Dr. Gareth Williams, PhD
Cumming School of Medicine, Department of Biochemistry and Molecular Biology
Arnie Charbonneau Cancer Institute
Robson DNA Science Centre
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B.S. Biochemistry and genetics, University of Nottingham, 2002
Doctor of Philosophy Biology, University of St. Andrews, 2006
Dr. Gareth Williams is an Assistant Professor in the Department of Biochemistry & Molecular Biology. He earned his PhD from the University of St Andrews (Scotland, UK) in 2006 and trained at Lawrence Berkeley National Laboratory (California, USA) until the end of 2015. In February 2016, he opened his own laboratory within the Arnie Charbonneau Cancer Institute at the University of Calgary’s Cumming School of Medicine.
Areas of Research
- Structure-based mechanisms of genomic instability and cancer
The Williams lab applies structural biology approaches to determine molecular mechanisms of genomic instability and cancer. A main interest in the lab is to understand the molecular basis for key steps in the homologous recombination repair (HRR) pathway. HRR plays a critical role in maintaining genomic stability by accurately repairing DNA double strand breaks and inter-strand crosslinks, the most toxic forms of DNA damage, as well as damaged replication forks. The importance of HRR for protecting against cancer is highlighted by inherited mutations in HRR genes (including BRCA1, BRCA2, and the RAD51 paralogs) that predispose to breast and ovarian cancers. Using hybrid structural techniques, with a focus on combining small-angle X-ray scattering with macromolecular X-ray crystallography, we can determine the structural basis for protein-protein and protein-DNA interactions, as well as the effect of ATP binding and hydrolysis on macromolecular conformational changes and assembly states. Using structure-based insights we design mutations to perturb interactions and activities, which are then used in biochemical and functional assays to inform the underlying biology. Furthermore, our structures and approaches provide a molecular framework that can be used to both understand the effect of disease associated mutations, and to guide the design of future cancer therapies.
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