#COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol

Creators: Dommer, Abigail; Casalino, Lorenzo; Kearns, Fiona; Rosenfeld, Mia; Wauer, Nicholas; Ahn, Surl-Hee; Russo, John; Oliveira, Sofia; Morris, Clare; Bogetti, Anthony; Trifan, Anda; Brace, Alexander; Sztain, Terra; Clyde, Austin; Ma, Heng; Chennubhotla, Chakra; Lee, Hyungro; Turilli, Matteo; Khalid, Syma; Tamayo-Mendoza, Teresa; Welborn, Matthew; Christensen, Anders; Smith, Daniel G. A.; Qiao, Zhuoran; Sirumalla, Sai Krishna; O'Connor, Michael; Manby, Frederick; Anandkumar, Anima; Hardy, David; Phillips, James; Stern, Abraham; Romero, Josh; Clark, David; Dorrell, Mitchell; Maiden, Tom; Huang, Lei; McCalpin, John; Woods, Christopher; Gray, Alan; Williams, Matt; Barker, Bryan; Rajapaksha, Harinda; Pitts, Richard; Gibbs, Tom; Stone, John; Zuckerman, Daniel; Mulholland, Adrian; Miller, Thomas, III; Jha, Shantenu; Ramanathan, Arvind; Chong, Lillian; Amaro, Rommie

Abstract

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.

Additional Information

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. We thank Prof. Kim Prather for inspiring and informative discussions about aerosols and for her commitment to convey the airborne nature of SARS-CoV-2. We thank D. Veesler for sharing the Delta spike NTD coordinates in advance of publication. We thank B. Messer, D. Maxwell, and the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory supported by the DOE under Contract DE-AC05-00OR22725. We thank the Texas Advanced Computing Center Frontera team, especially D. Stanzione and T. Cockerill, and for compute time made available through a Director's Discretionary Allocation (NSF OAC-1818253). We thank the Argonne Leadership Computing Facility supported by the DOE under DE-AC02-06CH11357. We thank the Pittsburgh Supercomputer Center for providing priority queues on Bridges-2 through the XSEDE allocation NSF TG-CHE060063. We thank N. Kern and J. Lee of the CHARMM-GUI support team for help converting topologies between NAMD and GROMACS. We thank J. Copperman, G. Simpson, D. Aristoff, and J. Leung for valuable discussions and support from NIH grant GM115805. NAMD and VMD are funded by NIH P41-GM104601. This work was supported by the NSF Center for Aerosol Impacts on Chemistry of the Environment (CAICE), National Science Foundation Center for Chemical Innovation (NSF CHE-1801971), as well as NIH GM132826, NSF RAPID MCB-2032054, an award from the RCSA Research Corp., a UC San Diego Moore's Cancer Center 2020 SARS-CoV-2 seed grant, to R.E.A. This work was also supported by Oracle Cloud credits and related resources provided by the Oracle for Research program. The authors have declared no competing interest.

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