RESUMO
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. https://doi.org/finalDOI
RESUMO
The recent global COVID-19 pandemic has prompted a rapid response in terms of vaccine and drug development targeting the viral pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this work, we modelled a complete membrane-embedded SARS-CoV-2 spike (S) protein, the primary target of vaccine and therapeutics development, based on available structural data and known glycan content. We then used molecular dynamics (MD) simulations to study the system in the presence of benzene probes designed to enhance discovery of cryptic, potentially druggable pockets on the S protein surface. We uncovered a novel cryptic pocket with promising druggable properties located underneath the 617-628 loop, which was shown to be involved in the formation of S protein multimers on the viral surface. A marked multi-conformational behaviour of this loop in simulations was validated using hydrogen-deuterium exchange mass spectrometry (HDX-MS) experiments, supportive of opening and closing dynamics. Interestingly, the pocket is also the site of the D614G mutation, known to be important for SARS-CoV-2 fitness, and within close proximity to mutations in the novel SARS-CoV-2 strains B.1.1.7 and B.1.1.28, both of which are associated with increased transmissibility and severity of infection. The pocket was present in systems emulating both immature and mature glycosylation states, suggesting its druggability may not be dependent upon the stage of virus maturation. Overall, the predominantly hydrophobic nature of the cryptic pocket, its well conserved surface, and proximity to regions of functional relevance in viral assembly and fitness are all promising indicators of its potential for therapeutic targeting. Our method also successfully recapitulated hydrophobic pockets in the receptor binding domain and N-terminal domain associated with detergent or lipid binding in prior cryo-electron microscopy (cryo-EM) studies. Collectively, this work highlights the utility of the benzene mapping approach in uncovering potential druggable sites on the surface of SARS-CoV-2 targets.
RESUMO
We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spikes full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems. ACM Reference FormatLorenzo Casalino1{dagger}, Abigail Dommer1{dagger}, Zied Gaieb1{dagger}, Emilia P. Barros1, Terra Sztain1, Surl-Hee Ahn1, Anda Trifan2,3, Alexander Brace2, Anthony Bogetti4, Heng Ma2, Hyungro Lee5, Matteo Turilli5, Syma Khalid6, Lillian Chong4, Carlos Simmerling7, David J. Hardy3, Julio D. C. Maia3, James C. Phillips3, Thorsten Kurth8, Abraham Stern8, Lei Huang9, John McCalpin9, Mahidhar Tatineni10, Tom Gibbs8, John E. Stone3, Shantenu Jha5, Arvind Ramanathan2*, Rommie E. Amaro1*. 2020. AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics. In Supercomputing 20: International Conference for High Performance Computing, Networking, Storage, and Analysis. ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI
