High-resolution structure, function, and anti-viral inhibition of the SARS-CoV2 E protein ion channel
- Funded by UK Research and Innovation (UKRI)
- Total publications:0 publications
Grant number: BB/V01997X/1
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Key facts
Disease
COVID-19Start & end year
20212022Known Financial Commitments (USD)
$699,363.21Funder
UK Research and Innovation (UKRI)Principal Investigator
Ulrich ZachariaeResearch Location
United KingdomLead Research Institution
University of DundeeResearch Priority Alignment
N/A
Research Category
Pathogen: natural history, transmission and diagnostics
Research Subcategory
Pathogen morphology, shedding & natural history
Special Interest Tags
N/A
Study Type
Unspecified
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
The Covid19-causing SARS-coronavirus-2 (SARS-CoV2) contains a small number of proteins, three of which reside in its membrane. They include the spike protein (S), responsible for attachment to the host, the membrane protein (M), and a cation-channel (membrane pore) formed by the envelope protein (E), CoV2E. Cation flow through the E-protein of SARS-coronaviruses (SARS-CoV) plays a role in virus replication in host cells. Inhibitors of the E-protein channel have been shown to substantially diminish virulence of SARS-CoV, the coronavirus responsible for the SARS outbreak in 2003. Inhibitors of CoV2E cation flux are expected to attenuate SARS-CoV2 pathogenicity and their discovery is the goal of this application. Repurposing drugs originally developed for other diseases offer a fast-track to new treatments. These will be included in the current study to expedite delivery of effective drugs. Structure-based drug design is another means to accelerate the discovery of drugs by enabling focused, rational approaches to design and repurposing. However, the structure of CoV2E is only partially known. We thus propose to (i) solve high-resolution crystal-structures of CoV2E, (ii) apply computational electrophysiology and in silico screens including cheminformatics/machine learning approaches to identify CoV2E inhibitors from libraries of commercially available and repurposing drugs, and (iii) perform lead validation and further development of inhibitors by electrophysiology and crystallography.