Modulation of nuclear receptor function: Targeting the protein-DNA interface

Filho, Helder Veras Ribeiro; Tambones, Izabella Luisa; Dias, Marieli M. G.; Videira, Natália Bernardi; Bruder, Marjorie; Amato, Angélica Amorim; Figueira, Ana Carolina Migliorini

doi:10.1016/j.mce.2019.01.023

Cited by 19 publications

(8 citation statements)

References 116 publications

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“…NRs are a superfamily of ligand-dependent transcriptional factors containing n N-terminal transactivation domain, a flexible hinge region, and a C-terminal ligand-binding domain (LBD) [6,8,13]. NRs are classified mainly into two types according to their subcellular distribution in the absence of a ligand and their mechanisms: Type I steroid receptors, including the estrogen receptor (ER), androgen receptor (AR), progesterone receptor (PR), and glucocorticoid receptor (GR); and Type II nonsteroid receptors, including the thyroid receptor (TR alpha and beta), retinoic acid receptor (RAR alpha, beta, and gamma), retinoid X receptor (RXR), vitamin D receptor (VDR), peroxisome proliferator-activated receptor (PPAR alpha, beta, and gamma), liver X receptor (LXR), farnesoid X receptor (FXR), and pregane X receptor (PXR), [6,14,15]. In the absence of a ligand, the type I NR forms inactive complexes with chaperone proteins in the cytoplasm, whereas type II NR, regardless of the ligand-binding status, is located in the nucleus and binds to the DNA response elements of its target genes along with corepressors [6,14,16].…”

Section: Introductionmentioning

confidence: 99%

Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library

Matsuzaka

Uesawa

2020

Molecules

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The interaction of nuclear receptors (NRs) with chemical compounds can cause dysregulation of endocrine signaling pathways, leading to adverse health outcomes due to the disruption of natural hormones. Thus, identifying possible ligands of NRs is a crucial task for understanding the adverse outcome pathway (AOP) for human toxicity as well as the development of novel drugs. However, the experimental assessment of novel ligands remains expensive and time-consuming. Therefore, an in silico approach with a wide range of applications instead of experimental examination is highly desirable. The recently developed novel molecular image-based deep learning (DL) method, DeepSnap-DL, can produce multiple snapshots from three-dimensional (3D) chemical structures and has achieved high performance in the prediction of chemicals for toxicological evaluation. In this study, we used DeepSnap-DL to construct prediction models of 35 agonist and antagonist allosteric modulators of NRs for chemicals derived from the Tox21 10K library. We demonstrate the high performance of DeepSnap-DL in constructing prediction models. These findings may aid in interpreting the key molecular events of toxicity and support the development of new fields of machine learning to identify environmental chemicals with the potential to interact with NR signaling pathways.

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Section: Introductionmentioning

confidence: 99%

Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library

Matsuzaka

Uesawa

2020

Molecules

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“…However, at higher concentrations (≥5 µM), it inhibited proliferation in both cell lines [306]. The inhibition of proliferation was attributed to direct covalent modifications of Cys227 and Cys240 in the C-terminal Zinc fingers in the ERα DBD by 15d-PGJ2 and thus disruption of transcription of ER dependent genes [306,307].…”

Section: Compoundmentioning

confidence: 97%

Computer-Aided Ligand Discovery for Estrogen Receptor Alpha

Bafna

Ban

Rennie

et al. 2020

IJMS

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Breast cancer (BCa) is one of the most predominantly diagnosed cancers in women. Notably, 70% of BCa diagnoses are Estrogen Receptor α positive (ERα+) making it a critical therapeutic target. With that, the two subtypes of ER, ERα and ERβ, have contrasting effects on BCa cells. While ERα promotes cancerous activities, ERβ isoform exhibits inhibitory effects on the same. ER-directed small molecule drug discovery for BCa has provided the FDA approved drugs tamoxifen, toremifene, raloxifene and fulvestrant that all bind to the estrogen binding site of the receptor. These ER-directed inhibitors are non-selective in nature and may eventually induce resistance in BCa cells as well as increase the risk of endometrial cancer development. Thus, there is an urgent need to develop novel drugs with alternative ERα targeting mechanisms that can overcome the limitations of conventional anti-ERα therapies. Several functional sites on ERα, such as Activation Function-2 (AF2), DNA binding domain (DBD), and F-domain, have been recently considered as potential targets in the context of drug research and discovery. In this review, we summarize methods of computer-aided drug design (CADD) that have been employed to analyze and explore potential targetable sites on ERα, discuss recent advancement of ERα inhibitor development, and highlight the potential opportunities and challenges of future ERα-directed drug discovery.

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“…In addition, only the ligand-binding domains (LBDs) of NRs were targeted in this study. However, it has been suggested that the DNA binding domains (DBDs) of NRs and even the DNA sites recognized by NRs can also be targeted by ligands (Brodie 2005;Meijsing et al 2009;Li et al 2014;Dalal et al 2014;Shizu et al 2018;Frank et al 2018;Pal et al 2019;Veras Ribeiro Filho et al 2019). Thus we expect that the application of our in silico method to these new molecular interfaces will also facilitate potential EDC prediction.…”

Section: Future Perspectivesmentioning

confidence: 99%

Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids

Jaladanki

Zhao

et al. 2020

Arch Toxicol

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Nuclear receptors (NRs) are key regulators of energy homeostasis, body development, and sexual reproduction. Xenobiotics binding to NRs may disrupt natural hormonal systems and induce undesired adverse effects in the body. However, many chemicals of concerns have limited or no experimental data on their potential or lack-of-potential endocrine-disrupting effects. Here, we propose a virtual screening method based on molecular docking for predicting potential endocrine-disrupting chemicals (EDCs) that bind to NRs. For 12 NRs, we systematically analyzed how multiple crystal structures can be used to distinguish actives and inactives found in previous high-throughput experiments. Our method is based on (i) consensus docking scores from multiple structures at a single functional state (agonist-bound or antagonist-bound), (ii) multiple functional states (agonist-bound and antagonist-bound), and (iii) multiple pockets (orthosteric site and alternative sites) of these NRs. We found that the consensus enrichment from multiple structures is better than or comparable to the best enrichment from a single structure. The discriminating power of this consensus strategy was further enhanced by a chemical similarity-weighted scoring scheme, yielding better or comparable enrichment for all studied NRs. Applying this optimized method, we screened 252 fatty acids against peroxisome proliferator-activated receptor gamma (PPARγ) and successfully identified 3 previously unknown fatty acids with Kd = 100–250 μM including two furan fatty acids: furannonanoic acid (FNA) and furanundecanoic acid (FUA), and one cyclopropane fatty acid: phytomonic acid (PTA). These results suggested that the proposed method can be used to rapidly screen and prioritize potential EDCs for further experimental evaluations.

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Modulation of nuclear receptor function: Targeting the protein-DNA interface

Cited by 19 publications

References 116 publications

Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library

Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library

Computer-Aided Ligand Discovery for Estrogen Receptor Alpha

Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids

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