Computer-Aided Design of Peptide Ligands to SARS-CoV-2 Targets

Investigators: Paul Keim, Nagarajan Vaidehi, Supriyo Bhattacharya, John Altin, Erik Settles, Jason Ladner
Affiliation: Beckman Research Institute of the City of Hope
More Information: NERSC’s Scientific Computing Power Steps into Coronavirus Battle

Supriyo Bhattacharya is an Assistant Research Professor at the City of Hope where he works on small molecule drug design, peptide and antibody design, protein-protein interaction inhibition, RNA design, nanoparticle design for drug delivery, understanding protein conformational dynamics.

The goal of this project is to design peptides and small molecules that will bind to the coronavirus surface proteins and inhibit their binding to human proteins. Besides potential therapy, these inhibitory peptides and molecules are useful for understanding the mechanisms of virus entry and interaction with the human host and the immune system.

The project will start by simulating the viral Spike protein bound to human ACE2 which the virus uses for invading the lungs. NERSC computing resources will be used for running MD simulations on the complex of Spike bound to ACE2. These simulations will be then used for designing peptides and small molecules to disrupt the interactions between Spike and ACE2. The team has a collaboration with collaborators at TGen and Northeastern University to test the binding of the designed peptides and compounds.

Small molecules can be used as probes for understanding the interaction of the SARS-Cov2 virus with its host and offer future potential as diagnostic and therapeutic agents. Protein-protein interactions are critical to viruses as they infect host cells and the ability to disrupt this with small-molecule ligands will lead to a greater understanding of viral biology. The most prominent feature of the coronavirus virion is the spike protein and it is known to be critical in the infectious process. We will use the molecular structure of the spike protein to identify potential ligand binding sites and then design peptides (11-20 amino acids) as potentially high-affinity ligands. Our computational search will identify a large number of potential candidates that can be included in a single peptide library and tested empirically for binding. A computational search for peptide ligands that bind to the spike protein will be performed using the Rosetta algorithm in conjunction with distributed high performance computing. The best peptide sequences will then be connected together by peptide linkers to attain further affinity gain. The top 100,000 peptide sequences obtained by this approach will be synthesized for rapid testing in in-vitro assays for Spike binding. The top peptide candidates will also be tested on SARS-Cov-2 grown on human tissue culture cells and murine COVID19 models that are being developed.

Video: One microsecond molecular dynamics simulation of SARS-Cov2 Spike bound to human ACE2; The Spike is colored in cyan and ACE2 in orange. The surface glycans are shown as spheres.