Characterisation and validation of an in vitro transactivation assay based on the 22Rv1/MMTV_GR-KO cell line to detect human androgen receptor agonists and antagonists

2021 ◽  
Vol 152 ◽  
pp. 112206
Author(s):  
Yooheon Park ◽  
Da-Woon Jung ◽  
Anne Milcamps ◽  
Masahiro Takeyoshi ◽  
Miriam N. Jacobs ◽  
...  
Keyword(s):  
Androgen Receptor ◽  
Cell Line ◽  
Receptor Agonists ◽  
Transactivation Assay ◽  
Receptor Agonists And Antagonists ◽  
Human Androgen Receptor

scholarly journals Overexpression of Yeast Hsp110 Homolog Sse1p Suppressesydj1-151Thermosensitivity and Restores Hsp90-dependent Activity

2002 ◽  
Vol 13 (8) ◽  
pp. 2760-2770 ◽  
Author(s):  
Jennifer L. Goeckeler ◽  
Andi Stephens ◽  
Paul Lee ◽  
Avrom J. Caplan ◽  
Jeffrey L. Brodsky
Keyword(s):  
Androgen Receptor ◽  
Src Kinase ◽  
Firefly Luciferase ◽  
Temperature Sensitive ◽  
Dependent Activity ◽  
Multicopy Suppressors ◽  
Folding Defect ◽  
Human Androgen Receptor ◽  
Hsp 40

The Saccharomyces cerevisiae heat-shock protein (Hsp)40, Ydj1p, is involved in a variety of cellular activities that control polypeptide fate, such as folding and translocation across intracellular membranes. To elucidate the mechanism of Ydj1p action, and to identify functional partners, we screened for multicopy suppressors of the temperature-sensitive ydj1-151 mutant and identified a yeast Hsp110, SSE1. Overexpression of Sse1p also suppressed the folding defect of v-Src kinase in theydj1-151 mutant and partially reversed the α-factor translocation defect. SSE1-dependent suppression ofydj1-151 thermosensitivity required the wild-type ATP-binding domain of Sse1p. However, the Sse1p mutants maintained heat-denatured firefly luciferase in a folding-competent state in vitro and restored human androgen receptor folding in sse1mutant cells. Because the folding of both v-Src kinase and human androgen receptor in yeast requires the Hsp90 complex, these data suggest that Ydj1p and Sse1p are interacting cochaperones in the Hsp90 complex and facilitate Hsp90-dependent activity.

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.

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