The development of molecular dynamics (MD) simulations builds off the maturing field of structural biology to provide new insight into the mechanisms of disease on an atomic level. However, there are few established methods that use these insights to develop effective therapies. This thesis begins by discussing the creation of “POVME 3.0”, a novel method to generate drug design-relevant insights from MD simulations. POVME 3.0 takes as input a MD simulation of a binding pocket of interest, and returns a summary of how the pocket shape changes over time. We then discuss the application of POVME 3.0 and other analysis techniques to the APOBEC3 family of proteins. APOBEC3 proteins are a newly discovered driver of mutation in many cancers, and their inhibition could contribute to cancer treatments. Next, we review algorithms that could be productively used in the study of APOBEC3 enzymes, both to understand their essential dynamics and also to discover new modes of inhibition. Finally, we discuss CELPP, a community-driven analysis of computer-aided drug design algorithms, which aims to improve the quality of predictive models in drug design.
|Advisor:||Amaro, Rommie E.|
|Commitee:||Abagyan, Ruben, Gilson, Michael K., Komives, Elizabeth A., Wang, Wei|
|School:||University of California, San Diego|
|School Location:||United States -- California|
|Source:||DAI-B 79/08(E), Dissertation Abstracts International|
|Subjects:||Computational chemistry, Biochemistry, Molecular physics|
|Keywords:||APOBEC3B, Allostery, Binding pocket, Cheminformatics, Docking, Molecular dynamics|
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