Lestaurtinib Inhibits Histone Phosphorylation and Androgen-Dependent Gene Expression in Prostate Cancer Cells

Background: Epigenetics is defined as heritable changes in gene expression that are not based on changes in the DNA sequence. Posttranslational modification of histone proteins is a major mechanism of epigenetic regulation. The kinase PRK1 (protein kinase C related kinase 1, also known as PKN1) phosphorylates histone H3 at threonine 11 and is involved in the regulation of androgen receptor signalling. Thus, it has been identified as a novel drug target but little is known about PRK1 inhibitors and consequences of its inhibition. Methodology/Principal Finding: Using a focused library screening approach, we identified the clinical candidate lestaurtinib (also known as CEP-701) as a new inhibitor of PRK1. Based on a generated 3D model of the PRK1 kinase using the homolog PKC-theta (protein kinase c theta) protein as a template, the key interaction of lestaurtinib with PRK1 was analyzed by means of molecular docking studies. Furthermore, the effects on histone H3 threonine phosphorylation and androgen-dependent gene expression was evaluated in prostate cancer cells. Conclusions/Significance: Lestaurtinib inhibits PRK1 very potently in vitro and in vivo. Applied to cell culture it inhibits histone H3 threonine phosphorylation and androgen-dependent gene expression, a feature that has not been known yet. Thus our findings have implication both for understanding of the clinical activity of lestaurtinib as well as for future PRK

Application of Consensus Scoring and Principal Component Analysis for Virtual Screening against β-Secretase (BACE-1)

BACKGROUND: In order to identify novel chemical classes of β-secretase (BACE-1) inhibitors, an alternative scoring protocol, Principal Component Analysis (PCA), was proposed to summarize most of the information from the original scoring functions and re-rank the results from the virtual screening against BACE-1. METHOD: Given a training set (50 BACE-1 inhibitors and 9950 inactive diverse compounds), three rank-based virtual screening methods, individual scoring, conventional consensus scoring and PCA, were judged by the hit number in the top 1% of the ranked list. The docking poses were generated by Surflex, five scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, and PMF_Score) were used for pose extraction. For each pose group, twelve scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, PMF_Score, LigScore1, LigScore2, PLP1, PLP2, jain, Ludi_1, and Ludi_2) were used for the pose rank. For a test set, 113,228 chemical compounds (Sigma-Aldrich® corporate chemical directory) were docked by Surflex, then ranked by the same three ranking methods motioned above to select the potential active compounds for experimental test. RESULTS: For the training set, the PCA approach yielded consistently superior rankings compared to conventional consensus scoring and single scoring. For the test set, the top 20 compounds according to conventional consensus scoring were experimentally tested, no inhibitor was found. Then, we relied on PCA scoring protocol to test another different top 20 compounds and two low micromolar inhibitors (S450588 and 276065) were emerged through the BACE-1 fluorescence resonance energy transfer (FRET) assay. CONCLUSION: The PCA method extends the conventional consensus scoring in a quantitative statistical manner and would appear to have considerable potential for chemical screening applications

Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review

Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers’ practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques