The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2020 has impacted daily life globally for over a year. While multiple vaccines have been authorized for emergency use and one oral medication has entered clinical trials, we are still seeking antiviral drugs for a long-term treatment for SARS-CoV-2 as well as other coronaviruses. Computational drug screenings of two SARS-CoV-2 protein target candidates are presented in this thesis: the nidoviral RNA uridylate-specific endoribonuclease (Nsp15) and the main protease (Mpro) of SARS-CoV-2. Nonstructural proteins of coronaviruses were selected as targets as they are more conserved across coronavirus strains than structural proteins. High throughput virtual screening of small molecule libraries including DrugBank and ZINC 15 resulted in several promising compounds for each of these targets. Molecular dynamics simulation allowed us to predict the binding energies for these compounds using molecular mechanics with generalized born surface area solvation calculations (MM-GBSA). Four top compounds were discovered for Nsp15 and eight compounds for Mpro.
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Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing
Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs.
more » « less- Award ID(s):
- 1651995
- NSF-PAR ID:
- 10214231
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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