CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV‑2 Helicase Nsp13

AbstractA critical assessment of computational
hit-finding experiments
(CACHE) challenge was conducted to predict ligands for the SARS-CoV-2
Nsp13 helicase RNA binding site, a highly conserved COVID-19 target.
Twenty-three participating teams comprised of computational chemists
and data scientists used protein structure and data from fragment-screening
paired with advanced computational and machine learning methods to
each predict up to 100 inhibitory ligands. Across all teams, 1957
compounds were predicted and were subsequently procured from commercial
catalogs for biophysical assays. Of these compounds, 0.7% were confirmed
to bind to Nsp13 in a surface plasmon resonance assay. The six best-performing
computational workflows used fragment growing, active learning, or
conventional virtual screening with and without complementary deep-learning
scoring functions. Follow-up functional assays resulted in identification
of two compound scaffolds that bound Nsp13 with a Kd below 10 μM and inhibited in vitro helicase activity. Overall, CACHE #2 participants were successful
in identifying hit compound scaffolds targeting Nsp13, a central component
of the coronavirus replication-transcription complex. Computational
design strategies recurrently successful across the first two CACHE
challenges include linking or growing docked or crystallized fragments
and docking small and diverse libraries to train ultrafast machine-learning
models. The CACHE #2 competition reveals how crowd-sourcing ligand
prediction efforts using a distinct array of approaches followed with
critical biophysical assays can result in novel lead compounds to
advance drug discovery efforts.