Date of Award
Fall 12-2023
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computational and Data Sciences
First Advisor
Gennady Verkhivker
Second Advisor
Cyril Rakovski
Third Advisor
Mohamed Allali
Abstract
The pandemic caused by the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 has caused a global public health crisis of nearly unprecedented scale. In the years following the outbreak, the scientific community has mobilized to develop several vaccines and treatments. Drug repurposing as a strategy for drug development has produced many of the current therapeutic options. The greatest challenge to designing a therapeutic inhibitor of SARS-CoV-2 is the shifting mutational landscape of the virus as it evolves. In this study, we focus on the spike protein as a target for potential inhibitors. We explore two methods of inhibiting spike function, allosteric inhibition and direct inhibition. In the study of allosteric inhibition, we screened two compound libraries against two allosteric sites. In the study of direct inhibition, several top-performing direct inhibitors of the wildtype spike were evaluated against five variants, B.1.1.7, B.1.351, P.1, B.1.617.2, and B.1.1.529. In summary, we identify four potential allosteric inhibitors that warrant further in-vitro study. We also find that the direct potential inhibitors of the wildtype spike had the most similar performance against the B.1.617.2 and B.1.1.7 variants.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
G. Gupta, "Computational molecular docking studies of small molecule inhibitors with the SARS-CoV-2 spike protein variants: In-silico drug discovery using virtual screening and drug repurposing approaches," M. S. thesis, Chapman University, Orange, CA, 2023. https://doi.org/10.36837/chapman.000515
Comments
This scholarship is part of the Chapman University COVID-19 Archives.