Computational Molecular Docking Models and Design of Diarylpentanoids for the Androgen Receptor

<p>The androgen receptor (AR) is a member of the nuclear receptor protein family that, upon binding to its natural ligand dihydrotestosterone (DHT) in the cytoplasm, translocates to the nucleus and exerts nuclear transcription factor activity to drive gene expression related to normal prostate development. AR signaling becomes overactive during the development and progression of prostate cancer through different mechanisms, including over-expression and mutation of the AR. Therefore, the AR is a prominent molecular target in the clinical management of prostate cancer. However, all therapeutic modalities targeting the AR, including androgen ablation therapy and AR block suffer from transient efficacy and invariably lead towards resistance and more aggressive, metastatic disease. Therefore, it is imperative to develop new therapeutic approaches. ca27 is a diarylpentanoid organic small molecule analog of the natural product curcumin that was previously shown to down-regulate the expression of the AR and to induce prostate cancer cell death. While its mechanism of action remains unknown, we hypothesized a potential physical interaction with the AR, leading to its degradation. In this research, we analyzed the AR in the DBD and LBD both in binding pockets and in the entire domain. The results indicate negligible binding to the DBD and positive binding in both binding models of the LBD. Further analysis of the models indicates the presence of a new binding pocket, hereby referred to as the ca27 binding pocket, in the LBD where ca27 and its various analogs bind in non-competition to the natural DHT ligand. These results were developed in MolSoft ICM Pro and further verified by AutoDock through 1-Click Docking. Further research into designing new ligands was performed however the results were inconclusive and needs further study.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computational and Data Sciences

Year dc:date.available

2020

Author dc:creator

• Guillow, Jarett

Contributors dc:contributor

• Marco Bisoffi
• Gennady Verkhivker
• Cedric Owens
• Andrew Orry

• Molecular Docking
• Androgen Receptor
• ca27
• MolSoft
• AutoDock
• Biochemistry
• Bioinformatics
• Biophysics

Repository record dc:identifier

https://digitalcommons.chapman.edu/cads_theses/7

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:cads_theses-1006