

(17 citation statements)

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“…The literature [9,10] reports how to calculate the PMF from the electrostatic potential obtained by solving a modified Poisson-Boltzmann (PB) equation in a different set of coordinates. The modification in the PB equation allows the PMF to be a function of the solution conditions, i.e., temperature, ionic strength, salt type, and pH.…”

Section: Mathematical Model For Protein Adsorption In Column Chromato...mentioning

confidence: 99%

“…It is beyond the purpose of this work to show how to solve the modified PB equation. Thus, we applied the exact calculation details reported in the literature [9,10]. In this way, the Langmuir isotherm (Equation ( 5)) is linked with a thermodynamic parameter (Equation ( 7)), becoming a function of the mAb and stationary phase's surface properties and the solution conditions.…”

Section: Mathematical Model For Protein Adsorption In Column Chromato...mentioning

confidence: 99%

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A First Approach towards Adsorption-Oriented Physics-Informed Neural Networks: Monoclonal Antibody Adsorption Performance on an Ion-Exchange Column as a Case Study

et al. 2022

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Adsorption systems are characterized by challenging behavior to simulate any numerical method. A novel field of study emerged within the numerical method in the last two years: the physics-informed neural network (PINNs), the application of artificial intelligence to solve partial differential equations. This is a complete new standpoint for solving engineering first-principle models, which up to that date was not explored in the field of adsorption systems. Therefore, this work proposed the evaluation of PINN to address the numerical solutions of a fixed-bed column where a monoclonal antibody is purified. The PINNs solution is compared with a traditional numerical method. The results show the accuracy of the proposed PINNs when compared with the numerical method. This points towards the potential of this technique to address complex numerical problems found in chemical engineering.

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Section: Mathematical Model For Protein Adsorption In Column Chromato...mentioning

confidence: 99%

Section: Mathematical Model For Protein Adsorption In Column Chromato...mentioning

confidence: 99%

A First Approach towards Adsorption-Oriented Physics-Informed Neural Networks: Monoclonal Antibody Adsorption Performance on an Ion-Exchange Column as a Case Study

et al. 2022

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confidence: 94%

A parallel hybrid model for integrating protein adsorption models with deep neural networks

de Souza Gama,

Lima,

Santana

et al. 2023

Adsorption

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pH 10, using different salts: NaCl, NaBr, and NaI. The results indicate a low retention time for the pHs near the isoelectric points for lysozyme (pH 10.8) and silica (pH 5.7) [1]. In addition, differences in salt types allow a significant decay in retention time and an increase in mass front compressibility in the following order: I -> Br -> Cldue to the differences in anion polarizability. The results here indicate that a link between different scales is successfully achieved using DNN. KeywordsMultiscale model • Deep Neural Network • Surrogate Model • Evolution of mass front in a fixed bed • Non-linear chromatography.

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“…[1][2][3][4][5] In addition, the adsorption process can also be modulated by external experimental conditions such as light, [6] pH, ionic strength, and temperature. [7][8][9][10][11] On conducting substrates, an external applied potential can also be used to control the accumulation of the protein molecules at the polarized interface. [12][13][14][15][16][17][18] This phenomenon is particularly important because it could open the door for the possibility to control the efficiency, selectivity, and kinetics of the adsorption process.…”

Section: Introductionmentioning

confidence: 99%

Dielectric Spectroscopy Can Predict the Effect of External AC Fields on the Dynamic Adsorption of Lysozyme

2022

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This report describes the application of dielectric spectroscopy as a simple and fast way to guide protein adsorption experiments. Specifically, the polarization behavior of a layer of adsorbed lysozyme was investigated using a triangular-wave signal with frequencies varying from 0.5 to 2 Hz. The basic experiment, which can be performed in less than 5 min and with a single sample, not only allowed confirming the susceptibility of the selected protein towards the electric signal but also identified that this protein would respond more efficiently to signals with lower frequencies. To verify the validity of these observations, the adsorption behavior of lysozyme onto optically transparent carbon electrodes was also investigated under the influence of an applied alternating potential. In these experiments, the applied signal was defined by a sinusoidal wave with an amplitude of 100 mV and superimposed to + 800 mV (applied as a working potential) and varying the frequency in the 0.1-10000 Hz range. The experimental data showed that the greatest adsorbed amounts of lysozyme were obtained at the lowest tested frequencies (0.1-1.0 Hz), results that are in line with the corresponding dielectric features of the protein.

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The binding interaction of protein on a charged surface using Poisson–Boltzmann equation: lysozyme adsorption onto SBA-15

Cited by 5 publications

References 56 publications

A First Approach towards Adsorption-Oriented Physics-Informed Neural Networks: Monoclonal Antibody Adsorption Performance on an Ion-Exchange Column as a Case Study

A First Approach towards Adsorption-Oriented Physics-Informed Neural Networks: Monoclonal Antibody Adsorption Performance on an Ion-Exchange Column as a Case Study

A parallel hybrid model for integrating protein adsorption models with deep neural networks

Dielectric Spectroscopy Can Predict the Effect of External AC Fields on the Dynamic Adsorption of Lysozyme

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