TBC

The rampant COVID-19 pandemic has resulted in the known infection of hundreds of millions of people and led to the deaths of millions worldwide. First emerging as a pneumonia-like illness in Wuhan, China in early 2020, the disease now known as COVID-19 is caused by a novel strain of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Coronaviruses (CoVs) are enveloped 5'- capped, polyadenylated, single-stranded nonsegmented, positive sense RNA viruses that cause a range of diseases in animals. In humans, symptoms of CoV infection range from asymptomatic or common cold-like to lethal viral respiratory illness.

Whilst an unprecedented global vaccine effort has helped to flatten the infection curve, there are currently no highly-effective and specific direct-acting antivirals to treat CoV infection. This has led to fears of vaccine escape through viral mutation. Therefore, developing novel and targeted therapeutics for CoV represents an urgent medical need to combat the ongoing COVID-19 pandemic.

After infecting the host cell, CoVs assemble a multisubunit RNA-synthesis complex of viral nonstructural proteins (Nsp), which are responsible for the replication and transcription of the viral genome. Among the 16 known CoV Nsp proteins, the Nsp13 helicase is a critical component for viral replication and shares the highest sequence conservation across the CoV family, demonstrating their importance for viral spread. As such, this vital enzyme represents a highly promising target for anti-CoV drug development, both now and in the future.

Aims / Objectives:
Using medicinal chemistry to target small-molecule, cell-penetrant, specific inhibitors (probes), we will focus on the SARS-CoV-2 helicase (non-structual protein Nsp13) which is critical for viral replication and the most conserved non-structural protein within the coronavirus family. In collaboration with medicinal and computational teams at the University of North Carolina, Chapel Hill, we will carry out hit-to-lead chemical research to optimise early stage fragment hits.

Novelty of the methodology:
Raw electron density data generated from nsp13 crystallographic structures will be used to train an A.I. algorithm to generate virtual "hits" (pharmacophores). We will then then design analogues of the pharmacophores which can be rapidly synthesised using medicinal chemistry means, and then confirm these hits by assaying the new molecules, which in turn will allow us to improve the A.I. algorithm.

We will compare this (more traditional) structure-guided and target class chemistry approach with one that uses an alternative approach: ignoring the structures of the original hits and instead predicting (using FTMap) commercially available compounds that are structurally unrelated to the fragments bound, but which retain the key contacts. Both methods should provide hit identification and increase hit potency while maintaining or enhancing the drug-like properties of a series by using structure-activity relationship (SAR) trends. The research question is whether the traditional approach that builds on the fragments out-performs the computationally more demanding approach where the fragments are "ignored".

In the interests of increasing the research, development and molecule output, this project will all be done open source. This means that all data and ideas will be shared online in real time via GitHub:https://github.com/StructuralGenomicsConsortium/CNP4-Nsp13-C-terminus-B , so that anyone can participate at any level and there will be no IP rights claimed. This "open science" approach will mean that others will come to the project and contribute, creating a wide team of collaborators around the world seeking new coronavirus medicines that we synthesise and subsequently make available to anybody who needs it at low cost.