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(Unpublished)
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Abstract
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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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| 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 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/39666 |
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