Zeta Potential Prediction from Protein Structure in General Aqueous Electrolyte Solutions

Grisham, Daniel R.; Nanda, Vikas

doi:10.1021/acs.langmuir.0c02031

Cited by 16 publications

(8 citation statements)

References 21 publications

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“…The ζ ‐potential reflects the electrostatic interaction between the skim milk protein molecules, and it is closely related to the stability of the solution 42 . All ζ ‐potentials of the goat milk protein samples were negative.…”

Section: Resultsmentioning

confidence: 99%

“…The ζ-potential reflects the electrostatic interaction between the skim milk protein molecules, and it is closely related to the stability of the solution. 42 All ζ-potentials of the goat milk protein samples were negative. This was because the pH of the sample solution was higher than the isoelectric point of the protein, and the anions mainly come from acidic amino acid residues on the surface of the protein molecules.…”

Section: The Effects Of Fat Separation Methods On the Physicochemical...mentioning

confidence: 97%

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Structure andin vitrodigestion characteristics of skim goat milk protein during processing: effects of fat separation

et al. 2023

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BACKGROUNDAlthough nonfat milk has been used worldwide in the industrial dairy process, little is known about the effects of fat separation during the manufacturing process on skim milk's structural and digestive properties. This study investigated the effects of the manufacturing process on the structure and in vitro digestion properties of skim goat milk, particularly emphasizing fat separation.RESULTSChanges in the surface charge and hydrophobicity of milk proteins caused by fat separation resulted in oxidation and aggregation in the subsequent homogenization, heat and spray‐drying processing, which affected its digestibility. Compared with separation by dish separator (DS), skim milk after tubular centrifugal separation (CS) showed a higher initial and final digestibility. The CS samples also had a lower surface hydrophobicity level and higher free sulfhydryl content, ζ‐potential, and average particle size (P < 0.05). Goat milk protein after CS was more readily oxidized and aggregated during the subsequent homogenization and heat treatment, as evidenced by the higher carbonyl content and particle size. Centrifugal separation also converted more β‐sheets to α‐helices, thus promoting the aggregation of oxidized skim milk protein.CONCLUSIONThe skim milk after CS and DS demonstrated different structural and digestive properties. Skim goat milk products after CS were more susceptible to oxidant‐induced protein structural changes, resulting in higher protein digestibility. These findings provide insights into the mechanism involved in the control of gastric digestion of skim milk during manufacturing process. © 2023 Society of Chemical Industry.

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Section: Resultsmentioning

confidence: 99%

Section: The Effects Of Fat Separation Methods On the Physicochemical...mentioning

confidence: 97%

Structure andin vitrodigestion characteristics of skim goat milk protein during processing: effects of fat separation

et al. 2023

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“…Furthermore, EV damage and hemolysis could occur if the blood is pressed through the membrane at high pressure. Given the fact that the surface charge density of proteins is lower than that of EVs − (ζ potential of serum albumin, which is the most abundant protein in blood, was measured to be −13.2 mV at pH 7.5, whereas EVs exhibited ζ potential values of −19.30 mV under the same conditions), electrical force effect which is dependent on the magnitude of surface charge would be combined with filtration to increase the separation efficiency of EVs from proteins. It was demonstrated that electro-driven filtration improved the purity of the separated EVs while extracting greater filtrates (filtration proceeded for 2 h and 240 μL of blood was processed) without clogging, EV damage, and hemolysis .…”

Section: Label-free Microfluidic Methods For Exosome Isolationmentioning

confidence: 99%

Label-Free Isolation of Exosomes Using Microfluidic Technologies

2021

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Exosomes are cell-derived structures packaged with lipids, proteins, and nucleic acids. They exist in diverse bodily fluids and are involved in physiological and pathological processes. Although their potential for clinical application as diagnostic and therapeutic tools has been revealed, a huge bottleneck impeding the development of applications in the rapidly burgeoning field of exosome research is an inability to efficiently isolate pure exosomes from other unwanted components present in bodily fluids. To date, several approaches have been proposed and investigated for exosome separation, with the leading candidate being microfluidic technology due to its relative simplicity, costeffectiveness, precise and fast processing at the microscale, and amenability to automation. Notably, avoiding the need for exosome labeling represents a significant advance in terms of process simplicity, time, and cost as well as protecting the biological activities of exosomes. Despite the exciting progress in microfluidic strategies for exosome isolation and the countless benefits of label-free approaches for clinical applications, current microfluidic platforms for isolation of exosomes are still facing a series of problems and challenges that prevent their use for clinical sample processing. This review focuses on the recent microfluidic platforms developed for labelfree isolation of exosomes including those based on sieving, deterministic lateral displacement, field flow, and pinched flow fractionation as well as viscoelastic, acoustic, inertial, electrical, and centrifugal forces. Further, we discuss advantages and disadvantages of these strategies with highlights of current challenges and outlook of label-free microfluidics toward the clinical utility of exosomes.

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“…There have been few studies on the prediction of zeta potential using one machine learning technique such as ZPRED or neural networks. In addition, these studies have only considered a single medium with limited variables [ 86 , 87 ]. The current research proposes various machine learning approaches considering several mediums and variables to at least partly substitute mathematical modeling for experimentation.…”

Section: Introductionmentioning

confidence: 99%

Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning

et al. 2023

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Nanoparticles have gained significance in modern science due to their unique characteristics and diverse applications in various fields. Zeta potential is critical in assessing the stability of nanofluids and colloidal systems but measuring it can be time-consuming and challenging. The current research proposes the use of cutting-edge machine learning techniques, including multiple regression analyses (MRAs), support vector machines (SVM), and artificial neural networks (ANNs), to simulate the zeta potential of silica nanofluids and colloidal systems, while accounting for affecting parameters such as nanoparticle size, concentration, pH, temperature, brine salinity, monovalent ion type, and the presence of sand, limestone, or nano-sized fine particles. Zeta potential data from different literature sources were used to develop and train the models using machine learning techniques. Performance indicators were employed to evaluate the models’ predictive capabilities. The correlation coefficient (r) for the ANN, SVM, and MRA models was found to be 0.982, 0.997, and 0.68, respectively. The mean absolute percentage error for the ANN model was 5%, whereas, for the MRA and SVM models, it was greater than 25%. ANN models were more accurate than SVM and MRA models at predicting zeta potential, and the trained ANN model achieved an accuracy of over 97% in zeta potential predictions. ANN models are more accurate and faster at predicting zeta potential than conventional methods. The model developed in this research is the first ever to predict the zeta potential of silica nanofluids, dispersed kaolinite, sand–brine system, and coal dispersions considering several influencing parameters. This approach eliminates the need for time-consuming experimentation and provides a highly accurate and rapid prediction method with broad applications across different fields.

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Zeta Potential Prediction from Protein Structure in General Aqueous Electrolyte Solutions

Cited by 16 publications

References 21 publications

Structure andin vitrodigestion characteristics of skim goat milk protein during processing: effects of fat separation

Structure andin vitrodigestion characteristics of skim goat milk protein during processing: effects of fat separation

Label-Free Isolation of Exosomes Using Microfluidic Technologies

Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning

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