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

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 <i>K</i><sub>d</sub> below 10 μM and inhibited <i>in vitro</i> 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.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2025-06-20
DOI
https://doi.org/10.1021/acs.jcim.5c00535
Citations
8
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
12.03

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article

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

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8 citations
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
12.03
article

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

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

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 <i>K</i><sub>d</sub> below 10 μM and inhibited <i>in vitro</i> 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.

Journal of Chemical Information and ModelingVol. 65(13)
Ontario Institute for Cancer Research (CA), University of Messina (IT), University of North Carolina at Chapel Hill (US), University of Primorska (SI), University of Copenhagen (DK), Universidade Federal do Rio Grande (BR), Medpace (United States) (US), University Health Network (CA), Boehringer Ingelheim (Germany) (DE), Karolinska University Hospital (SE), XLAB (Slovenia) (SI), 3M (United States) (US), University of Trieste (IT), Universidade Federal de Pelotas (BR), University of Pittsburgh (US), Shanghai Jiao Tong University (CN), University of California, Santa Cruz (US), University of Toronto (CA), Taras Shevchenko National University of Kyiv (UA), Vanderbilt University (US), Trinity College Dublin (IE), Discovery Centre (CA), Princess Margaret Cancer Centre (CA), Health Net (US), Structural Genomics Consortium (CA), Karolinska Institutet (SE), University of Maribor (SI), University of Oxford (GB), International Life Sciences Institute (US), Joint Center for Structural Genomics (US), Office of Infectious Diseases (US), Relay Therapeutics (United States) (US), AstraZeneca (Brazil) (BR), Boehringer Ingelheim (Austria) (AT), Drug Discovery Laboratory (Norway) (NO), AstraZeneca (Sweden) (SE), Nuvisan (Germany) (DE), Dublin City University (IE), Naval Research Laboratory Information Technology Division, University College London (GB), University of Virginia (US), Korea Institute of Science and Technology (KR), Bayer (Germany) (DE), Freie Universität Berlin (DE), University of Missouri (US), Newcastle University (GB), Aalborg University (DK), University of Chemistry and Technology, Prague (CZ), University of Edinburgh (GB)
Boehringer Ingelheim, Bristol-Myers Squibb, Pfizer, Bayer, Vanderbilt University, Northwestern University, University of Pittsburgh, McGill University, Genome Canada, Merck KGaA, Innovation, Science and Economic Development Canada, Diamond Light Source, Ontario Genomics, European Federation of Pharmaceutical Industries and Associations, UK Research and Innovation, Ontario Genomics Institute, University of Oxford, Newcastle University, European Commission, University of Edinburgh, National Research Foundation, Monash University, Irish Research Council, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Korea Institute of Science and Technology, National Research Foundation of Korea, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, Kungliga Tekniska Högskolan, Javna Agencija za Raziskovalno Dejavnost RS, Univerza v Mariboru, Innovative Medicines Initiative, Ministry of Science and ICT, South Korea, Irish Centre for High-End Computing, National Institutes of Health, Genentech, Medical Research Council, Engineering and Physical Sciences Research Council, Research England, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases
Openalex Percentile: Top 1%
Computational Drug Discovery Methods
12.03
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