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

Christin Rakers,Fabian Schumacher,Walter Meinl,Hansruedi Glatt,Burkhard Kleuser,Gerhard Wolber

doi:10.1074/jbc.m115.685610

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.

Highlights

Among the predominant phase II enzyme families are the soluble sulfotransferases that form a gene superfamily termed SULT
Based on selected docking results, eight specific three-dimensional pharmacophores were created that enable identification of active ligands of SULT1E1
Ensemble Docking and Three-dimensional Pharmacophore Development—Because three-dimensional pharmacophores represent a valuable tool for metabolism prediction [2], our goal was to create a collection of pharmacophores that would allow identification of substrates and inhibitors of SULT1E1

Summary

Introduction

Sulfonation can lead to the formation of chemically reactive or toxic metabolites [10, 11] It is a commonly accepted concept that in some sulfonation reactions with certain molecules, e.g. alkylated polycyclic aromatic hydrocarbons or aromatic amines, the resulting sulfate group is electron withdrawing and becomes a good leaving group. The inhibition of SULTs decreases sulfonation rates, which disrupts homeostasis of endogenous molecules like hormones, neurotransmitters, or bile acids. An in silico prediction model for SULT1E1 activation and inhibition further supports drug design by guiding the development of metabolically inert drug candidates This might in turn decrease severe adverse events that are caused by the emergence of reactive metabolites. The presented in silico model was experimentally validated and allows efficient screening of large numbers of compounds

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Conclusion