Computational Biophysics in the Petascale Computing Era
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The document discusses advancements in computational biophysics using petascale computing, focusing on simulations of complex biological systems and their applications in drug targeting and disease understanding. It highlights contributions made by the Blue Waters supercomputer and various research projects on molecular dynamics, including studies on the hepatitis B virus and HIV-1 assembly processes. The content underscores the significance of high-resolution structures and large-scale simulations in bridging gaps across multiple biological and chemical scales.
Computational Biophysics in the Petascale Computing Era
- 1. Rommie E. Amaro . UC San Diego . Blue Waters Symposium . June 2018 Computational Biophysics in the Petascale Computing Era
- 2. Convergence of HPC, data science, & data enabling transformative advances at the intersection of observational and simulation sciences HP 735 12 CPUs protein 10k atoms 100s ps SGI Origin 128 CPUs Ranger 60k CPUs time ion channel 100k atoms 1 ns ribosome 2 mil atoms 100s ns Enveloped virus 160 mil+ atoms 1-100 μs ComputePower 1993 1997 2007 2013 Exascale 2002 LeMieux 3k CPUs ATPase 500k atoms 10s ns 360,000 cores + GPU acceleration Anton BW is a key component of the cyberinfrastructure ecosystem
- 4. Biophysics on Blue Waters Biophysics 15% Elementary Particle Physics 13% Stellar Astronomy and Astrophysics 13% Physics 8% Earth Sciences 7% Chemistry 6% Astronomical Sciences 5% Atmospheric Sciences 4% Molecular Biosciences 4% Engineering 4% Materials Research 3% Fluid, Particulate, and Hydraulic Systems 3% Extragalactic Astronomy and Cosmology 2% Magnetospheric Physics 2% Biological Sciences 2% Galactic Astronomy 1%Nuclear Physics 1% Climate Dynamics 1% Planetary Astronomy 1% Computer and Computation Research 1% Biochemistry and Molecular Structure and Function 1% Geophysics 0% Social, Behavioral, and Economic Sciences 0% Computer and Information Science and Engineering 0% Neuroscience Biology 0% Chemical, Thermal Systems 0% Other 5% Actual Usage by Discipline, 4/2013-5/2018
- 5. Biophysics on Blue Waters DI Data Intensive: uses large numbers of files, e.g. large disk space/bandwidth, or automated workflows/off-site transfers. GA GPU-Accelerated: written to run faster on XK nodes than on XE nodes TN Thousand Node: scales to at least 1,000 nodes for production science MI Memory Intensive: uses at least 50 percent of available memory on 1,000-node-runs BW Blue Waters: research only possible on Blue Waters MP Multi-Physics/multi-scale: job spans multiple length/timescales or physical/chemical processes ML Machine Learning: employs deep learning or other techniques, includes “big data” CI Communication-Intensive: requires high-bandwidth/low-latency interconnect for frequent, tightly coupled messaging IA Industry Applicable: Researcher has private sector collaborators or results directly applicable to industry
- 6. Computational biophysics bridges gaps across scales e.g., Can we understand the drug target in its real environment? Can we understand the molecular and chemical mechanisms underlying disease? Blue waters took us into and across these key “capability gaps”; Engaging all-atom & coarse grained MD to give unseen views into the inner workings of cells at the molecular level
- 7. / OL15 is 0.44 A from NMR average of Dickerson DNA dodecamer Tom Cheatham University of Utah Reproducibility and convergence (ensembles, replica exchange) – we can overcome the sampling problem for modest systems (tetraloops and other RNA motifs) Force field assessment, validation, and optimization
- 8. Inner gate opening shifts the SF conformational preference from conductive to constricted conformation Pinched ConductivePinched Conductive Allosteric Dynamics of C-type Inactivation in the KcsA Potassium Channel Benoît Roux Eduardo Perozo Jing Li
- 9. These waters are now visible in a new high-resolution structure of the open-inactivated KcsA (Perozo & Cuello, private communication)
- 10. Hepatitis B Viral capsid is a semi-permeable container with charge selectivity. Sodium (+) translocates five times faster than chloride (-) in HBV capsids. Analysis of 6 M solvent particles in parallel in Blue Waters. 0 Blue Waters XK nodes for 6 months
- 11. HBV flexibility reveals complex dynamics Hepatitis B virus capsid Hadden, JA., Perilla, JR. et al. eLife (2018)
- 12. CONFORMATIONAL DYNAMICS MOLECULAR DYNAMICS ELECTRON DYNAMICS BROWNIAN DYNAMICS Bottom-up biology of entire photosynthetic cell organelle ! JACS 138, 12077 (2016); eLife 5, e09541 (2016); JACS 139, 293 (2017)
- 13. LARGE-SCALE COARSE-GRAINED MOLECULAR SIMULATIONS OF THE VIRAL LIFECYCLE OF HIV-1 Gregory A. Voth University of Chicago The immature HIV-1 assembly process is catalyzed by scaffolds RNA co-localizes protein & promotes assembly Pak, Grime, … and Voth. PNAS 114:E10056 (2017) Membrane deformation co-localizes & promotes assembly
- 14. HIV-1 capsidHIV-1 virion 14 186 hexamers 12 pentamers
- 15. HIV capsid: 4.2 million atoms, 1300+ proteinsHIV capsid contains 186 hexamers, 12 pentamers
- 16. A204 E213 K203 I201 Perilla and Schulten . Nat. Commun. (2017), 15959 Acoustic analysis reveals allostery between distant sites Contact points control curvatureIons permeate through capsid
- 17. How membrane organization controls influenza infection: simulation & experiment Simulations yield a new molecular organizing principle for cholesterol that controls influenza binding and infection. Zawada…Kasson, 2016; Goronzy…Kasson, 2018.
- 18. 18 Routine dataset is 1.2 trillion pixels • 100,000’s of structures in a single dataset 3D Structural data to build visible virtual cells Serial Section EM Resin-embedded samples Serial Block EM Resin-embedded tissues
- 19. 19 Extending Molecular Structure to Cellular Environments
- 21. 21 Cell-centered, data-centric modeling framework
- 22. Alasdair Steven, NIH PyMolecule LipidWrapper CellPACK Fully Atomic Reconstructions Moving from single protein to whole virus Johnson et al, Nature Methods (2014),; Durrant & Amaro, PLOS Comp Bio (2014). • Improved sense of the physical arrangement of biological entities in complex biological milieu • Enables simultaneous study of multiple components • Mesoscale molecular models as a platform for other simulation approaches (e.g., Brownian dynamics, Mcell, lattice boltzmann MD) … leads us to new avenues of investigation, not possible on the single protein scale
- 23. 114,688 processors (16,384 Blue Waters nodes) 25.6 steps / s or ~4.5 ns/day 2013 2014 2015 2016 120 ns total, 12TB Durrant and Amaro, unpublished (2017) Equilibration, membrane System & tool building, waiting ProdProp. Rev. 160 million atoms, with explicit solvent
- 24. Petascale MD of Fully Enveloped Flu Virus • Largest biological system ever simulated (~165 million atoms) • 4.5 ns/day using 114,688 CPUs • 158 ns total simulation • Saving every 20 ps è ~25 TB of data • Collaboration with TCBG P41
- 25. Active Inactive Markov state models define metastable states and transitions between states Allows one to extract long timescale dynamics from many short timescale simulations Cell-scale Markov state models of protein dynamics Swope, Pande, Schutte, Noe…
- 26. MSMs characterize loop dynamics & druggable pockets Virion has 30 NAs, 236 HAs Enough sampling to make a Markov state model (MSM) of NA loop dynamics 2-state Macrostate model open/closed MFPT for the 150-loop: • open to closed 52.9ns • closed to open 198.4 ns
- 27. Molecular simulation at the mesoscale 100 nm 1000 nm (1 um) 10 nm Biophysics is ready for exascale!
- 29. Dedicated to Klaus Schulten, 1947-2016 “Why? … because we can.”