Journal of chemical information and modeling · 2025

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

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

Аннотация

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.

Предметные рубрики MeSH

Viral Nonstructural Proteins Coronavirus RNA-Dependent RNA Polymerase RNA Helicases Binding Sites Ligands Deep Learning RNA

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

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Аннотация

Предметные рубрики MeSH