TY - JOUR
T1 - Intelligent resolution
T2 - Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action
AU - Trifan, Anda
AU - Gorgun, Defne
AU - Salim, Michael
AU - Li, Zongyi
AU - Brace, Alexander
AU - Zvyagin, Maxim
AU - Ma, Heng
AU - Clyde, Austin
AU - Clark, David
AU - Hardy, David J.
AU - Burnley, Tom
AU - Huang, Lei
AU - McCalpin, John
AU - Emani, Murali
AU - Yoo, Hyenseung
AU - Yin, Junqi
AU - Tsaris, Aristeidis
AU - Subbiah, Vishal
AU - Raza, Tanveer
AU - Liu, Jessica
AU - Trebesch, Noah
AU - Wells, Geoffrey
AU - Mysore, Venkatesh
AU - Gibbs, Thomas
AU - Phillips, James
AU - Chennubhotla, S. Chakra
AU - Foster, Ian
AU - Stevens, Rick
AU - Anandkumar, Anima
AU - Vishwanath, Venkatram
AU - Stone, John E.
AU - Tajkhorshid, Emad
AU - Harris, Sarah A.
AU - Ramanathan, Arvind
N1 - Funding Information:
We thank the Argonne Leadership Computing Facility supported by the DOE under DE-AC02-06CH11357, the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory supported by the DOE under Contract DE-AC05-00OR22725, and the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory supported by the DOE under Contract No. DE-AC02-05CH11231. We also 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 MCB-20024). NAMD and VMD are funded by NIH P41-GM104601. The NAMD team thanks Intel and M. Brown for contributing the AVX-512 tile list kernels. Anda Trifan acknowledges support from a DOE CSGF (DE-FG02-97ER25308). This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the US DOE Office of Science and the National Nuclear Security Administration. Research was supported by the DOE through the National Virtual Biotechnology Laboratory, a consortium of DOE national laboratories focused on response to COVID-19, with funding from the Coronavirus CARES Act. This work used resources, services, and support from the COVID-19 HPC Consortium (https://covid19-hpc-consortium.org/), a private-public effort uniting government, industry, and academic leaders who are volunteering free compute time and resources in support of COVID-19 research. 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 the U.S. Department of Energy and National Institutes of Health.
Funding Information:
We thank the Argonne Leadership Computing Facility supported by the DOE under DE-AC02-06CH11357, the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory supported by the DOE under Contract DE-AC05-00OR22725, and the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory supported by the DOE under Contract No. DE-AC02-05CH11231. We also 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 MCB-20024). NAMD and VMD are funded by NIH P41-GM104601. The NAMD team thanks Intel and M. Brown for contributing the AVX-512 tile list kernels. Anda Trifan acknowledges support from a DOE CSGF (DE-FG02-97ER25308). This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the US DOE Office of Science and the National Nuclear Security Administration. Research was supported by the DOE through the National Virtual Biotechnology Laboratory, a consortium of DOE national laboratories focused on response to COVID-19, with funding from the Coronavirus CARES Act. This work used resources, services, and support from the COVID-19 HPC Consortium ( https://covid19-hpc-consortium.org/ ), a private-public effort uniting government, industry, and academic leaders who are volunteering free compute time and resources in support of COVID-19 research.
Publisher Copyright:
© The Author(s) 2022.
PY - 2022/11
Y1 - 2022/11
N2 - The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g. cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.
AB - The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g. cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.
KW - High performance computing
KW - Multi-resolution simulations
KW - artificial intelligence
KW - coronavirus 2019
KW - severe acute respiratory syndrome coronavirus-2
UR - http://www.scopus.com/inward/record.url?scp=85130366071&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85130366071&partnerID=8YFLogxK
U2 - 10.1177/10943420221113513
DO - 10.1177/10943420221113513
M3 - Article
AN - SCOPUS:85130366071
SN - 1094-3420
VL - 36
SP - 603
EP - 623
JO - International Journal of High Performance Computing Applications
JF - International Journal of High Performance Computing Applications
IS - 5-6
ER -