@article{TeraoGarattiniRomaoetal.2020,
  author    = {Terao, Mineko and Garattini, Enrico and Rom{\~a}o, Maria Jo{\~a}o and Leimk{\"u}hler, Silke},
  title     = {Evolution, expression, and substrate specificities of aldehyde oxidase enzymes in eukaryotes},
  series = {The journal of biological chemistry},
  volume    = {295},
  journal   = {The journal of biological chemistry},
  number    = {16},
  publisher = {American Society for Biochemistry and Molecular Biology},
  address   = {Rockville},
  issn      = {0021-9258},
  doi       = {10.1074/jbc.REV119.007741},
  pages     = {5377 -- 5389},
  year      = {2020},
  abstract  = {Aldehyde oxidases (AOXs) are a small group of enzymes belonging to the larger family of molybdo-flavoenzymes, along with the well-characterized xanthine oxidoreductase. The two major types of reactions that are catalyzed by AOXs are the hydroxylation of heterocycles and the oxidation of aldehydes to their corresponding carboxylic acids. Different animal species have different complements of AOX genes. The two extremes are represented in humans and rodents; whereas the human genome contains a single active gene (AOX1), those of rodents, such as mice, are endowed with four genes (Aox1-4), clustering on the same chromosome, each encoding a functionally distinct AOX enzyme. It still remains enigmatic why some species have numerous AOX enzymes, whereas others harbor only one functional enzyme. At present, little is known about the physiological relevance of AOX enzymes in humans and their additional forms in other mammals. These enzymes are expressed in the liver and play an important role in the metabolisms of drugs and other xenobiotics. In this review, we discuss the expression, tissue-specific roles, and substrate specificities of the different mammalian AOX enzymes and highlight insights into their physiological roles.},
  language  = {en}
}
@article{RakersSchumacherMeinletal.2016,
  author    = {Rakers, Christin and Schumacher, Fabian and Meinl, Walter and Glatt, Hansruedi and Kleuser, Burkhard and Wolber, Gerhard},
  title     = {In Silico Prediction of Human Sulfotransferase 1E1 Activity Guided by Pharmacophores from Molecular Dynamics Simulations},
  series = {The journal of biological chemistry},
  volume    = {291},
  journal   = {The journal of biological chemistry},
  publisher = {American Society for Biochemistry and Molecular Biology},
  address   = {Bethesda},
  issn      = {0021-9258},
  doi       = {10.1074/jbc.M115.685610},
  pages     = {58 -- 71},
  year      = {2016},
  abstract  = {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.},
  language  = {en}
}