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Cheminformatics and Data Mining in Drug Discovery Targeting Bacterial Enoyl-ACP Reductase

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Title: Cheminformatics and Data Mining in Drug Discovery Targeting Bacterial Enoyl-ACP Reductase
Author(s): Su, Pin-Chih
Advisor(s): Johnson, Michael E.
Contributor(s): Minh, David; Jeong, Hyun Young; Petukhov, Pavel; Gaponenko, Vadim
Department / Program: Pharmacognosy
Graduate Major: Pharmacognosy
Degree Granting Institution: University of Illinois at Chicago
Degree: PhD, Doctor of Philosophy
Genre: Doctoral
Subject(s): Cheminformatics data mining drug discovery computer aided drug design enoyl acyl reductase (FabI) bacterial fatty acid synthesis tularemia Francisella tularensis Staphylococcus aureus antibiotic resistance virtual screening shape matching electrostatic matching docking lead optimization radii sets molecular dynamics quantum mechanics/molecular mechanics thermodynamic integration (TI) free energy calculation explicit solvent models implicit solvent models halogen bond benzimidazole-based inhibitors bioinformatics FabI essentiality enoyl acyl reductase (FabK), enoyl acyl reductase (FabV), enoyl acyl reductase (FabL)
Abstract: The enzymes of the bacterial fatty acid biosynthetic pathway (FAS II), represent attractive targets for antimicrobial drug design, because their mammalian counterpart (FAS I) uses a single, multifunctional enzyme with low sequence and structural similarity. This provides an opportunity to selectively target this essential bacterial pathway without interfering with mammalian enzymes that could result in off-target effects. The enoyl-[acyl-carrier-protein] reductase enzyme, FabI, catalyzes the reduction of a double bond in enoyl-ACP to acyl-ACP as a key step in the bacterial production of fatty acids. The FabI essentiality in the two target bacteria in this dissertation, F. tularensis and S. aureus, has been strongly proven in vivo. This thesis project is divided into three parts. The first part of the thesis project is to perform computer aided drug design techniques (virtual screening and data mining algorithms) to identify novel scaffolds FabI inhibitors, as highlighted in Chapter 2. In this hit discovery part, three novel FabI inhibitor scaffolds showed FabI enzymatic inhibitory activities. Among these three inhibitors, the initial SAR extension suggests that the benzimidazole scaffold inhibitors have tractable SAR in enzymatic activities and show Gram-positive and –negative anti-bacterial activities. Therefore, the benzimidazole scaffold was selected for further optimization. The second part is to build structure based computational models (both implicit and explicit solvent methods) to predict FabI benzimidazole inhibitors’ activities and to prioritize inhibitors to synthesize, as detailed in Chapter 3 & 4. The last part (Chapter 5) is to use bioinformatics analysis to identify more bacterial pathogens, of which FabI would be their only enoyl acyl reductase, in the hope to expand the inhibitory spectrum and clinical values of the developed FabI inhibitors. In summary, it is anticipated that the above computer aided lead optimization cycle will help generate benzimidazole inhibitors in sub-nM range for FabI enzymatic activities, and single digit or lower MICs (µg/mL) against the tested Gram-positive and –negative bacteria. Promising benzimidazole inhibitors will be selected for animal challenge and pharmacokinetic tests. We believe that FabI inhibitors will be developed into a FDA-approved antibiotic, and ease the biodefense and public health threats that our society is facing.
Issue Date: 2016-02-17
Genre: thesis
URI: http://hdl.handle.net/10027/20245
Rights Information: Copyright 2015 Pin-Chih Su
Date Available in INDIGO: 2018-02-18
Date Deposited: 2015-12
 

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