Glutathione-S-transferase Fusion Protein Nanosensor

Williams, R. T.; Harvey, Jackson D.; Budhathoki-Uprety, Januka; Heller, Daniel A.

doi:10.1021/acs.nanolett.0c02691

Cited by 29 publications

(29 citation statements)

References 60 publications

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“…[74][75][76] They operate by conjugating the thiol moiety of GSH to chemical agents for cell detoxification processes, in which GSH is characteristically integrated to a substrate, authorizing binding to GST coexpressed enzymes or proteins. [77,78] In this context, because GST and GSH play a significant role in the mechanism of anticancer agent resistance, GST inhibitors (GSTIs) are one of the keys to overcoming this resistance. [79,80] GSTIs may increase the sensitivity of cancer cells to antitumor agents and therefore they may be used for different therapeutic applications.…”

Section: Resultsmentioning

confidence: 99%

Infection Medications: Assessment In‐Vitro Glutathione S‐Transferase Inhibition and Molecular Docking Study

2021

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Glutathione S‐transferases (EC 2.5.1.18, GSTs), consisting of at least seven subfamilies, such as alpha, kappa, mu, pi, theta, zeta, and omega, are the family of cytosolic proteins with many known functions and also are abundant in cells. Moreover, they play significant roles in influencing the efficacy and bioavailability of pharmaceutical agents in humans. It is also known that multiple types of cancer tissue frequently have high levels of GSTs compared to the corresponding healthy tissue. Herein, firstly, the GST was purified from human erythrocytes by rapid and straightforward chromatographic techniques. Subsequently, the active infection medications, the antimycotics (amphotericin B, anidulafungin, and caspofungin), antibacterials (daptomycin, ertapenem, and tigecycline), and antiviral (ganciclovir) were assessed for their inhibitory actions versus GST. All drugs demonstrated micromolar levels of potent inhibitory activity towards GST. Antifungal drugs had KI constants ranging between 20.60±0.05 μM and 50.43±0.12 μM, whilst antibacterial drugs exhibited KIs in the range of 20.92±0.09–114.80±0.41 μM. Daptomycin and ertapenem exhibited competitive inhibition, while other drugs in noncompetitive inhibition. Moreover, amphotericin B exhibited the most potent inhibitory activity (KI=20.60±0.05 μM), and in contrast, antiviral drug ganciclovir had the highest inhibition constant (KI=463.10±1.28 μM). Studying both in‐vitro inhibitory activity and molecular docking interactions in silico activities of these infection medications in complex with the GST, an enzyme biologically important, demonstrates was caused significant associations between these drugs and GST were detected. The results here might provide structural guidance to design more potent GST inhibitors.

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Section: Resultsmentioning

confidence: 99%

Infection Medications: Assessment In‐Vitro Glutathione S‐Transferase Inhibition and Molecular Docking Study

2021

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“…Most DNA-SWNT-based sensors are generated using either pre-existing molecular recognition elements 28,29 or low-throughput screening approaches, in which fewer than a hundred DNA sequences are screened for fluorescence modulation by target analytes 2,30 . The latter approach, characterized by random successful parameter hits, relies upon the fortuitous discovery of candidate sensors.…”

Section: Introductionmentioning

confidence: 99%

Machine learning enables discovery of DNA-carbon nanotube sensors for serotonin

Kelich

Jeong

Navarro

et al. 2021

Preprint

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DNA-wrapped single walled carbon nanotube (SWNT) conjugates have remarkable optical properties leading to their use in biosensing and imaging applications. A critical limitation in the development of DNA-SWNT sensors is the current inability to predict unique DNA sequences that confer a strong analyte-specific optical response to these sensors. Here, near-infrared (nIR) fluorescence response datasets for ~100 DNA-SWNT conjugates, narrowed down by a selective evolution protocol starting from a pool of ~1010 unique DNA-SWNT candidates, are used to train machine learning (ML) models to predict new unique DNA sequences with strong optical response to neurotransmitter serotonin. First, classifier models based on convolutional neural networks (CNN) are trained on sequence features to classify DNA ligands as either high response or low response to serotonin. Second, support vector machine (SVM) regression models are trained to predict relative optical response values for DNA sequences. Finally, we demonstrate with validation experiments that integrating the predictions of ensembles of the highest quality CNN classifiers and SVM regression models leads to the best predictions of both high and low response sequences. With our ML approaches, we discovered five new DNA-SWNT sensors with higher fluorescence intensity response to serotonin than obtained previously. Overall, the explored ML approaches introduce an important new tool to predict useful DNA sequences, which can be used for discovery of new DNA-based sensors and nanobiotechnologies.

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“…[28] A different approach for SWCNT-based molecular recognition relies on biomolercular binding elements such as antibodies, [55][56][57] aptamers, [58,59] and other binding partners. [60][61][62][63][64][65][66][67][68][69][70] Aptamers are short, single stranded nucleic acids which are selected for their specific target using the SELEX procedure (systematic evolution of ligands by exponential enrichment). [71] Often, their high affinity to the targets results from a conformational effect as they may fold around the target upon binding.…”

Section: Introductionmentioning

confidence: 99%

Optical Nanosensors for Real-time Feedback on Insulin Secretion by β-Cells

Ehrlich

Hendler‐Neumark

Wulf

et al. 2021

Preprint

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Quantification of insulin is essential for diabetes research in general, and for the study of pancreatic β-cell function in particular. Herein, fluorescent single-walled carbon nanotubes (SWCNT) are used for the recognition and real-time quantification of insulin. Two approaches for rendering the SWCNT sensors for insulin are compared, using surface functionalization with either a natural insulin aptamer with known affinity to insulin, or a synthetic PEGylated-lipid (C16-PEG(2000Da)-Ceramide), both of which show a modulation of the emitted fluorescence in response to insulin. Although the PEGylated-lipid has no prior affinity to insulin, the response of C16-PEG(2000Da)-Ceramide-SWCNTs to insulin is more stable and reproducible compared to the insulin aptamer-SWCNTs. The C16-PEG(2000Da)-Ceramide-SWCNTs optical response is excitation-wavelength dependent, where resonant excitation leads to a larger fluorescence decrease in response to insulin. The SWCNT sensors successfully detect insulin secreted by β-cells within the complex environment of the conditioned media. The insulin is quantified by comparing the SWCNTs fluorescence response to a standard calibration curve, and the results are found to be in agreement with an enzyme-linked immunosorbent assay (ELISA). This novel analytical tool for real time quantification of insulin secreted by β-cells provides new opportunities for rapid assessment of β-cell function, with the ability to push forward many aspects of diabetes research.

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Glutathione-S-transferase Fusion Protein Nanosensor

Cited by 29 publications

References 60 publications

Infection Medications: Assessment In‐Vitro Glutathione S‐Transferase Inhibition and Molecular Docking Study

Infection Medications: Assessment In‐Vitro Glutathione S‐Transferase Inhibition and Molecular Docking Study

Machine learning enables discovery of DNA-carbon nanotube sensors for serotonin

Optical Nanosensors for Real-time Feedback on Insulin Secretion by β-Cells

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