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Aging is accompanied by the accumulation of oxidized proteins. To remove them, cells employ the proteasomal and autophagy-lysosomal systems; however, if the clearance rate is inferior to its formation, protein aggregates form as a hallmark of proteostasis loss. In cells, during stress conditions, actin aggregates accumulate leading to impaired proliferation and reduced proteasomal activity, as observed in cellular senescence. The heat shock protein 90 (Hsp90) is a molecular chaperone that binds and protects the proteasome from oxidative inactivation. We hypothesized that in oxidative stress conditions a malfunction of Hsp90 occurs resulting in the aforementioned protein aggregates. Here, we demonstrate that upon oxidative stress Hsp90 loses its function in a highly specific non-enzymatic iron-catalyzed oxidation event and its breakdown product, a cleaved form of Hsp90 (Hsp90cl), acquires a new function in mediating the accumulation of actin aggregates. Moreover, the prevention of Hsp90 cleavage reduces oxidized actin accumulation, whereas transfection of the cleaved form of Hsp90 leads to an enhanced accumulation of oxidized actin. This indicates a clear role of the Hsp90cl in the aggregation of oxidized proteins.
Acting during phase II metabolism, sulfotransferases (SULTs) serve detoxification by transforming a broad spectrum of compounds from pharmaceutical, nutritional, or environmental sources into more easily excretable metabolites. However, SULT activity has also been shown to promote formation of reactive metabolites that may have genotoxic effects. SULT subtype 1E1 (SULT1E1) was identified as a key player in estrogen homeostasis, which is involved in many physiological processes and the pathogenesis of breast and endometrial cancer. The development of an in silico prediction model for SULT1E1 ligands would therefore support the development of metabolically inert drugs and help to assess health risks related to hormonal imbalances. Here, we report on a novel approach to develop a model that enables prediction of substrates and inhibitors of SULT1E1. Molecular dynamics simulations were performed to investigate enzyme flexibility and sample protein conformations. Pharmacophores were developed that served as a cornerstone of the model, and machine learning techniques were applied for prediction refinement. The prediction model was used to screen the DrugBank (a database of experimental and approved drugs): 28% of the predicted hits were reported in literature as ligands of SULT1E1. From the remaining hits, a selection of nine molecules was subjected to biochemical assay validation and experimental results were in accordance with the in silico prediction of SULT1E1 inhibitors and substrates, thus affirming our prediction hypotheses.