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Ton, Anh-Tien

University of British Columbia

“Undruggable” or “difficult to drug” targets make up most of the human proteome; these proteins are described as such when it is considered impossible to pharmacologically target them. Therefore, “undruggable and difficult to drug” proteins represent significant challenges to the established drug discovery pipelines, but their successful inhibition would enable access to a wider range of therapeutic opportunities. In this thesis, we utilized various in silico tools to identify and design small molecule drug prototypes that could inhibit such undruggable and difficult targets. First, we introduced the recent development of new computer-aided drug design (CADD) methodologies and tools such as consensus docking, and Deep Docking. We then described the deployment of these innovative CADD methodologies to discover novel small molecule therapeutics against considered-as-difficult targets such as N-Myc, SARS-CoV-2 PLpro and Mpro. N-Myc is a highly desirable oncoprotein involved in many cancers and there is significant interest for targeting N-Myc in prostate cancer (PCa) and particularly in neuroendocrine prostate cancer (NEPC, an advanced, low-survival stage of PCa). However, N-Myc is considered an undruggable and unsuitable for small molecule inhibition due to its overall disordered structure. Thus, we developed new N-Myc specific small molecules with established and newly developed CADD protocols. Other examples of high value, but challenging targets are the viral main protease (Mpro) and the papain-like protease (PLpro) from severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2), as they are central pieces in its replication-transcription complex. However, intrinsic flexibility, multiple protonation state, solvent-exposed nature and other active site features unique to each protease decrease the success rate of conventional drug discovery protocols. Therefore, we discussed the identification of Mpro hits through naïve large virtual screening and ultra-large virtual screening that incorporated advanced consensus approaches. We also recapitulated the identification of new PLpro inhibitors through fine-tuned pharmacophore modelling and large-scale virtual screening with Deep Docking. In this thesis, we identified and designed small molecule inhibitors for each mentioned target of interest using state-of-the-art CADD methodologies. Notably, the compounds presented in this thesis provide the initial blueprints for the potential development of new anticancer and antiviral drugs.

Text

2023-09-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/82800

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/