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Computational High Throughput Screening Targeting DNA Repair Proteins To Improve Cancer Therapy Open Access


Other title
Drug Design
Virtual Screening
In Silico
DNA repair
DNA pol beta
Type of item
Degree grantor
University of Alberta
Author or creator
Barakat, Khaled H.
Supervisor and department
Jack A. Tuszynski, Department of Physics
Examining committee member and department
Morsink, Sharon (Physics)
Woodside, Michael (Physics)
Danani, Andrea (Head of Research Lab. of Applied Mathematics and Physics, University of Applied Sciences of Southern Switzerland, Manno-CH )
Klobukowski, Mariusz (Chemistry)
Kurgan, Lukasz (Electrical and Computer Engineering)
Tuszynski, Jack A. (Physics)
Department of Physics

Date accepted
Graduation date
Doctor of Philosophy
Degree level
Developing a new drug is a complex, highly structured, and expensive task. The further a potential drug progresses in the development process, the more costly its failure becomes. Virtual screening (VS) is the initial stage of a drug discovery process. Its job is to screen large compound databases for bioactive molecules. Its role is critical to reduce the probability of late-stage expensive failures. A reliable VS protocol would identify a diversity of lead compounds that are suitable for further structural optimizations. Most of the current available protocols fail at integrating the target flexibility or suggesting accurate ranking for the selected top hits. Here, we introduce an improved virtual screening protocol. A protocol that improves over current methodologies by employing complementary techniques comprising molecular docking, molecular dynamics simulations, iterative clustering techniques, principle component analysis and accurate scoring methods. The implemented VS protocol identified novel compounds that can bind to a number of important cancer-related targets. The targets chosen here play critical roles in tumor cell initiation and progression and their regulation promises for the improvement of current cancer therapy. Two of these important targets are DNA repair proteins that are linked to the hallmark “relapse” or “drug resistance” phenomena. These are Excision Repair Cross-Complementation Group 1 (ERCC1), and DNA polymerase beta (pol β). The former is a key player in Nucleotide Excision Repair (NER), while the latter is the error-prone polymerase of Base Excision Repair (BER). The third target is p53, a guardian of the genome that is inactivated in more than half of all human cancers. The work presented here has an outstanding significance on both the methods and their applications. On one hand the implemented protocol is generic and can be used almost against any target. On the other hand, the compounds we identified have the promise of being successful potential drug candidates that can progress through the drug discovery process and improve cancer therapy.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Barakat KH, Mane JY, Tuszynski JA (2011) Virtual Screening: An Overview on Methods and Applications. In: Liu LA, Wei D, Li Y, Lei H, editors. Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications: IGI.Barakat K, H, Tuszynski J (2011) Virtual Screening for DNA Repair Inhibitors. In: Storici F, editor. DNA Repair - On the Pathways to Fixing DNA Damage and Errors. 1 ed. Rijeka: InTech.Barakat KH, Torin Huzil J, Luchko T, Jordheim L, Dumontet C, Tuszynski J. J Mol Graph Model. 2009 Sep; 28(2): 113-30."DNA polymerase beta (pol ß) inhibitors: a comprehensive overview" Khaled H. Barakat, Melissa M. Gajewski and Jack A. Tuszynski. submitted to Drug Discovery Today, Jan, 2012Barakat K, Tuszynski J. J Mol Graph Model. 2011 Feb;29(5):702-16Barakat K, Mane J, Friesen D, Tuszynski J. J Mol Graph Model. 2010 Feb 26;28(6):555-68.Barakat K, Issack BB, Stepanova M, Tuszynski J. PLoS One. 2011; 6(11): e27651.

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File title: Computational High Throughput Screening Targeting DNA Repair Proteins To Improve Cancer Therapy, Ph. D. Thesis
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