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. 2017:2017:3572394.
doi: 10.1155/2017/3572394. Epub 2017 Feb 15.

Combined Ligand/Structure-Based Virtual Screening and Molecular Dynamics Simulations of Steroidal Androgen Receptor Antagonists

Yuwei Wang  1 , Rui Han  1 , Huimin Zhang  1 , Hongli Liu  1 , Jiazhong Li  1 , Huanxiang Liu  1 , Paola Gramatica  2
Affiliations

Combined Ligand/Structure-Based Virtual Screening and Molecular Dynamics Simulations of Steroidal Androgen Receptor Antagonists

Yuwei Wang et al. Biomed Res Int. 2017.

Abstract

The antiandrogens, such as bicalutamide, targeting the androgen receptor (AR), are the main endocrine therapies for prostate cancer (PCa). But as drug resistance to antiandrogens emerges in advanced PCa, there presents a high medical need for exploitation of novel AR antagonists. In this work, the relationships between the molecular structures and antiandrogenic activities of a series of 7α-substituted dihydrotestosterone derivatives were investigated. The proposed MLR model obtained high predictive ability. The thoroughly validated QSAR model was used to virtually screen new dihydrotestosterones derivatives taken from PubChem, resulting in the finding of novel compounds CID_70128824, CID_70127147, and CID_70126881, whose in silico bioactivities are much higher than the published best one, even higher than bicalutamide. In addition, molecular docking, molecular dynamics (MD) simulations, and MM/GBSA have been employed to analyze and compare the binding modes between the novel compounds and AR. Through the analysis of the binding free energy and residue energy decomposition, we concluded that the newly discovered chemicals can in silico bind to AR with similar position and mechanism to the reported active compound and the van der Waals interaction is the main driving force during the binding process.

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Conflict of interest statement

The authors declare that there is no conflict of interests regarding the publication of this paper.

Figures

Figure 1
Figure 1
The skeleton structure of 7α-substituted dihydrotestosterones derivatives.
Figure 2
Figure 2
The distribution of R2 and Q2 of 5000 iterated Y-scrambled models in comparison to the proposed model performances.
Figure 3
Figure 3
The scatter plot of the experimental versus the predicted pIC50 by MLR model.
Figure 4
Figure 4
The Williams plot of final MLR model.
Figure 5
Figure 5
Insubria graph (plot of hat values versus predicted values for the complete compounds).
Figure 6
Figure 6
The molecular binding models of compound 4 (cyan), CID_70128824 (yellow), CID_70126881 (pink), and CID_70127147 (blue) in the AR ligand binding site.
Figure 7
Figure 7
Time series of (a) the RMSDs of backbone atoms of androgen receptor, (b) the RMSD of Cα atoms for the residues around 5 Å of the ligand, and (c) the RMSD of the heavy atoms of ligand.
Figure 8
Figure 8
Energy decomposition of key residues in four complexes.
See this image and copyright information in PMC

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