Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action
Restricted accessResearch articleFirst published online November, 2022
Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action
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.
AgostiniMLPruijssersAJChappellJD, et al. (2019) Small-molecule antiviral β-d-N 4-hydroxycytidine inhibits a proofreading-intact coronavirus with a high genetic barrier to resistance. Journal of Virology93(24): e01348–19.
2.
AlamNHigginsMK (2020) A spike with which to beat COVID-19?Nature Reviews Microbiology18(8): 414–414. DOI: 10.1038/s41579-020-0383-2
3.
AlQuraishiM (2019) End-to-end differentiable learning of protein structure. Cell Systems8(4): 292–301. DOI: 10.1016/j.cels.2019.03.006
4.
ArantesPRSahaAPalermoG (2020) Fighting COVID-19 using molecular dynamics simulations. ACS Central Science6(10): 1654–1656. DOI: 10.1021/acscentsci.0c01236
5.
BalasubramanianVTreikalisAWeidnerO, et al. (2016) Ensemble toolkit: scalable and flexible execution of ensembles of tasks. In: 2016 45th International Conference on Parallel Processing (ICPP). Los Alamitos, CA, USA: IEEE Computer Society, 16-19 August 2016, pp. 458–463. DOI: 10.1109/ICPP.2016.59
6.
BárcenaMBarnesCOBeckM, et al. (2021) Structural biology in the fight against COVID-19. Nature Structural & Molecular Biology28(1): 2–7. DOI: 10.1038/s41594-020-00544-8
7.
BarrantesFJ (2021) The contribution of biophysics and structural biology to current advances in COVID-19. Annual Review of Biophysics50(1): 493–523. DOI: 10.1146/annurev-biophys-102620-080956
8.
BarrosEPCasalinoLGaiebZ, et al. (2020) The flexibility of ACE2 in the context of SARS-CoV-2 infection. bioRxiv. DOI: 10.1101/2020.09.16.300459
9.
BhowmikDGaoSYoungMT, et al. (2018) Deep clustering of protein folding simulations. BMC Bioinformatics19(18): 484. DOI: 10.1186/s12859-018-2507-5
10.
BowermanSAmbarSRanaJB, et al. (2017) Determining atomistic SAXS models of tri-ubiquitin chains from bayesian analysis of accelerated molecular dynamics simulations. Journal of Chemical Theory and Computation13(6): 2418–2429. DOI: 10.1021/acs.jctc.7b00059
11.
BraceALeeHMaH, et al. (2021) Achieving 100X faster simulations of complex biological phenomena by coupling ML to HPC ensembles. arXiv Preprint arXiv:2104.04797.
12.
BratholmLAChristensenASHamelryckT, et al. (2015) Bayesian inference of protein structure from chemical shift data. PeerJ3: e861. DOI: 10.7717/peerj.861
13.
BreunigMMKriegelHPNgRT, et al. (2000) LOF: identifying density-based local outliers. In: ACM Sigmod Record. ACM, Vol. 29, pp. 93–104.
14.
CasalinoLDommerACGaiebZ, et al. (2021) AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics. The International Journal of High Performance Computing Applications35(5): 432–451. DOI: 10.1177/10943420211006452
15.
CavalliASalvatellaXDobsonCM, et al. (2007) Protein structure determination from NMR chemical shifts. Proceedings of the National Academy of Sciences104(23): 9615–9620. DOI: 10.1073/pnas.0610313104
16.
ChardRBabujiYLiZ, et al. (2020) funcX: a federated function serving fabric for science. In: Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing.23 June 2020, pp. 65–76. DOI: 10.1145/3369583.3392683
17.
ChenJMaloneBLlewellynE, et al. (2020) Structural basis for helicase-polymerase coupling in the SARS-CoV-2 replication-transcription complex. Cell182(6): 1560–1573. DOI: 10.1016/j.cell.2020.07.033
18.
CignoniPCallieriMCorsiniM, et al. (2008) Meshlab: an open-source mesh processing tool. In: Eurographics Italian Chapter Conference. Salerno, Italy, 2 July 2008, Vol. 2008, pp. 129–136.
19.
ClydeAGalanieKnellerDW, et al. (2021) High throughput virtual screening and validation of a SARS-CoV-2 main protease non-covalent inhibitor. bioRxiv. DOI: 10.1101/2021.03.27.437323
20.
CruzFAMartinassoM (2019) FirecREST: restful API on cray XC systems. arXiv.
21.
De SanchoDGaviraJAPérez-JiménezR (2020) Coarse-grained molecular simulations of the binding of the SARS-CoV-2 spike protein RBD to the ACE2 receptor. bioRxiv. DOI: 10.1101/2020.05.07.083212
22.
DongCSkalakR (1992) Leukocyte deformability: finite element modeling of large viscoelastic deformation. Journal of Theoretical Biology158(2): 173–193. DOI: 10.1016/S0022-5193(05)80716-7
23.
DurumericAEPVothGA (2019) Adversarial-residual-coarse-graining: Applying machine learning theory to systematic molecular coarse-graining. The Journal of Chemical Physics151(12): 124110. DOI: 10.1063/1.5097559
24.
EarnestTMWatanabeRStoneJE, et al. (2017) Challenges of integrating stochastic dynamics and cryo-electron tomograms in whole-cell simulations. The Journal of Physical Chemistry B121(15): 3871–3881. DOI: 10.1021/acs.jpcb.7b00672
25.
EastmanPSwailsJChoderaJD, et al. (2017) OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Computational Biology13(7): e1005659. DOI: 10.1371/journal.pcbi.1005659
26.
EndersBBardDSnavelyC, et al. (2020) Cross-facility science with the Superfacility Project at LBNL. In: 2020 IEEE/ACM 2nd Annual Workshop on Extreme-Scale Experiment-in-the-Loop Computing (XLOOP). Los Alamitos, CA, USA: IEEE Computer Society, 12 November 2020, pp. 1–7. DOI: 10.1109/XLOOP51963.2020.00006
27.
EngelBDSchafferMCuellarLK, et al. (2015) Native architecture of the Chlamydomonas chloroplast revealed by in situ cryo-electron tomography. eLife4: e04889. DOI: 10.7554/eLife.04889
28.
EssmannUPereraLBerkowitzML, et al. (1995) A smooth particle mesh Ewald method. Journal of Chemical Physics103: 8577–8593.
29.
EsterMKriegelHPSanderJ, et al. (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96). Portland, Oregon, 2–4 August 1996, pp. 226–231.
30.
FiorinGKleinMLHéninJ (2013) Using collective variables to drive molecular dynamics simulations. Molecular Physics111(22–23): 3345–3362. DOI: 10.1080/00268976.2013.813594
31.
GaoYYanLHuangY, et al. (2020) Structure of the RNA-dependent RNA polymerase from COVID-19 virus. Science368(6492): 779–782. DOI: 10.1126/science.abb7498
32.
GarayPGBarreraEEKleinF, et al. (2021) The SIRAH-CoV-2 initiative: a coarse-grained simulations’ dataset of the SARS-CoV-2 proteome. Frontiers in Medical Technology3: 644039. DOI: 10.3389/fmedt.2021.644039
33.
GeuzaineCRemacleJF (2009) Gmsh: A 3-D finite element mesh generator with built-in pre-and post-processing facilities. International Journal for Numerical Methods in Engineering79(11): 1309–1331.
34.
GrishaevALlinásM (2005) Protein structure elucidation from minimal NMR data: the CLOUDS approach. In: Nuclear Magnetic Resonance of Biological Macromolecules. Methods in Enzymology. Academic Press, Vol. 394, pp. 261–295. DOI: 10.1016/S0076-6879(05)94010-X
35.
HansonBSIidaSReadDJ, et al. (2021) Continuum mechanical parameterisation of cytoplasmic dynein from atomistic simulation. Methods185: 39–48.
36.
HoseiniPZhaoLShehuA (2021) Generative deep learning for macromolecular structure and dynamics. Current Opinion in Structural Biology67: 170–177.
37.
HumphreyWDalkeASchultenK (1996) VMD – visual molecular dynamics. Journal of Medical Genetics14(1): 33–38. DOI: 10.1016/0263-7855(96)00018-5
38.
HusicBECharronNELemmD, et al. (2020) Coarse graining molecular dynamics with graph neural networks. arXiv.
39.
JainAOngSPChenW, et al. (2015) FireWorks: a dynamic workflow system designed for high-throughput applications. Concurrency and Computation: Practice and Experience27(17): 5037–5059. DOI: 10.1002/cpe.3505
40.
JumperJEvansRPritzelA, et al. (2021) Highly accurate protein structure prediction with AlphaFold. Nature596(7873): 583–589. DOI: 10.1038/s41586-021-03819-2
41.
KaleLAcunBBakS, et al. (2019) The Charm++ Parallel Programming System. DOI: 10.5281/zenodo.3370873
42.
KaleL.V.BhateleA. (Eds.). (2013). Parallel Science and Engineering Applications: The Charm++ Approach (1st ed.). CRC Press. DOI: 10.1201/b16251
43.
KennawayRCoenE (2019) Volumetric finite-element modelling of biological growth. Open Biology9(5): 190057. DOI: 10.1098/rsob.190057
44.
KimHJungHS (2021) Cryo-EM as a powerful tool for drug discovery: recent structural based studies of SARS-CoV-2. Applied Microscopy51(1): 1–7. DOI: 10.1186/s42649-021-00062-x
45.
KühlbrandtW (2014) The resolution revolution. Science343(6178): 1443–1444. DOI: 10.1126/science.1251652
46.
LeeHTurilliMJhaS, et al. (2019) DeepDriveMD: deep-learning driven adaptive molecular simulations for protein folding. In: 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS). Denver, CO, USA: IEEE, 17-17 November 2019, pp. 12–19.
47.
LiZKovachkiNAzizzadenesheliK, et al. (2020) Neural operator: graph kernel network for partial differential equations. arXiv.
48.
LyumkisD (2019) Challenges and opportunities in cryo-EM single-particle analysis. Journal of Biological Chemistry294(13): 5181–5197. DOI: 10.1074/jbc.REV118.005602
49.
MahamidJPfefferSSchafferM, et al. (2016) Visualizing the molecular sociology at the HeLa cell nuclear periphery. Science351(6276): 969–972. DOI: 10.1126/science.aad8857
50.
MerkABartesaghiABanerjeeS, et al. (2016) Breaking Cryo-EM resolution barriers to facilitate drug discovery. Cell165(7): 1698–1707. DOI: 10.1016/j.cell.2016.05.040
51.
MerzkyATurilliMMaldonadoM, et al. (2018) Using pilot systems to execute many task workloads on supercomputers. arXiv.
52.
MinkyungBDiMaioFAnishchenkoI, et al. (2021) Accurate prediction of protein structures and interactions using a three-track neural network. Science373(6557): 871–876. DOI: 10.1126/science.abj8754
53.
MuratovENAmaroRAndradeCH, et al. (2021) A critical overview of computational approaches employed for COVID-19 drug discovery. Chemical Society Reviews50: 9121–9151. DOI: 10.1039/D0CS01065K
54.
NoéF (2020) Machine Learning for Molecular Dynamics on Long Timescales. Cham: Springer International Publishing, pp. 331–372. DOI: 10.1007/978-3-030-40245-7_16
55.
OliverRCReadDJHarlenOG, et al. (2013) A stochastic finite element model for the dynamics of globular macromolecules. Journal of Computational Physics239: 147–165. DOI: 10.1016/j.jcp.2012.12.027
56.
PadhiAKLipsa RathSTripathiT (2021) Accelerating COVID-19 research using molecular dynamics simulation. Journal of Palliative Care125(32): 9078–9091. DOI: 10.1021/acs.jpcb.1c04556
57.
PakAJVothGA (2018) Advances in coarse-grained modeling of macromolecular complexes. Current Opinion in Structural Biology52: 119–126. DOI: 10.1016/j.sbi.2018.11.005
58.
PanagiotopoulouOIriarte-DiazJWilshinS, et al. (2017) In vivo bone strain and finite element modeling of a rhesus macaque mandible during mastication. Zoology124: 13–29. DOI: 10.1016/j.zool.2017.08.010
59.
PerryJKApplebyTCBilelloJP, et al. (2021) An atomistic model of the coronavirus replication-transcription complex as a hexamer assembled around nsp15. bioRxiv. DOI: 10.1101/2021.06.08.447516
60.
PettersenEFGoddardTDHuangCC, et al. (2021) UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Science30(1): 70–82.
61.
PhillipsJZhengGKumarS, et al. (2002) NAMD: biomolecular simulation on thousands of processors. In: Proceedings of the IEEE/ACM SC2002 Conference, Technical Paper 277. Baltimore, MD: IEEE Press, 16 November 2002, pp. 1–18. DOI: 10.1109/SC.2002.10019
62.
PhillipsJCBraunRWangW, et al. (2005) Scalable molecular dynamics with NAMD. Journal of Computational Chemistry26: 1781–1802.
63.
PhillipsJCHardyDJMaiaJDC, et al. (2020) Scalable molecular dynamics on CPU and GPU architectures with NAMD. The Journal of Chemical Physics153: 044130. DOI: 10.1063/5.0014475
64.
PhillipsJCStoneJESchultenK (2008) Adapting a message-driven parallel application to GPU-accelerated clusters. In: SC ’08: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing. Piscataway, NJ, USA: IEEE Press, 15 November 2008, pp. 1–9.
65.
RamanathanASavolAJLangmeadCJ, et al. (2011) Discovering conformational sub-states relevant to protein function. PLoS ONE6(1): e15827. DOI: 10.1371/journal.pone.0015827
66.
RichardsonRAHansonBSReadDJ, et al. (2020) Exploring the dynamics of flagellar dynein within the axoneme with Fluctuating Finite Element Analysis. Quarterly Reviews of Biophysics53, E9. DOI: 10.1017/S0033583520000062
67.
RichardsonRHansonBReadDHarlenOHarrisS. (2020) Exploring the dynamics of flagellar dynein within the axoneme with Fluctuating Finite Element Analysis. Quarterly Reviews of Biophysics, 53, E9, DOI: 10.1017/S0033583520000062
68.
RomanoMRuggieroASquegliaF, et al. (2020) A structural view of SARS-CoV-2 RNA replication machinery: RNA synthesis, proofreading and final capping. Cells9(5): 1267. DOI: 10.3390/cells9051267
69.
SalimMUramTChildersJT, et al. (2021) Toward real-time analysis of experimental science workloads on geographically distributed supercomputers. arXiv.
70.
ScheresSHW (2012) A bayesian view on Cryo-EM structure determination. Journal of Molecular Biology415(2): 406–418. DOI: 10.1016/j.jmb.2011.11.010
71.
SchöberlJ (1997) NETGEN an advancing front 2D/3D-mesh generator based on abstract rules. Computing and Visualization in Science1(1): 41–52.
72.
SenerMLevySStoneJE, et al. (2021) Multiscale modeling and cinematic visualization of photosynthetic energy conversion processes from electronic to cell scales. Parallel Computing102: 102698.
73.
ShangJWanYLuoC, et al. (2020) Cell entry mechanisms of SARS-CoV-2. Proceedings of the National Academy of Sciences117(21): 11727–11734.
74.
SheahanTPSimsACZhouS, et al. (2020) An orally bioavailable broad-spectrum antiviral inhibits SARS-CoV-2 in human airway epithelial cell cultures and multiple coronaviruses in mice. Science Translational Medicine12: 541.
75.
SolernouAHansonBSRichardsonRA, et al.(2018) Fluctuating Finite Element Analysis (FFEA): a continuum mechanics software tool for mesoscale simulation of biomolecules. PLoS Computational Biology14(3): e1005897.
76.
SommerKIzzoRLShepardL, et al. (2017) Design optimization for accurate flow simulations in 3D printed vascular phantoms derived from computed tomography angiography. In: Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications. International Society for Optics and Photonics. Vol. 10138.
77.
StoneJEIsralewitzBSchultenK (2013a) Early experiences scaling VMD molecular visualization and analysis jobs on blue waters. In: Extreme Scaling Workshop (XSW). pp. 43–50. DOI: 10.1109/XSW.2013.10
78.
StoneJEMcGreevyRIsralewitzB, et al. (2014) GPU-accelerated analysis and visualization of large structures solved by molecular dynamics flexible fitting. Faraday Discussions169: 265–283. DOI: 10.1039/C4FD00005F
79.
StoneJESenerMVandivortKL, et al. (2016) Atomic detail visualization of photosynthetic membranes with GPU-accelerated ray tracing. Parallel Computing55: 17–27. DOI: 10.1016/j.parco.2015.10.015
80.
StoneJEVandivortKLSchultenK (2013b) GPU-accelerated molecular visualization on petascale supercomputing platforms. In: Proceedings of the 8th International Workshop on Ultrascale Visualization (UltraVis ’13). New York, NY, USA: ACM, 17 November 2013, pp. 1–8.
81.
SztainTAhnS-HBogettiAT, et al. (2021) A glycan gate controls opening of the SARS-CoV-2 spike protein. Nature Chemistry13: 963–968.
82.
TozziniV (2010) Multiscale modeling of proteins. Accounts of Chemical Research43(2): 220–230. DOI: 10.1021/ar9001476
83.
TrabucoLGVillaESchreinerE, et al. (2009) Molecular dynamics flexible fitting: a practical guide to combine cryo-electron microscopy and X-ray crystallography. Methods49(2): 174–180.
84.
TunyasuvunakoolKAdlerJWuZ, et al. (2021) Highly accurate protein structure prediction for the human proteome. Nature596(7873): 590–596. DOI: 10.1038/s41586-021-03828-1
85.
van der HeijdenTWReadDJHarlenOG, et al. (2020) Combined force-torque spectroscopy of proteins by means of multiscale molecular simulation. Biophysical Journal119(11): 2240–2250.
86.
VanommeslaegheKHatcherEAcharyaC, et al. (2010) CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. Journal of Computational Chemistry31(4): 671–690.
87.
VantJWLaheySLJanaK, et al. (2020) Flexible fitting of small molecules into electron microscopy maps using molecular dynamics simulations with neural network potentials. Journal of Chemical Information and Modeling60(5): 2591–2604.
88.
VillaELaskerK (2014) Finding the right fit: chiseling structures out of cryo-electron microscopy maps. Current Opinion in Structural Biology25: 118–125. DOI: 10.1016/j.sbi.2014.04.001
89.
WalpoleJPapinJAPeirceSM (2013) Multiscale computational models of complex biological systems. Annual Review of Biomedical Engineering15(1): 137–154. DOI: 10.1146/annurev-bioeng-071811-150104
90.
WangQWuJWangH, et al. (2020) Structural basis for RNA replication by the SARS-CoV-2 polymerase. Cell182(2): 417–428.
91.
WellsDBAbramkinaVAksimentievA (2007) Exploring transmembrane transport through α-hemolysin with grid-steered molecular dynamics. The Journal of Chemical Physics127(12): 09B619.
92.
WuAPengYHuangB, et al. (2020) Genome composition and divergence of the novel coronavirus (2019-nCoV) originating in China. Cell Host & Microbe27(3): 325–328.
93.
YanLGeJZhengL, et al. (2021a) Cryo-EM structure of an extended SARS-CoV-2 replication and transcription complex reveals an intermediate state in cap synthesis. Cell184(1): 184–193. DOI: 10.1016/j.cell.2020.11.016
94.
YanLYangYLiM, et al. (2021b) Coupling of N7-methyltransferase and 3-5 exoribonuclease with SARS-CoV-2 polymerase reveals mechanisms for capping and proofreading. Cell184(13): 3474–3485.
95.
YanLZhangYGeJ, et al. (2020) Architecture of a SARS-CoV-2 mini replication and transcription complex. Nature Communications11(1): 5874. DOI: 10.1038/s41467-020-19770-1
96.
YuAPakAJHeP, et al. (2021) A multiscale coarse-grained model of the SARS-CoV-2 virion. Biophysical Journal120(6): 1097–1104. DOI: 10.1016/j.bpj.2020.10.048
97.
ZhangQXiangRHuoS, et al. (2021) Molecular mechanism of interaction between SARS-CoV-2 and host cells and interventional therapy. Signal Transduction and Targeted Therapy6(1): 1–19.
98.
ZhouHX (2014) Theoretical frameworks for multiscale modeling and simulation. Current Opinion in Structural Biology25: 67–76. DOI: 10.1016/j.sbi.2014.01.004
99.
ZimmermanMIPorterJRWardMD, et al. (2020) SARS-CoV-2 simulations go exascale to capture spike opening and reveal cryptic pockets across the proteome. bioRxiv.
