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Targeting the Poly (ADP-Ribose) Polymerase-1 Catalytic Pocket Using AutoGrow4, a Genetic Algorithm for De Novo Design

Spiegel, Jacob (2020) Targeting the Poly (ADP-Ribose) Polymerase-1 Catalytic Pocket Using AutoGrow4, a Genetic Algorithm for De Novo Design. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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AutoGrow4 is a free and open-source program for de novo drug design that uses a genetic algorithm (GA) to create novel predicted small-molecule ligands for a given protein target without the constraints of a finite, pre-defined virtual library. By leveraging recent computational and cheminformatic advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. Features such as docking-software compatibility, chemical filters, multithreading options, and selection methods have been expanded to support a wide range of user needs. This dissertation will cover the development and validation of AutoGrow4, as well as its application to poly (ADP-ribose) polymerase-1 (PARP-1).
PARP-1 is a well-characterized DNA-damage recognition protein, and PARP-1 inhibition is an effective treatment for ovarian and breast cancers that are homologous-recombination (HR) deficient1–5. As a well-studied protein, PARP-1 is also an excellent drug target with which to validate AutoGrow4. Multiple crystallographic structures of PARP-1 bound to various PARP-1 inhibitors (PARPi) serve as positive controls for assessing the quality of AutoGrow4-generated compounds in terms of predicted binding affinity, chemical structure, and predicted protein-ligand interactions.
This dissertation describes how I (1) generated novel potential PARPi with predicted binding affinities that surpass those of known PARPi; (2) validated AutoGrow4 as a tool for de novo drug design, lead optimization, and hypothesis generation, using PARP-1 as a test target; (3) contributed support to the growing notion that there is a need for HR-deficient cancer chemotherapies that do not rely on the same set of protein-ligand interactions typical of current PARPi; (4) generated novel potential PARPi that are predicted to bind to PARP-1 independent of a post-translational modification that is known to cause PARPi resistance; and (5) generated novel potential PARPi that are predicted to bind a secondary PARP-1 pocket that is distant from the primary catalytic site.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Spiegel, Jacobjspiegel@pitt.edujspiegel0000-0002-8496-6915
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairDurrant,
Committee MemberVanDemark,
Committee MemberVan Houten,
Committee MemberLawrence,
Date: 16 September 2020
Date Type: Publication
Defense Date: 10 March 2020
Approval Date: 16 September 2020
Submission Date: 19 July 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 413
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Molecular Biophysics and Structural Biology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: CADD, PARP-1, PARPi, lead-optimization, inhibitors, AutoGrow, AutoGrow4, Genetic Algorithm,
Date Deposited: 16 Sep 2020 15:03
Last Modified: 16 Sep 2020 15:03

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