AIMC Topic: Cysteine

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Intestinal permeability of N-acetylcysteine is driven by gut microbiota-dependent cysteine palmitoylation.

Nature communications May 19, 2025
Trillions of intestinal microbiota are essential to the permeability of orally administered drugs. However, identifying microbial-drug interactions remains challenging due to the highly variable composition of intestinal flora among individuals. Usin...
Rats, Sprague-Dawley Intestinal Mucosa Permeability Male Humans Rats Gastrointestinal Microbiome Female Specific Pathogen-Free Organisms Animals Germ-Free Life Intestinal Barrier Function Cysteine Acetylcysteine Lipoylation
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CovCysPredictor: Predicting Selective Covalently Modifiable Cysteines Using Protein Structure and Interpretable Machine Learning.

Journal of chemical information and modeling Jan 8, 2025
Targeted covalent inhibition is a powerful therapeutic modality in the drug discoverer's toolbox. Recent advances in covalent drug discovery, in particular, targeting cysteines, have led to significant breakthroughs for traditionally challenging targ...
Proteins Humans Ligands Databases, Protein Machine Learning Protein Conformation Models, Molecular Drug Discovery Cysteine
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Machine Learning-Assisted Liquid Crystal Optical Sensor Array Using Cysteine-Functionalized Silver Nanotriangles for Pathogen Detection in Food and Water.

ACS applied materials & interfaces Dec 12, 2024
The challenge of rapid identification of bacteria in food and water still persists as a major health problem. To tackle this matter, we have developed a single-probe liquid crystal (LC)-based optical sensing platform for the differentiation of five c...
Machine Learning Bacteria Food Microbiology Water Microbiology Cysteine Liquid Crystals Metal Nanoparticles Silver Milk
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Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design.

Journal of computer-aided molecular design May 1, 2024
Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays...
Quantitative Structure-Activity Relationship Acrylamide Cysteine Machine Learning Drug Design Models, Molecular Molecular Structure Quantum Theory Linear Models Humans
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CysPresso: a classification model utilizing deep learning protein representations to predict recombinant expression of cysteine-dense peptides.

BMC bioinformatics May 16, 2023
BACKGROUND: Cysteine-dense peptides (CDPs) are an attractive pharmaceutical scaffold that display extreme biochemical properties, low immunogenicity, and the ability to bind targets with high affinity and selectivity. While many CDPs have potential a...
Mammals Cysteine Peptides Deep Learning Proteins Algorithms Animals
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CoO/CoFeO Hollow Nanocube Multifunctional Nanozyme with Oxygen Vacancies for Deep-Learning-Assisted Smartphone Biosensing and Organic Pollutant Degradation.

ACS applied materials & interfaces Feb 21, 2023
Although the application of nanozymes has been widely studied, it is still a huge challenge to develop highly active and multifunctional nanozyme catalysts with a wider application prospect. CoO/CoFeO hollow nanocubes (HNCs) with oxygen vacancies wer...
Oxygen Cysteine Norfloxacin Peroxidase Colorimetry Deep Learning Smartphone Hydrogen Peroxide Oxidation-Reduction
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Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen.

European journal of medicinal chemistry Oct 3, 2022
SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly f...
SARS-CoV-2 Deep Learning Antiviral Agents Coronavirus 3C Proteases COVID-19 Drug Treatment Humans Cysteine Protease Inhibitors
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Mul-SNO: A Novel Prediction Tool for S-Nitrosylation Sites Based on Deep Learning Methods.

IEEE journal of biomedical and health informatics May 5, 2022
Protein s-nitrosylation (SNO) is one of the most important post-translational modifications and is formed by the covalent modification of nitric oxide and cysteine residues. Extensive studies have shown that SNO plays a pivotal role in the plant immu...
Protein Processing, Post-Translational Cysteine Humans Deep Learning Nitric Oxide
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DeepCys: Structure-based multiple cysteine function prediction method trained on deep neural network: Case study on domains of unknown functions belonging to COX2 domains.

Proteins Feb 22, 2021
Cysteine (Cys) is the most reactive amino acid participating in a wide range of biological functions. In-silico predictions complement the experiments to meet the need of functional characterization. Multiple Cys function prediction algorithm is scar...
Glutathione Disulfides Software Electron Transport Complex IV Sulfonic Acids Protein Processing, Post-Translational Deep Learning Nitroso Compounds Protein Domains Cysteine Structure-Activity Relationship Sulfinic Acids Amino Acid Sequence Cations, Divalent Models, Molecular Sulfides Protein Structure, Secondary
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Prediction and analysis of redox-sensitive cysteines using machine learning and statistical methods.

Biological chemistry Jan 6, 2021
Reactive oxygen species are produced by a number of stimuli and can lead both to irreversible intracellular damage and signaling through reversible post-translational modification. It is unclear which factors contribute to the sensitivity of cysteine...
Electron Transport Complex I Protein Processing, Post-Translational Humans Oxidation-Reduction Machine Learning Cysteine
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