ArticleThe Journal of biological chemistry2016
In Silico Prediction of Human Sulfotransferase 1E1 Activity Guided by Pharmacophores from Molecular Dynamics Simulations.
Article in The Journal of biological chemistry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Estrogen sulfotransferase in the metabolism of estrogenic drugs and in the pathogenesis of diseases.Expert opinion on drug metabolism & toxicology · 2019Pooled it
- Artificial intelligence and computational methods in human metabolism research: A comprehensive survey.Journal of pharmaceutical analysis · 2025Review
- Cross-Tissue Regulatory Network Analyses Reveal Novel Susceptibility Genes and Potential Mechanisms for Endometriosis.Biology · 2024Article
- FMO-guided design of darunavir analogs as HIV-1 protease inhibitors.Scientific reports · 2024Article
- Identification of repurposing therapeutics toward SARS-CoV-2 main protease by virtual screening.PloS one · 2022Article
- Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9.PLoS computational biology · 2022Article
- Computational Analysis of Chemical Space of Natural Compounds Interacting with Sulfotransferases.Molecules (Basel, Switzerland) · 2021Article
- Insights into the substrate binding mechanism of SULT1A1 through molecular dynamics with excited normal modes simulations.Scientific reports · 2021Article
- An activity-based fluorescent sensor for the detection of the phenol sulfotransferase SULT1A1 in living cells.RSC chemical biology · 2021Article
- Functional Expression of All Human Sulfotransferases in Fission Yeast, Assay Development, and Structural Models for Isoforms SULT4A1 and SULT6B1.Biomolecules · 2020Article
- Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens.International journal of molecular sciences · 2020Review
- Mechanistic Insights into the Effect of Ligands on Structural Stability and Selectivity of Sulfotransferase 2A1 (SULT2A1).ACS omega · 2019Article
- Optimization of Pyrazoles as Phenol Surrogates to Yield Potent Inhibitors of Macrophage Migration Inhibitory Factor.ChemMedChem · 2018Article
- Comparing pharmacophore models derived from crystallography and NMR ensembles.Journal of computer-aided molecular design · 2017Article
- Structural and biochemical studies of sulphotransferase 18 from Arabidopsis thaliana explain its substrate specificity and reaction mechanism.Scientific reports · 2017Article
- Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools.Current drug metabolism · 2017Review
- Ligand Binding Ensembles Determine Graded Agonist Efficacies at a G Protein-coupled Receptor.The Journal of biological chemistry · 2016Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.