Computer-Aided Drug Design (CADD) has deservedly gained increasing popularity in modern drug discovery (Schneider, G.; Fechner, U. 2005), whether applied to academic basic research or the pharmaceutical industry pipeline.
In this work, after reviewing theoretical advancements in CADD, we integrated novel and stateof- the-art methods to assist in the design of small-molecule inhibitors of current cancer drug targets, specifically: Androgen Receptor (AR), a nuclear hormone receptor required for carcinogenesis of Prostate Cancer (PCa); Signal Transducer and Activator of Transcription 5 (STAT5), implicated in PCa progression; and Epstein-Barr Nuclear Antigen-1 (EBNA1), essential to the Epstein Barr Virus (EBV) during latent infections.
Androgen Receptor. With the aim of generating binding mode hypotheses for a class (Handratta, V.D. et al. 2005) of dual AR/CYP17 inhibitors (CYP17 is a key enzyme for androgens biosynthesis and therefore implicated in PCa development), we successfully implemented a receptor-based computational strategy based on flexible receptor docking (Gianti, E.; Zauhar, R.J. 2012). Then, with the ultimate goal of identifying novel AR binders, we performed Virtual Screening (VS) by Fragment-Based Shape Signatures, an improved version of the original method developed in our Laboratory (Zauhar, R.J. et al. 2003), and we used the results to fully assess the high-level performance of this innovative tool in computational chemistry.
STAT5. The SRC Homology 2 (SH2) domain of STAT5 is responsible for phospho-peptide recognition and activation. As a keystone of Structure-Based Drug Design (SBDD), we characterized key residues responsible for binding. We also generated a model of STAT5 receptor bound to a phospho-peptide ligand, which was validated by docking publicly known STAT5 inhibitors. Then, we performed Shape Signatures- and docking-based VS of the ZINC database (zinc.docking.org), followed by Molecular Mechanics Generalized Born Surface Area (MMGBSA) simulations, paired with Principal Component Analysis (PCA) of top-scoring hits to identify novel lead molecules likely to be active against STAT5.
EBNA1 is the only viral protein consistently expressed in the many EBV-associated tumors, and is required for viral genome maintenance during latent infection. To immediately assist SBDD, we computationally identified “druggable” binding sites of EBNA1, and our predictions were later confirmed by experimental evidence (The Wistar Institute proprietary data).
|Advisor:||Zauhar, Randy J.|
|School:||University of the Sciences in Philadelphia|
|School Location:||United States -- Pennsylvania|
|Source:||DAI-B 75/07(E), Dissertation Abstracts International|
|Subjects:||Biochemistry, Molecular physics, Bioinformatics|
|Keywords:||Androgen receptors, Computer-Aided Drug Design, Drug discovery, Epstein-Barr virus|
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