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With the development of accurate protein structure prediction algorithms, artificial intelligence (AI) has emerged as a powerful tool in the field of structural biology. AI-based algorithms have been used to analyze large amounts of protein sequence data including the human proteome, complementing experimental structure data found in resources such as the Protein Data Bank. The EBI AlphaFold Protein Structure Database (for example) contains over 230 million structures. In this study, these data have been analyzed to find all human proteins containing (or predicted to contain) the cytosolic glutathione transferase (cGST) fold. A total of 39 proteins were found, including the alpha-, mu-, pi-, sigma-, zeta- and omega-class GSTs, intracellular chloride channels, metaxins, multisynthetase complex components, elongation factor 1 complex components and others. Three broad themes emerge: cGST domains as enzymes, as chloride ion channels and as protein–protein interaction mediators. As the m
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Post-translational modifications (PTMs) provide a rapid mechanism that enables protein phenotypic diversity so that proteins can react to external and internal disturbances and regulate cellular activity. This Biotech Brief discusses tools and methods that are employed in detecting these sub-stoichiometric and elusive protein modifiers, including mass spectrometry, antibody, artificial intelligence, ubiquitin-clipping, and specific uncaging-assisted biotinylation-based approaches. The article also discusses challenges in detecting PTMs, such as the development of PTM antibodies with high specificity and binding affinity.