Structural Characterization of the Native NH2-Terminal Transactivation Domain of the Human Androgen Receptor: A Collapsed Disordered Conformation Underlies Structural Plasticity and Protein-Induced Folding†

Biochemistry ◽  
2008 ◽  
Vol 47 (11) ◽  
pp. 3360-3369 ◽  
Author(s):  
Derek N. Lavery ◽  
Iain J. McEwan
Keyword(s):  
Androgen Receptor ◽  
Structural Characterization ◽  
Structural Plasticity ◽  
Transactivation Domain ◽  
Induced Folding ◽  
Human Androgen Receptor

scholarly journals Structural Characterization of a Noncovalent Complex between Ubiquitin and the Transactivation Domain of the Erythroid-Specific Factor EKLF

Structure ◽  
2013 ◽  
Vol 21 (11) ◽  
pp. 2014-2024 ◽  
Author(s):  
Luca Raiola ◽  
Mathieu Lussier-Price ◽  
David Gagnon ◽  
Julien Lafrance-Vanasse ◽  
Xavier Mascle ◽  
...  
Keyword(s):  
Structural Characterization ◽  
Noncovalent Complex ◽  
Specific Factor ◽  
Transactivation Domain

scholarly journals Characterization of human androgen receptor overexpressed in the baculovirus system.

1992 ◽  
Vol 89 (13) ◽  
pp. 5946-5950 ◽  
Author(s):  
C. Chang ◽  
C. Wang ◽  
H. F. DeLuca ◽  
T. K. Ross ◽  
C. C. Shih
Keyword(s):  
Androgen Receptor ◽  
Baculovirus System ◽  
Human Androgen Receptor

The androgen receptor transactivation domain: the interplay between protein conformation and protein–protein interactions

2003 ◽  
Vol 31 (5) ◽  
pp. 1042-1046 ◽  
Author(s):  
J. Reid ◽  
R. Betney ◽  
K. Watt ◽  
I.J. McEwan
Keyword(s):  
Androgen Receptor ◽  
Protein Interactions ◽  
Transcriptional Activation ◽  
Specific Protein ◽  
Transactivation Domain ◽  
Protein Protein Interactions ◽  
Induced Folding ◽  
Large N ◽  
Tryptophan Residues ◽  
Receptor Transactivation

The AR (androgen receptor) belongs to the nuclear receptor superfamily and directly regulates patterns of gene expression in response to the steroids testosterone and dihydrotestosterone. Sequences within the large N-terminal domain of the receptor have been shown to be important for transactivation and protein–protein interactions; however, little is known about the structure and folding of this region. Folding of the AR transactivation domain was observed in the presence of the helix-stabilizing solvent trifluorethanol and the natural osmolyte TMAO (trimethylamine N-oxide). TMAO resulted in the movement of two tryptophan residues to a less solvent-exposed environment and the formation of a protease-resistant conformation. Critically, binding to a target protein, the RAP74 subunit of the general transcription factor TFIIF, resulted in a similar resistance to protease digestion, consistent with induced folding of the receptor transactivation domain. Our current hypothesis is that the folding of the transactivation domain in response to specific protein–protein interactions creates a platform for subsequent interactions, resulting in the formation of a competent transcriptional activation complex.

scholarly journals Deep Learning Modeling of Androgen Receptor Responses to Prostate Cancer Therapies

2020 ◽  
Vol 21 (16) ◽  
pp. 5847 ◽  
Author(s):  
Oliver Snow ◽  
Nada Lallous ◽  
Martin Ester ◽  
Artem Cherkasov
Keyword(s):  
Prostate Cancer ◽  
Androgen Receptor ◽  
Cancer Therapies ◽  
Learning Approaches ◽  
Experimental Characterization ◽  
Treatment Protocols ◽  
Mixed Response ◽  
Human Androgen Receptor

Gain-of-function mutations in human androgen receptor (AR) are among the major causes of drug resistance in prostate cancer (PCa). Identifying mutations that cause resistant phenotype is of critical importance for guiding treatment protocols, as well as for designing drugs that do not elicit adverse responses. However, experimental characterization of these mutations is time consuming and costly; thus, predictive models are needed to anticipate resistant mutations and to guide the drug discovery process. In this work, we leverage experimental data collected on 68 AR mutants, either observed in the clinic or described in the literature, to train a deep neural network (DNN) that predicts the response of these mutants to currently used and experimental anti-androgens and testosterone. We demonstrate that the use of this DNN, with general 2D descriptors, provides a more accurate prediction of the biological outcome (inhibition, activation, no-response, mixed-response) in AR mutant-drug pairs compared to other machine learning approaches. Finally, the developed approach was used to make predictions of AR mutant response to the latest AR inhibitor darolutamide, which were then validated by in-vitro experiments.

scholarly journals In Vitro Characterization of the Endocrine Disrupting Effects of Per- and Poly-fluoroalkyl Substances (PFASs) on the Human Androgen Receptor

2022 ◽  
pp. 128243
Author(s):  
Phum Tachachartvanich ◽  
Azhagiya Singam Ettayapuram Ramaprasad ◽  
Kathleen A. Durkin ◽  
J. David Furlow ◽  
Martyn T. Smith ◽  
...  
Keyword(s):  
Androgen Receptor ◽  
In Vitro Characterization ◽  
Endocrine Disrupting ◽  
Human Androgen Receptor

Abstract 3522: Structural characterization of androgen receptor variant 7 in prostate cancer

2017 ◽  
Author(s):  
Fatma Özgün ◽  
Zeynep Kaya ◽  
Halil Bayraktar ◽  
Selen Manioğlu ◽  
Doğancan Özturan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Androgen Receptor ◽  
Structural Characterization
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