Experimental and computational methods for identification of novel fungal histone acetyltransferase Rtt109 inhibitors

University of Minnesota Ph.D. dissertation. February 2014. Major: Medicinal Chemistry. Advisor: Elizabeth A. Amin. 1 computer file (PDF); xii, 180 pages.Rtt109 is a fungal-specific histone acetyltransferase that catalyzes histone H3 lysine 56 acetylation and is a promising antifungal drug target. To identify novel Rtt109 inhibitors as potential drug scaffolds, we employed in vitro high throughput screening (HTS) and various computer-assisted strategies, including molecular dynamics, docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis, pharmacophore modeling, and Support Vector Machine (SVM) mining. An initial experimental screening of 82,861 compounds (HTS1) yielded hits with activity ranging from 0.49 - 17.5 µM against Rtt109. The molecular dynamics simulation of Rtt109 suggested that the histone lysine tunnel, a potential inhibitor binding site, was not flexible and thus the use of a rigid protein structure of Rtt109 was appropriate for docking studies. From a virtual screen using Surflex-Dock, we have identified 878 additional compounds as potential hits, with predicted Kd values of 0.1 nm or lower. Based on preliminary experimental data from HTS1, validated pharmacophore maps were developed and used to pinpoint potential Rtt109 ligand-receptor interactions. 3D-QSAR CoMFA and CoMSIA models that were also derived from the hit series generated in the initial experimental HTS display high self-consistency (r2 = 0.985 [CoMFA] and r2 = 0.976 [CoMSIA]) and robust internal predictivity (rcv2 = 0.754 [CoMFA] and rcv2 = 0.654 [CoMSIA]). Importantly, key features identified in both the pharmacophore hypotheses and the 3D-QSAR models agreed well with each other and with experimentally defined structural features in the Rtt109 lysine-binding tunnel. In addition, our optimized SVM models demonstrated high predictive power against the external test sets for Rtt109 with accuracy of 91.1%. We also identified novel features with significant differentiating ability to separate Rtt109 inhibitors from non-inhibitors

Targeting protein kinases to manage or prevent Alzheimer’s disease

Due to the pressing need for new disease-modifying drugs for Alzheimer’s disease (AD), new treatment strategies and alternative drug targets are currently being heavily researched. One such strategy is to modulate protein kinases such as cyclin-dependent kinase 1 (CDK1), cyclin-dependent kinase 5 (CDK5), glycogen synthase kinase-3 (GSK-3α and GSK-3β), and the protein kinase RNA-like endoplasmic reticulum kinase (PERK). AD intervention by reduction of amyloid beta (Aβ) levels is also possible through development of protein kinase C-epsilon (PKC-ϵ) activators to recover α-secretase levels and decrease toxic Aβ levels, thereby restoring synaptogenesis and cognitive function. In this way, we aim to develop new AD drugs by targeting kinases that participate in AD pathophysiology. In our studies, comparative modeling was performed to construct 3D models for kinases whose crystal structures have not yet been identified. The information from structurally similar proteins was used to define the amino acid residues in the ATP binding site as well as other important sites and motifs. We searched for the comstructural motifs and domains of GSK-3β, CDK5 and PERK. Further, we identified the conserved water molecules in GSK-3β, CDK5 and PERK through calculation of the degree of water conservation. We investigated the protein-ligand interaction profiles of CDK1, CDK5, GSK-3α, GSK-3β and PERK based on molecular dynamics (MD) simulations, which provided a time-dependent demonstration of the interactions and contacts for each ligand. In addition, we explored the protein-protein interactions between CDK5 and p25. Small molecules which target this interaction may offer a prospective therapeutic benefit for AD. In order to identify new modulators for protein kinase targets in AD, we implemented three virtual screening protocols. The first protocol was a combined ligand- and protein structure-based approach to find new PERK inhibitors. In the second protocol, protein structure-based virtual screening was applied to find multiple-kinase inhibitors through parallel docking simulations into validated models of CDK1, CDK5 and GSK-3 kinases. In the third protocol, we searched for potential activators of PKC-ϵ based on the structure of its C1B domain

Development of Virtual Screening and In Silico Biomarker Identification Model for Pharmaceutical Agents

Ph.DDOCTOR OF PHILOSOPH

Principal Component Analysis

This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as taxonomy, biology, pharmacy,finance, agriculture, ecology, health and architecture

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Chemical Information Bulletin

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Pain Management

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