Dommer, Abigail and Casalino, Lorenzo and Kearns, Fiona and Rosenfeld, Mia and Wauer, Nicholas and Ahn, Surl-Hee and Russo, John and Oliveira, Sofia and Morris, Clare and Bogetti, Anthony and Trifan, Anda and Brace, Alexander and Sztain, Terra and Clyde, Austin and Ma, Heng and Chennubhotla, Chakra and Lee, Hyungro and Turilli, Matteo and Khalid, Syma and Tamayo-Mendoza, Teresa and Welborn, Matthew and Christensen, Anders S. and Smith, Daniel G. A. and Qiao, Zhuoran and Sirumalla, Sai K. and O'Connor, Michael and Manby, Frederick and Anandkumar, Anima and Hardy, David and Phillips, James and Stern, Abraham and Romero, Josh and Clark, David and Dorrell, Mitchell and Maiden, Tom and Huang, Lei and McCalpin, John and Woods, Christopher and Gray, Alan and Williams, Matt and Barker, Bryan and Rajapaksha, Harinda and Pitts, Richard and Gibbs, Tom and Stone, John and Zuckerman, Daniel M. and Mulholland, Adrian J. and Miller, Thomas F., III and Jha, Shantenu and Ramanathan, Arvind and Chong, Lillian and Amaro, Rommie E. (2023) #COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol. International Journal of High Performance Computing Applications, 37 (1). pp. 28-44. ISSN 1094-3420. PMCID PMC9527558. doi:10.1177/10943420221128233. https://resolver.caltech.edu/CaltechAUTHORS:20221017-15547800.39
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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 obscure 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.
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| Additional Information: | © The Author(s) 2022. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). 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. AJM and ASFO receive funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (PREDACTED Advanced Grant, Grant agreement No.: 101021207). The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Science Foundation (CHE- 1801971); National Science Foundation (MCB- 2032054); National Science Foundation (OAC-1818253); National Science Foundation (TG-CHE060063); U.S. Department of Energy (DE-AC02-06CH11357); U.S. Department of Energy (DE-AC05- 00OR22725); National Institutes of Health (P41-GM104601); National Institutes of Health (R01-GM132826). The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. | ||||||||||||||||||||
| Group: | COVID-19 | ||||||||||||||||||||
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| Issue or Number: | 1 | ||||||||||||||||||||
| PubMed Central ID: | PMC9527558 | ||||||||||||||||||||
| DOI: | 10.1177/10943420221128233 | ||||||||||||||||||||
| Record Number: | CaltechAUTHORS:20221017-15547800.39 | ||||||||||||||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20221017-15547800.39 | ||||||||||||||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||||||||||
| ID Code: | 117475 | ||||||||||||||||||||
| Collection: | CaltechAUTHORS | ||||||||||||||||||||
| Deposited By: | Research Services Depository | ||||||||||||||||||||
| Deposited On: | 18 Oct 2022 21:55 | ||||||||||||||||||||
| Last Modified: | 23 May 2023 16:58 |
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