CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13
Oleksandra Herasymenko, Madhushika Silva, Abd Al‐Aziz A. Abu‐Saleh, Ayaz Ahmad, Jesus Antonio Alvarado-Huayhuaz, Oscar E. A. Arce, Roly J. Armstrong, C. Arrowsmith, Kelly E. R. Bachta, Hartmut Beck, Dénes Berta, M. Bieniek, Vincent Blay, Albina Bolotokova, Philip E. Bourne, Marko Breznik, Peter J. Brown, Aaron D. G. Campbell, Emanuele Carosati, Irene Chau, Daniel J. Cole, Ben Cree, Wim Dehaen, Katrin Denzinger, Karina Machado, Ian Dunn, Prasannavenkatesh Durai, Kristina Edfeldt, A.M. Edwards, Darren Fayne, F. Daniel Felfoldi, Kallie Friston, Pegah Ghiabi, Elisa Gibson, Judith Günther, Anders Gunnarsson, Alexander Hillisch, Douglas R. Houston, Jan H. Jensen, Rachel Harding, Claire L. Harris, Laurent Hoffer, Anders Hogner, Joshua T. Horton, Scott Houliston, Judd F. Hultquist, Ashley Hutchinson, John J. Irwin, Marko Jukič, Shubhangi Kandwal, Andrea Karlova, V.L. Katis, Ryan P. Kich, Dmitri Kireev, David Ryan Koes, Nicole L. Inniss, Uta Lessel, Sijie Liu, P. Loppnau, Wei Lu, Sam Martino, Miles McGibbon, Jens Meiler, Akhila Mettu, Sam Money-Kyrle, Rocco Moretti, Yurii S. Moroz, Charuvaka Muvva, J.A. Newman, Leon Obendorf, Brooks Paige, Amit Pandit, Keunwan Park, Sumera Perveen, Rachael Pirie, Gennady Poda, Mykola Protopopov, Vera Pütter, Federico Ricci, Natalie J. Roper, Edina Rosta, Margarita Rzhetskaya, Yogesh Sabnis, K.J.F. Satchell, Frederico Schmitt Kremer, T. W. Scott, Almagul Seitova, Casper Steinmann, Valerij Talagayev, Olga O. Tarkhanova, Natalie J. Tatum, Dakota Treleaven, Adriano Velasque Werhli, W. Patrick Walters, Xiaowen Wang, Jude Wells, Geoffrey Wells, Yvonne Westermaier, Gerhard Wolber, Lars Wortmann
Abstract
Abstract A 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 K d 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.