Date of Award
9-28-2023
Publication Type
Dissertation
Degree Name
Ph.D.
Department
Chemistry and Biochemistry
Keywords
Biochemistry;CADD;Cancer;Cannabis;Computational Chemistry;Peptides
Supervisor
John Trant
Rights
info:eu-repo/semantics/embargoedAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
The thesis is divided into eight chapters exploring various diseases and proteins of pharmacological interest using computational methods to gain a better understanding of the mechanism and the underlying biology, and to use this information to design potential therapeutics. Chapter 1 provides information on the different targets and diseases examined and provides a rationale for their choice as targets and how computational chemistry can provide valuable insight into mechanisms and the potential for design of new therapeutics. Also discussed are notable techniques that are used during the investigations in subsequent chapters. In Chapters 2 the off-target binding of GUL based prostate cancer probes are investigated. Docking and molecular dynamics simulations are used to identify previously unknown targets, as well as to aid in the design of a new fluorescent based probe for use in in vitro studies. NAALADase1 and the metabotropic glutamate receptors were identified as the off-target binders. Chapter 3 shifts focus to understanding another important protein in cancer, Spy1. Here we explored the mechanism behind Spy1 induced CKI resistance using docking and MD based approaches to calculate the binding affinity of an array of inhibitors which revealed they have reduced binding affinity to the Spy1-CDK2 complex, resulting in their decreased effectiveness. In Chapter 4 there is a switch to my investigation of the CB1 receptor. Cannabinoids have the potential to be highly effective therapeutics and a model was developed for predicting the binding of cannabinoids to CB1. Also investigated was the mechanism through which compounds are capable of activating or inactivating CB1, particularly to explain partial agonism or partial antagonism. It was found that partial agonists induce a structural change in between the active and inactive receptor. Chapters 5 and 6 were focused on the design of peptides for the treatment of autoimmune disorders. Chapter 5 was a benchmarking study to evaluate the accuracy of FlexPepDock in the ranking of peptides. FlexPepDock performed well with canonical amino acids but was less accurate at ranking NCAA containing peptides, but more diverse data sets are required for evaluation. FlexPepDock did generate accurate binding poses. MD and MM-PB(GB)SA was also tested to determine accuracy in calculating binding affinities and evaluate several parameters as well as hydrophobicity corrections based on solvent water partition coefficients. Lastly a peptide was designed with FlexPepDock as a potential HLA-DQ2 blocker. In Chapter 6 a series of peptides were investigated computationally to determine their binding affinity to HLA-DR4 for use as HLA-blockers. Citrulline and homocitrulline containing peptides were found to be strong binders and are promising candidates. Also investigated were how the peptides interacted with mouse HLA receptors to determine how the peptides would behave in the common arthritis models. It was found that mouse HLAs have significantly different binding preferences and that these mice are not good for the evaluation of APLs and HLABs. Chapter 7 explores the design of stapled peptides as therapeutics as treatment for severe SARS-CoV-2 infections. A series of stapled peptides were designed, and their secondary structure evaluated to ensure peptides maintain an α-helix which is integral for their function. Their binding affinity was then calculated to ensure binding to Spike as well as several mutants from SARS-CoV-2 variants. Several α-helical peptides were identified with strong binding affinity to Spike across multiple variants. Finally in Chapter 8 results are summarized and interpreted in the context of CADD the current literature and progress on these targets. Much work is required before a functional therapeutic is obtained and a discussion is included on future work required to achieve this goal.
Recommended Citation
Meister, Daniel, "Cancers, Cannabinoids, COVID and Autoimmune Disorders: Investigating Biological Activity by Computational Chemistry" (2023). Electronic Theses and Dissertations. 9280.
https://scholar.uwindsor.ca/etd/9280