Argonne-FNL Collaboration Aims to Find a SARS-CoV-2 Inhibitor
A unique library of small-molecule fragments designed and developed by the National Cancer Institute’s RAS Initiative at the Frederick National Laboratory is now furthering the search for a COVID-19 treatment.
Scientists use this unique library to conduct disulfide tethering, a fragment-based drug discovery method that determines whether any of the library’s compounds can bind to target proteins. Originally, that target was the cancer-driving K-Ras protein mutation. Now, a collaboration between FNL and Argonne National Laboratory is using the same process to search for a molecule that could act against SARS-CoV-2, the virus that causes COVID-19.
Certain SARS-CoV-2 proteins are required for the virus to reproduce, and those proteins contain specific amino acids that could be targeted with the molecules in the fragment library. If any of the fragments can bind to the SARS-CoV-2 proteins, they could potentially interrupt the process that allows the virus to spread.
FNL’s Protein Expression Laboratory, led by Dominic Esposito, Ph.D., recently purified the two relevant SARS-CoV-2 proteins for testing. Anna Maciag, Ph.D., and her colleagues in the RAS Initiative have since begun screening the roughly 1,200 molecules for any fragments that bind well to the SARS-CoV-2 proteins. Once finished, they will send the data to Argonne, where a team will use machine learning to further examine the fragment hits.
Argonne’s Rick Stevens, Ph.D., and Arvind Ramanathan, Ph.D., will use some of the most advanced supercomputers in the world to run millions of simulations that will narrow the list of potential inhibitors to the most-promising candidates.
Based on the machine learning data, FNL chemists led by David Turner, Ph.D., will resynthesize the most-promising fragments into further optimized versions. (Artificial intelligence can tell the scientists not only which fragments are the most promising but also how to structure them most effectively). They will then send these enhanced compounds back to Argonne for crystallization, which may give the team all the information they need to build an inhibitor-like molecule that can be tested in vitro.
“For us, it’s a great opportunity to utilize RAS resources and contribute,” Maciag said. “As a national lab, this is part of our mission: to help with national emergencies like this one.”
The collaboration between FNL and Argonne is advantageous for both parties. Screening data from FNL’s unique small molecule library can help Argonne test its artificial intelligence algorithms. Then, in an iterative process that marries computation and biology, FNL can validate the algorithm’s results in actual experiments and send the data back to Argonne, where scientists will use the data to refine and further improve the algorithm.