In Silico Prediction of Human Sulfotransferase 1E1 Activity Guided by Pharmacophores from Molecular Dynamics Simulations

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.Metabolism plays an important role in drug discovery and appropriate metabolic profiles of new drug candidates should be taken into consideration during the process of drug development. Acting in phase I metabolism, the enzyme family of cytochrome P450 is estimated to transform about 75% of marketed drugs (1) and numerous in silico approaches for the prediction of cytochrome P450-mediated metabolism have emerged to date (2, 3). Although the majority of metabolism prediction studies focuses on phase I, the significance of phase II metabolism is generally underestimated (4) and to this day, computer-based models for the prediction of phase II metabolism remain scarce (5).Among the predominant phase II enzyme families are the soluble sulfotransferases that form a gene superfamily termed SULT. These enzymes regulate the sulfonation of smaller molecules such as endogenous hormones, neurotransmitters, and xenobiotic substances from pharmaceutical, nutritional, or environmental sources. Based on sequence similarity, functional human SULTs are divided into two main families (SULT1 and SULT2) and further into subfamilies that exhibit individual, but somewhat overlapping substrate specificities (6). Influencing the level of female sex hormones (estrogens), SULT subtype 1E1 (SULT1E1) shows a specific substrate preference for physiological estrogenic compounds (K m ϭ 5 nM for estradiol (7)) and has been extensively investigated in experimental studies.In general, sulfonation reactions in which a sulfona...

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“…Sulfotransferases (SULTs) play an important role in phase II metabolism, but represent challenging targets due to their high flexibility and broad substrate specificity. Rakers and colleagues developed a pharmacophore‐based SULT prediction model to discriminate between substrates, inhibitors, and ligands that show both characteristics dependent on their concentration . This study is remarkable for two reasons.…”

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“…Unlike previous applications of gathering pharmacophore information from molecular dynamics (33,69), this new implementation works in a fully automated way: dynophore groups interaction points (such as hydrogen bonds, charges, and lipophilic contacts) of each trajectory frame according to their type and ligand atoms involved. All feature groups are graphically represented by three-dimensional volumetric feature density clouds, which are statistically characterized by occurrence frequency and interaction patterns with the protein.…”

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“…An early study by de Groot et al exemplifies the development of pharmacophore modeling for CYP2D6-catalyzed N-dealkylation reactions [84]. An example where pharmacophore screening is integrated into a larger in silico pipeline for drug metabolism is highlighted by Rakers et al [85]. They developed a pharmacophore model for sulfotransferase 1E1 and used it to successfully screen for drug-like molecules that were known to bind SULT1E1 as well as experimentally validated nine compounds previously not known to bind.…”