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Lookup NU author(s): Ayaz Ahmad, Dr Roly ArmstrongORCiD, Dr Mat Bieniek, Aaron Campbell, Dr Daniel ColeORCiD, Ben Cree, Kallie Friston, Dr Kate HarrisORCiD, Joshua Horton, Dr Rachael Pirie, Natalie Roper, Dr Natalie TatumORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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 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.
Author(s): Herasymenko O, Silva M, Abu-Saleh AA, Ahmad A, Alvarado-Huayhuaz J, Arce OEA, Armstrong RJ, Arrowsmith C, Bachta KE, Beck H, Berta D, Bieniek MK, Blay V, Bolotokova A, Bourne PE, Breznik M, Brown PJ, Campbell ADG, Carosati E, Chau I, Cole DJ, Cree B, Dehaen W, Denzinger K, dos Santos Machado K, Dunn I, Durai P, Edfeldt K, Edwards A, Fayne D, Felfoldi D, Friston K, Ghiabi P, Gibson E, Guenther J, Gunnarsson A, Hillisch A, Houston DR, Halborg Jensen J, Harding RJ, Harris KS, Hoffer L, Hogner A, Horton JT, Houliston S, Hultquist JF, Hutchinson A, Irwin JJ, Jukic M, Kandwal S, Karlova A, Katis VL, Kich RP, Kireev D, Koes D, Inniss NL, Lessel U, Liu S, Loppnau P, Lu W, Martino S, McGibbon M, Meiler J, Mettu A, Money-Kyrle S, Moretti R, Moroz YS, Muvva C, Newman JA, Obendorf L, Paige B, Pandit A, Park K, Perveen S, Pirie R, Poda G, Protopopov M, Putter V, Ricci F, Roper NJ, Rosta E, Rzhetskaya M, Sabnis Y, Satchell KJF, Schmitt Kremer F, Scott T, Seitova A, Steinmann C, Talagayev V, Tarkhanova OO, Tatum NJ, Treleaven D, Velasque Werhli A, Walters WP, Wang X, Wells J, Wells G, Westermaier Y, Wolber G, Wortmann L, Zhang J, Zhao Z, Zheng S, Schapira M
Publication type: Article
Publication status: Published
Journal: Journal of Chemical Information and Modeling
Year: 2025
Volume: 65
Issue: 13
Pages: 6884-6898
Online publication date: 20/06/2025
Acceptance date: 05/06/2025
Date deposited: 11/07/2025
ISSN (print): 1549-9596
ISSN (electronic): 1549-960X
Publisher: American Chemical Society
URL: https://doi.org/10.1021/acs.jcim.5c00535
DOI: 10.1021/acs.jcim.5c00535
ePrints DOI: 10.57711/e3b2-d314
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