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Preprint Dans Anglais | bioRxiv | ID: ppbiorxiv-468428


We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized. ACM Reference FormatAbigail Dommer1{dagger}, Lorenzo Casalino1{dagger}, Fiona Kearns1{dagger}, Mia Rosenfeld1, Nicholas Wauer1, Surl-Hee Ahn1, John Russo,2 Sofia Oliveira3, Clare Morris1, AnthonyBogetti4, AndaTrifan5,6, Alexander Brace5,7, TerraSztain1,8, Austin Clyde5,7, Heng Ma5, Chakra Chennubhotla4, Hyungro Lee9, Matteo Turilli9, Syma Khalid10, Teresa Tamayo-Mendoza11, Matthew Welborn11, Anders Christensen11, Daniel G. A. Smith11, Zhuoran Qiao12, Sai Krishna Sirumalla11, Michael OConnor11, Frederick Manby11, Anima Anandkumar12,13, David Hardy6, James Phillips6, Abraham Stern13, Josh Romero13, David Clark13, Mitchell Dorrell14, Tom Maiden14, Lei Huang15, John McCalpin15, Christo- pherWoods3, Alan Gray13, MattWilliams3, Bryan Barker16, HarindaRajapaksha16, Richard Pitts16, Tom Gibbs13, John Stone6, Daniel Zuckerman2*, Adrian Mulholland3*, Thomas MillerIII11,12*, ShantenuJha9*, Arvind Ramanathan5*, Lillian Chong4*, Rommie Amaro1*. 2021. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy ofDeltaSARS-CoV-2 in a Respiratory Aerosol. In Supercomputing 21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis. ACM, New York, NY, USA, 14 pages.

Preprint Dans Anglais | bioRxiv | ID: ppbiorxiv-437323


Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel non-covalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 {micro}M [95% CI 2.2, 4.0]. Further, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple {micro}s-timescale molecular dynamics (MD) simulations, and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design. O_TEXTBOXSignificance StatementThe ongoing novel coronavirus pandemic (COVID-19) has prompted a global race towards finding effective therapeutics that can target the various viral proteins. Despite many virtual screening campaigns in development, the discovery of validated inhibitors for SARS-CoV-2 protein targets has been limited. We discover a novel inhibitor against the SARS-CoV-2 main protease. Our integrated platform applies downstream biochemical assays, X-ray crystallography, and atomistic simulations to obtain a comprehensive characterization of its inhibitory mechanism. Inhibiting Mpro can lead to significant biomedical advances in targeting SARS-CoV-2 treatment, as it plays a crucial role in viral replication. C_TEXTBOX

- The COVID Moonshot Initiative; Hagit Achdout; Anthony Aimon; Elad Bar-David; Haim Barr; Amir Ben-Shmuel; James Bennett; Vitaliy A. Bilenko; Vitaliy A. Bilenko; Melissa L. Boby; Bruce Borden; Gregory R. Bowman; Juliane Brun; Sarma BVNBS; Mark Calmiano; Anna Carbery; Daniel Carney; Emma Cattermole; Edcon Chang; Eugene Chernyshenko; John D. Chodera; Austin Clyde; Joseph E. Coffland; Galit Cohen; Jason Cole; Alessandro Contini; Lisa Cox; Milan Cvitkovic; Alex Dias; Kim Donckers; David L. Dotson; Alice Douangamath; Shirly Duberstein; Tim Dudgeon; Louise Dunnett; Peter K. Eastman; Noam Erez; Charles J. Eyermann; Mike Fairhead; Gwen Fate; Daren Fearon; Oleg Fedorov; Matteo Ferla; Rafaela S. Fernandes; Lori Ferrins; Richard Foster; Holly Foster; Ronen Gabizon; Adolfo Garcia-Sastre; Victor O. Gawriljuk; Paul Gehrtz; Carina Gileadi; Charline Giroud; William G. Glass; Robert Glen; Itai Glinert; Andre S. Godoy; Marian Gorichko; Tyler Gorrie-Stone; Ed J. Griffen; Storm Hassell Hart; Jag Heer; Michael Henry; Michelle Hill; Sam Horrell; Victor D. Huliak; Matthew F.D. Hurley; Tomer Israely; Andrew Jajack; Jitske Jansen; Eric Jnoff; Dirk Jochmans; Tobias John; Steven De Jonghe; Anastassia L. Kantsadi; Peter W. Kenny; J. L. Kiappes; Serhii O. Kinakh; Lizbe Koekemoer; Boris Kovar; Tobias Krojer; Alpha Lee; Bruce A. Lefker; Haim Levy; Ivan G. Logvinenko; Nir London; Petra Lukacik; Hannah Bruce Macdonald; Beth MacLean; Tika R. Malla; Tatiana Matviiuk; Willam McCorkindale; Briana L. McGovern; Sharon Melamed; Kostiantyn P. Melnykov; Oleg Michurin; Halina Mikolajek; Bruce F. Milne; Aaron Morris; Garrett M. Morris; Melody Jane Morwitzer; Demetri Moustakas; Aline M. Nakamura; Jose Brandao Neto; Johan Neyts; Luong Nguyen; Gabriela D. Noske; Vladas Oleinikovas; Glaucius Oliva; Gijs J. Overheul; David Owen; Ruby Pai; Jin Pan; Nir Paran; Benjamin Perry; Maneesh Pingle; Jakir Pinjari; Boaz Politi; Ailsa Powell; Vladimir Psenak; Reut Puni; Victor L. Rangel; Rambabu N. Reddi; St Patrick Reid; Efrat Resnick; Emily Grace Ripka; Matthew C. Robinson; Ralph P. Robinson; Jaime Rodriguez-Guerra; Romel Rosales; Dominic Rufa; Kadi Saar; Kumar Singh Saikatendu; Chris Schofield; Mikhail Shafeev; Aarif Shaikh; Jiye Shi; Khriesto Shurrush; Sukrit Singh; Assa Sittner; Rachael Skyner; Adam Smalley; Bart Smeets; Mihaela D. Smilova; Leonardo J. Solmesky; John Spencer; Claire Strain-Damerell; Vishwanath Swamy; Hadas Tamir; Rachael Tennant; Warren Thompson; Andrew Thompson; Susana Tomasio; Igor S. Tsurupa; Anthony Tumber; Ioannis Vakonakis; Ronald P. van Rij; Laura Vangeel; Finny S. Varghese; Mariana Vaschetto; Einat B. Vitner; Vincent Voelz; Andrea Volkamer; Frank von Delft; Annette von Delft; Martin Walsh; Walter Ward; Charlie Weatherall; Shay Weiss; Kris M. White; Conor Francis Wild; Matthew Wittmann; Nathan Wright; Yfat Yahalom-Ronen; Daniel Zaidmann; Hadeer Zidane; Nicole Zitzmann.
Preprint Dans Anglais | bioRxiv | ID: ppbiorxiv-339317


The COVID-19 pandemic is a stark reminder that a barren global antiviral pipeline has grave humanitarian consequences. Future pandemics could be prevented by accessible, easily deployable broad-spectrum oral antivirals and open knowledge bases that derisk and accelerate novel antiviral discovery and development. Here, we report the results of the COVID Moonshot, a fully open-science structure-enabled drug discovery campaign targeting the SARS-CoV-2 main protease. We discovered a novel chemical scaffold that is differentiated from current clinical candidates in terms of toxicity, resistance, and pharmacokinetics liabilities, and developed it into noncovalent orally-bioavailable nanomolar inhibitors with clinical potential. Our approach leveraged crowdsourcing, high-throughput structural biology, machine learning, and exascale molecular simulations. In the process, we generated a detailed map of the structural plasticity of the main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. In a first for a structure-based drug discovery campaign, all compound designs (>18,000 designs), crystallographic data (>500 ligand-bound X-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2,400 compounds) for this campaign were shared rapidly and openly, creating a rich open and IP-free knowledgebase for future anti-coronavirus drug discovery.

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