Abstract
The emergence and rapid worldwide spread of the novel coronavirus disease, COVID-19, has prompted concerted efforts to find successful treatments. The causative virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), uses its spike (S) protein to gain entry into host cells. Therefore, the S protein presents a viable target to develop a directed therapy. Here, we deployed an integrated artificial intelligence with all-atom molecular dynamics simulation approach to provide new details of the S protein structure. Based on a comprehensive structural analysis of S proteins from SARS-CoV-2 and previous human coronaviruses, we found that the protomer state of S proteins is structurally flexible. Without the presence of a stabilizing beta sheet from another protomer chain, two regions in the S2 domain and the hinge connecting the S1 and S2 subunits lose their secondary structures. Interestingly, the region in the S2 domain was previously identified as an immunodominant site in the SARS-CoV-1 S protein. We anticipate that the molecular details elucidated here will assist in effective therapeutic development for COVID-19.
Original language | English |
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Title of host publication | Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 |
Editors | Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4333-4341 |
Number of pages | 9 |
ISBN (Electronic) | 9781665439022 |
DOIs | |
State | Published - 2021 |
Event | 2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States Duration: Dec 15 2021 → Dec 18 2021 |
Publication series
Name | Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 |
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Conference
Conference | 2021 IEEE International Conference on Big Data, Big Data 2021 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 12/15/21 → 12/18/21 |
Funding
Notice: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-accessplan). The research was supported by the U.S. Department of Energy, Office of Science, through the Office of Advanced Scientific Computing Research (ASCR), under contract number DE-AC05-00OR22725; the Exascale Computing Project (ECP) (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration; and in part by the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by the U.S. Department of Energy (DOE) and the National Cancer Institute (NCI) of the National Institutes of Health. DB would like to thank ASCR and ECP for assistance in the implementation of deep learning, data processing, and data analysis algorithms and would like to thank members of the DOE National Virtual Biotechnology Laboratory (NVBL) projects for insightful discussions on the design of and results This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract number DEAC05-00OR22725. This work was performed at the Oak Ridge Leadership Computing Facility (OLCF) of the Oak Ridge National Laboratory (ORNL), which is funded by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05- 00OR22725 and used the Extreme Science and Engineering Discovery Environment (XSEDE) [37] COVID-19HPC Consortium at the IBM AC922 Summit supercomputer of the OLCF at ORNL through allocation TG-ASC200020. It was performed under the auspices of the U.S. Department of Energy by Argonne National Laboratory under Contract DE-AC02-06-CH11357, Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, Los Alamos National Laboratory under Contract DE-AC5206NA25396, Oak Ridge National Laboratory under Contract DE-AC05-00OR22725, and Frederick National Laboratory for Cancer Research under Contract HHSN261200800001E.
Keywords
- Deep learning
- MD simulation
- SARS-CoV-2
- Spike protein