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The Computational Microscope Images Biomolecular Machines and Nanodevices - Klaus Schulten

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Copyright Theoretical and Computational Biophysics Group, www.ks.uiuc.edu

Copyright Theoretical and Computational Biophysics Group, www.ks.uiuc.edu

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  • 1. The Computational Microscope Images Biomolecular Machines and Nanodevices shadow is Accuracy • Speed-up • Unprecedented Scale crystal structure protein folding: villin headpiece 3 months on 329 CPUs Klaus Schulten Department of Physics and Theoretical and Computational Biophysics Group University of Illinois at Urbana-Champaign Lecture at CPLC Summer School, Urbana, July, 2012Wednesday, August 1, 2012
  • 2. Our Microscope is Made of...Chemistry NAMD Software 100 ns/day 10 Virus 1 128 256 512 1024 2048 4096 8192 16384 32768 51,000 registered users coresPhysicsMath ..and Supercomputers (repeat one billion times = microsecond)Wednesday, August 1, 2012
  • 3. 3 Outside researchers choose NAMD and succeed Corringer, et al., Nature, 2011 2100 external citations since 2007 Voth, et al., PNAS, 2010 180K-atom 30 ns study of anesthetic binding to bacterial ligand-gated ion channel provided “complementary interpretations…that could not have been deduced from the static structure alone.” Bound Propofol Anesthetic 500K-atom 500 ns investigation of effect of actin depolymerization factor/cofilin on mechanical properties and conformational dynamics of actin filament. Bare actin Cofilactin Recent NAMD Simulations in Nature • M. Koeksal, et al., Taxadiene synthase structure and evolution of modular architecture in terpene biosynthesis. (2011) • C.-C. Su, et al., Crystal structure of the CusBA heavy-metal efflux complex of Escherichia coli. (2011) • D. Slade, et al., The structure and catalytic mechanism of a poly(ADP-ribose) glycohydrolase. (2011) • F. Rose, et al., Mechanism of copper(II)-induced misfolding of Parkinson’s disease protein. (2011) • L. G. Cuello, et al., Structural basis for the coupling between activation and inactivation gates in K(+) channels. (2010) • S. Dang, et al.,, Structure of a fucose transporter in an outward-open conformation. (2010) • F. Long, et al., Crystal structures of the CusA efflux pump suggest methionine-mediated metal transport. (2010) • R. H. P. Law, et al., The structural basis for membrane binding and pore formation by lymphocyte perforin. (2010) • P. Dalhaimer and T. D. Pollard, Molecular Dynamics Simulations of Arp2/3 Complex Activation. (2010) • J. A. Tainer, et al., Recognition of the Ring-Opened State of Proliferating Cell Nuclear Antigen by Replication Factor C Promotes Eukaryotic Clamp-Loading. (2010) • D. Krepkiy, et al.,, Structure and hydration of membranes embedded with voltage-sensing domains. (2009) • N. Yeung, et al.,, Rational design of a structural and functional nitric oxide reductase. (2009) • Z. Xia, et al., Recognition Mechanism of siRNA by Viral p19 Suppressor ofBioinformatics BTRC for Macromolecular Modeling and RNA Silencing: A Molecular Dynamics Study. (2009) UIUC Beckman Institute, http://www.ks.uiuc.edu/Wednesday, August 1, 2012
  • 4. Successful folding of largest fast-folding protein: λ-repressor / Collaboration Gruebele-Schulten System: • 5-helix bundle protein • Largest fast-folding protein • Experimental folding time < 20 µs Recent progress: • Successful folding in < 5 µs using enhanced sampling • 100-µs folding trajectory using both NAMD and Anton 0 15 RMSD (Å) Liu et al, J. Chem. Phys. Lett. (2012) Alpha helix Pi-helix 3-10 helix Turn None (coil) BTRC for Macromolecular Modeling and Bioinformatics Beckman Institute, UIUC Lamda repressor folding, 24.1 µs. Per-residue secondary structure and RMSD. http://www.ks.uiuc.edu/Wednesday, August 1, 2012
  • 5. Computational Microscope at Work: Inspecting the Mechanical Strength of a Blood Clot A Blood Clot 20ns SMD Simulation of fibrinogen, 1.06 million atoms, 1.2 ns/day Red blood cells within a with pencil decomposition, 15 days on PSC XT3 Cray (1024 network of fibrin fibers, processors) composed of polyme- B. Lim, E. Lee, M. Sotomayor, and K. Schulten. Molecular basis of fibrin clot rized fibrinogen mole- elasticity. Structure, 16:449-459, 2008. cules. 12Wednesday, August 1, 2012
  • 6. Computational Microscope at Work: Inspecting the Mechanical Strength of a Blood Clot A Blood Clot 20ns SMD Simulation of fibrinogen, 1.06 million atoms, 1.2 ns/day Red blood cells within a with pencil decomposition, 15 days on PSC XT3 Cray (1024 network of fibrin fibers, processors) composed of polyme- B. Lim, E. Lee, M. Sotomayor, and K. Schulten. Molecular basis of fibrin clot rized fibrinogen mole- elasticity. Structure, 16:449-459, 2008. cules. 12Wednesday, August 1, 2012
  • 7. (d) Design of Tyrosine Kinase Sensor •Peptides are attached to a gold surface. The end of oligopeptide contains rhodamine. •Peptides are exposed to ATP and appropriate kinase. •Electric field is applied; change in conformation depends on phosphorylation state. •Distance from rhodamine determines magnitude of fluorescence signal. initial sequence: {1 GLU} {2 GLY} {3 ILE} {4 TYR} {5 GLY} {6 VAL} {7 LEU} {8 PHE} {9 LYS} {10 LYS} {11 LYS} {12 CYS} Rhodamine (R6g) Tyrosine residue interacts with kinases Experiment: Logan Liu andal image of sensor: A (a) by phosphorylation (b) (c) Fluorescence image (d) Chen, Nano Lab, U. Illinois Yiyer of 5 nm titanium showing peptide probes were successfullyo activate the sensor (b) immobilized on the (a) nanochip surface (e) than smooth surface. 300 nmensor. Peptide probes kinase sensor. Peptides are pattern are created by a by experiments and simulations. A thin layerimage nmsensor: A Figure 2: Proposed kinase sensor studied 5×5 square array SEPERISE method (see text). (a) Optical of 5 of titanium representation. Blue of 80 nm gold is depositedwithin theofsurface, so they structures tothan smooth surface. followed by a layer surface. The nanostructures trap incident light on top (a) the nanocone look darker (b) activate the sensor Fluorescence image showing peptide probes (c) (d)ectively. oligopeptide with 12Nanoscopic structures of the activated areas of the kinase sensor. Peptide probesAu surface, (b) MISSING FIGURE. (c) AA, were successfully immobilized on the are immobilized on the gold covered surface of nanocones. (c) Atomic model of kinase sensor. Peptides are nanochip surface bound via sulfide bond colored in green, metallic surface in yellow. A single peptide is highlighted in licorice representation. Blue 300 nm (c) (d) and red arrows point to the rhodamine cap and phosphorylated tyrosine residue, respectively. Figure 2: Proposed kinase sensor studied by experiments and simulations. (a) Optical image of sensor: A 5×5 square array pattern are created by a SEPERISE method (see text). A thin layer of 5 nm titanium followed by a layer of 80 nm gold is deposited on top of the nanocone structures to activate the sensor kin. surface. The nanostructures trap incident light within the surface, so they look darker than smooth surface. (b) MISSING FIGURE. (c) Nanoscopic structures of the activated areas of the kinase sensor. Peptide probes are immobilized on the gold covered surface of nanocones. (c) Atomic model of kinase sensor. Peptides are V V colored in green, metallic surface in yellow. A single peptide is highlighted in licorice representation. Blue and red arrows point to the rhodamine cap and phosphorylated tyrosine residue, respectively. Spectrometer Wednesday, August 1, 2012
  • 8. MD Simulations Revealed Problems in Designinitial sequence: {1 GLU} {2 GLY} {3 ILE} {4 TYR} {5 GLY} {6 VAL} {7 LEU} {8 PHE} {9 LYS} {10 LYS} {11 LYS} {12 CYS} Initial sequence did not work due to surface adhesion and rhodamine aggregation •Improved sequence. Avoid residues that strongly bind to gold surface. •Rhodamines molecules tend to aggregate. Aggregation affects peptide bending. •MD systems include gold surface, water, ions and 5x5 peptide grid, ~ 100,000 atoms. •Different sequences tested under positive and negative volt biases.Wednesday, August 1, 2012
  • 9. Current Design Improved New sequence: rhodamin removed and measurement replaced by Raman spectroscopy Rhodamine removed, 40 Fluorescence signal discarded 35 MD Simulations 30 dist Tyr-Gold / A show that +60V 25 phosphorylated -60V tyrosine bends the 20 0V peptide depending on 15 voltage polarity 10 5 0 0 1 2 3 4 5 6 time / ns 800 +1.2V Experiments show 600 -1.2V intensity / AU that peptide bends 0V towards the surface at 400 positive voltages, increasing the intensity of Raman 200 Phosphorylated tyrosine shifts. 0 Peptide bending is now measured with Raman Spectroscopy. 700 1100 1500 The detection relies on careful comparison of peak points Raman shift / cm-1Wednesday, August 1, 2012
  • 10. Unphosphorylated Phosphorylated (a) 4 4 rho-EGIYGVLFKKKC-gold (i) (a) rho-EGIYGVLFKKKC-gold (i) dist Y-gold / nm EGIYGVLFKKKC-AU EGI(pY)GVLFKKKC-AU 3 Simulation dist Y-gold / nm 3 2 40 40 2 35 35 1 1 30 30 dist Tyr-Gold / A 0 dist Tyr-Gold / A 25 0 1 2 3 4 +60V 5 6 25 1 2 3 time / 4 ns -60V 5 6 20 20 time / ns 0V 15 15 4 +60V 4 (b) rho-EGI(pY)GVLFKKKC-gold (ii) 10 -60V 10 (b) rho-EGI(pY)GVLFKKKC-gold (ii) dist Y-gold / nm 3 dist Y-gold / nm 5 0V 5 3 (i) 2 (i) 0 0 0 1 2 3 4 5 6 0 12 2 3 4 5 6 1 time / ns time / ns (ii) 1 (ii) 0 1 2 3 4 5 6 0 1 2 time 3 / ns 4 5 6 time / ns Figure 4: Peptide phosphorylation revealed by molecular dynamics simulations. Panels Figure 4: Peptide phosphorylation revealed by surface for dynamicsvoltage polarities. Er the distance from the tyrosine residue to the gold molecular different simulations. Panels thestandard deviation. (a) Non-phosphorylated peptide sensor under +60 V (blue), -60 V ( ± distance from the tyrosine residue to the gold surface for different voltage polarities. Er ± standard deviation. (a) Non-phosphorylated peptide sensor under +60 (red) and -60 V ( biases. (b) Phosphorylated peptide sensor under +60 V (blue), -60 V V (blue), 0 volta biases. (b) Phosphorylated peptide peptides after+60 Vfor +60 V (i) and -60 V 0 volta (i) and (ii) show snapshots of two sensor under 5 ns (blue), -60 V (red) and (ii) vo (i) and (ii) show snapshots gray tubes. Rhodamine and for +60 V (i) and -60 V (ii) vo peptides are represented as of two peptides after 5 ns tyrosine residues are shown in bl peptides are represented as gray tubes. Rhodamine and tyrosine residues are shown in bl respectively. respectively.Wednesday, August 1, 2012
  • 11. Unphosphorylated Phosphorylated (a) 4 4 rho-EGIYGVLFKKKC-gold (i) (a) rho-EGIYGVLFKKKC-gold (i) dist Y-gold / nm EGIYGVLFKKKC-AU EGI(pY)GVLFKKKC-AU 3 Simulation dist Y-gold / nm 3 2 40 40 2 35 35 1 1 30 30 dist Tyr-Gold / A 0 dist Tyr-Gold / A 25 0 1 2 3 4 +60V 5 6 25 1 2 3 time / 4 ns -60V 5 6 20 20 time / ns 0V 15 15 4 +60V 4 (b) rho-EGI(pY)GVLFKKKC-gold (ii) 10 -60V 10 (b) rho-EGI(pY)GVLFKKKC-gold (ii) dist Y-gold / nm 3 dist Y-gold / nm 5 0V 5 3 (i) 2 (i) 0 0 0 1 2 3 4 5 6 0 12 2 3 4 5 6 1 time / ns time / ns (ii) 1 (ii) 0 1 2 3 4 5 6 Experiment 0 1 2 time 3 / ns 4 5 6 time / ns 800 Figure 4: Peptide phosphorylation revealed by molecular dynamics simulations. Panels 800 +1.2V Figure 4: Peptide phosphorylation revealed by surface for dynamicsvoltage polarities. Er the distance from the tyrosine residue to the gold molecular different simulations. Panels 600 -1.2V thestandard deviation. (a)+1.2V ± distance from the tyrosine residue to the gold surface for different +60 V (blue), -60 V ( Non-phosphorylated peptide sensor under voltage polarities. Er ± standard deviation. (a) -1.2V 600 biases. (b) Phosphorylated peptide sensor under +60 sensor under +60 (red) and -60 V ( Non-phosphorylated peptide V (blue), -60 V V (blue), 0 volta intensity / AU intensity / AU 0V biases. (b) Phosphorylated0V two peptides after+60 Vfor +60 V (i) and -60 V 0 volta (i) and (ii) show snapshots peptide sensor under 5 ns (blue), -60 V (red) and (ii) vo of 400 (i) and (ii) show snapshots gray tubes. Rhodamine and for +60 V (i) and -60 V (ii) vo peptides are represented as of two peptides after 5 ns tyrosine residues are shown in bl 400 peptides are represented as gray tubes. Rhodamine and tyrosine residues are shown in bl respectively. 200 respectively. 200 0 0 700 1100 1500 700 1100 1500 Raman shift / cm-1 Raman shift / cm-1Wednesday, August 1, 2012
  • 12. dist Simulation-Optimized 1 Sequence 0 1 2 3 4 5 time / ns 4 4 (a) EGIYGVLAAAAC-gold (b)dist Y-gold / nm dist Y-gold / nm 3 3 2 2 EGI(pY)GVLAAAAC-gold 1 1 0 0 1 2 3 4 5 1 2 3 4 5 time / ns time / ns A new sequence is proposed from molecular dynamics simulations. There are dynamics simulations Figure 5: Optimized sequence. A new sequence is proposed from molecular no rhodamine caps, avoiding aggregation. Unnecessary lysineUnnecessary lysine residues (a), whileby alanine residu 4 is no rhodamine caps; avoiding aggregation. residueselectric field changed the phosphoryla non-phosphorylated peptide sensor is still insensitive to an are changed to alanine residues. The resulting non-phosphorylated peptide sensor is still distinctive raman signat (b) is responsive to an external electric field (b); therefore it should produce insensitive to an electric field, while the and 0 voltage Error bars represent is standard deviation. to an red an green r opposite voltage polarities. phosphorylated one ± responsive Color blue,t Y-gold / nm +60 V, -60 V biases. 3 external electric field; therefore the sensor should produce distinctive Raman signatures for opposite voltage polarities. Error bars represent standard deviation. Colors blue, red, and green represent +60 V, -60 V and 0 voltage biases. 2 EGI(pY)GVLAAAAC-goldWednesday, August 1, 2012 16
  • 13. Imaging Cellular MachinesWednesday, August 1, 2012
  • 14. 14 Larger machines enable larger simulations BTRC for Macromolecular Modeling and Bioinformatics Beckman Institute, UIUC http://www.ks.uiuc.edu/Wednesday, August 1, 2012
  • 15. Petascale Gateway to the Nation’s Top Computers Petascale computers (e.g., Blue Waters) will create ~100x larger datasets. The BTRC Petascale Gateway provides the necessary storage and analysis facilities all located in the BTRC area. 1-10 Gigabit Network External Visitor, Resources, 90% Researcher & of our Computer Developer Power Workstations Beckman Institute, UIUC RecentMacromolecular Modeling and Bioinformatics BTRC for http://www.ks.uiuc.edu/Wednesday, August 1, 2012
  • 16. How NAMD Runs Efficiently on Large Parallel Computers Before Load Balancing: Much Idle CPU Time! 23,000 atoms on BlueGene/P 4K processor cores WHITE = 32 ns/day IDLE CPU TIME! 2.75 ms/step Percentage CPU Utilization Time BTRC for Macromolecular Modeling and Bioinformatics Beckman Institute, UIUC http://www.ks.uiuc.edu/Wednesday, August 1, 2012
  • 17. After Load Balancing: Efficient Computing! 23,000 atoms on BlueGene/P 4K processor cores IDLE TIME REDUCED ~ 40% 63 ns/day 1.38 ms/step Percentage CPU Utilization Time BTRC for Macromolecular Modeling and Bioinformatics Beckman Institute, UIUC http://www.ks.uiuc.edu/Wednesday, August 1, 2012
  • 18. 18 Computational Microscope on New Supercomputers 100M-atom performance on Jaguar and Blue Waters Jaguar BlueWaters Cray Jaguar XT5 (ORNL) 4.000 224,000 coresns/day 3.000 2.000 BlueWaters XE6 (UIUC) 1.000 380,000 cores, 3000 GPUs 0 0 75000 150000 225000 300000 Number of Cores 1.50 CPU 12 cores CPU 12 cores + 1 GPU 1.13 CPU 12 cores + 2 GPU Tsubame CPU 12 cores + 3 GPU 0.75 ns/day Tokyo Institute of Tech. 4224 GPUs 0.38 0 50,000 registered users 64 128 256 512 700 BTRC for Macromolecular Modeling and Bioinformatics Beckman Institute, UIUC http://www.ks.uiuc.edu/ Number of Nodes Wednesday, August 1, 2012
  • 19. 19 1M Atom VirusGPU and CPU performanceGPU 1M-atom stmv on TitanDev GPU CPU 5 Single STMV PME every 4 steps ns/day 1 0.2 1 2 4 8 16 32 64 128 256 512 number of nodes BTRC for Macromolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUCWednesday, August 1, 2012
  • 20. 20 100M Atoms on TitanDev BTRC for Macromolecular Modeling and Bioinformatics Beckman Institute, UIUC http://www.ks.uiuc.edu/Wednesday, August 1, 2012
  • 21. Advanced Computer Facilities and the Computational Microscope Illinois Petascale Computing Facility BTRC for Macromolecular Modeling and Bioinformatics Beckman Institute, UIUC http://www.ks.uiuc.edu/Wednesday, August 1, 2012
  • 22. 2 Molecular Dynamics Flexible Fitting (MDFF) Method Electron microscopy X-ray crystallography MDFF APS at Argonne FEI microscope Acetyl – CoA Synthase L. Trabuco, E. Villa, K. Mitra, J. Frank, and K. Schulten. Flexible fitting of atomic structures into electron microscopy maps using molecular dynamics. Structure, 16:673-683, 2008.Wednesday, August 1, 2012
  • 23. 4 MDFF is young (2008), yet already successfulOver 30 reports of MDFF applications:• By Group Researchers: Villa et al. PNAS (2009): EF-Tu ribosome complex Seidelt et al. Science (2009): TnaC ribosome translation stalling Becker et al. Science (2009): Sec61 ribosome complex Frauenfeld et al. Nat. Struct. Mol. Biol. (2011): SecYE ribosome complex  Agirrezabala et al. PNAS (2012): Ribosome translocation intermediates• By Outside Researchers: Lorenz et al. PNAS (2010): actin-myosin interface Bhushan et al. Nat. Struct. Mol. Biol. (2010): α-helical nascent chains Armache et al. PNAS (2011): Eukaryotic ribosomal proteins Armache et al. PNAS (2011): Translating eukaryotic ribosome Guo et al. PNAS (2011): RsgA GTPase on ribosomal subunit Becker et al. Nat. Struct. Mol. Biol. (2011) Dom34–Hbs1 stalled ribosome complex Wollmann et al. Nature (2011): Mot1–TBP complex Strunk et al. Science (2011): Ribosome assembly factors Lasker et al. PNAS (2012): Proteasome Becker et al. Nature (2012): Ribosome recycling complex MDFF derived ribosome structure Villa et al. PNAS 2009 Tightly integrated into NAMD and VMD • Capable of quickly fitting very large structures • Adaptable to a wide range of applicationsWednesday, August 1, 2012
  • 24. Computation and Experiment in Virology 10 Collaboration with A. Gronenborn et al, U. Pittsburgh Molecular Dynamics Flexible Fitting of Tubular HIV Capsid: In the tubular geometry the capsid contains only hexameric subunits. EM fitted density structure view of hexameric subunit during MDFF final cross-correlation 0.96 Relaxation of capsid segment requires 13 million atom MD simulation on Blue Waters, 50 ns ns doneWednesday, August 1, 2012
  • 25. ed within a cone-shaped capsid assembled from ≈1500 copies of the viral capsid protein (CA) From Cylinder to HIV Capsidous attempts have been made to model the HIV capsid as a fullerene cone composed of ≈250examers and 12 CA-pentamers, either by self-assembling of coarse-grained models [28] or Replace hexameric units by pentameric units o-atomic models [29]; however, these models lack the atomic-level interactions that hold thed together. Collaboration with A. Gronenborn et al, U. Pittsburgh hexamers pentamer whole capsid made ofe 3: million atoms of the hexameric form of thehexamers found in cylindrical assemblies10 (A) All-atom model unit CA protein as and 12 pentamersV capsids in vitro. The model accurately captures the curvature of the tube. (B) CA pentame 60 million atoms nded by five CA hexamers. (C) Schlegel diagram for a fullerene end apex connected to a cylindricaof hexagonally linked carbon atoms. Wednesday, August 1, 2012
  • 26. Recent MDFF application: Intra-ring cooperation in group II chaperoninsCollaboration with Fei Sun (Chinese Academy of Sciences) Zhang et al. Submitted. Mar 2012 Symmetry-restrained MDFF applied to 12 EM maps revealed role of electrostatic interaction in chaperonin hetero-oligomer formation EM Maps of Various Chaperonin Conformations 5Wednesday, August 1, 2012
  • 27. Our Grand Ribosome CollaborationJoachim Frank Roland Beckmann Taekjip Ha Kurt Fredrick Ruben Gonzalez(Columbia U.) (U. Munich) (UIUC) (Ohio State U.) (Columbia U.)Cryo-EM Cryo-EM Single-molecule Mutagenesis Single-molecule FRET experiments FRETStructure (2008), Science (2009) x2, J. Mol. Bio. (2010), New collaborator New collaboratorPNAS (2009), Nature Struct. 1 submittedProteins (2011), Mol. Bio (2011),EMBO J. (2011), 1 submittedPNAS (2012)Wednesday, August 1, 2012
  • 28. Science 3: How Proteins AreMade from Genetic BlueprintLow-resolution data High-resolution structure Close-up of nascent protein Frauenfeld, Gumbart et al. Nat. Struct. Mol. Bio. 18, 614-621, 2011.Wednesday, August 1, 2012
  • 29. Molecular Dynamics Flexible Fitting (MDFF) Study of the Localization of Nascent Proteins 9 Ribosome expressing membrane protein James Gumbart2.7 million atom simulation Ribosome-SecY-complexFrauenfeld, Gumbart et al. Nat. Struct. Mol. Bio. 18, 614-621, 2011.Wednesday, August 1, 2012
  • 30. Molecular Dynamics Flexible Fitting (MDFF) Study of the Localization of Nascent Proteins 9 Ribosome expressing Ribosome expressing a membrane protein cytosolic protein QM description of cation-π interaction between nascent chain and exit tunnel L.Trabuco, C. Harrison, E. Schreiner, and K. Schulten. Recognition of the regulatory nascent chain TnaC by the2.7 million atom simulation ribosome. Structure, 18:627-637, 2010. Ribosome-Trigger Factor- Ribosome-SecY-complex complexFrauenfeld, Gumbart et al. Nat. Struct. Mol. Bio. 18, 614-621, 2011.Wednesday, August 1, 2012
  • 31. Structural characterization of mRNA-tRNA translocation intermediates X. Agirrezabala, H. Liao, E. Schreiner, J. Fu, R. Ortiz- Meoz, K. Schulten, R. Green, and J. Frank. PNAS 109:6094-6099, 2012.Wednesday, August 1, 2012
  • 32. Lots to be done! BTRC for Macromolecular Modeling and Bioinformatics Beckman Institute, UIUC http://www.ks.uiuc.edu/Wednesday, August 1, 2012
  • 33. 17 Achievements Built on People 5 faculty members (2 physics, 1 chemistry, 1 biochemistry, 1 computer science); 8 developers; 1 system admin.; 16 post docs; 24 graduate students; 3 administrative staff L. Kale, J. Phillips: NAMD J. Frank, Columbia U.: MDFF, ribosome J. Stone, K. Vandivort: VMD R. Beckmann, U. Munich: ribosome I. Solov’yov, D. Chandler, M. W. Baumeister, MPI: whole cell imaging Sener, J. Strumpfer, J. Perilla, L. Liu, Greg Timp, UIUC, H. Gaub, U. Y. Liu, J. Gumbart, Y. Chan, X. Munich: biosensors; N. Hunter, Sheffield: Zou, E. Cruz-Chu: Science Proj.photosynthesis;. BTRC for Macromolecular Modeling and Bioinformatics Beckman Institute, UIUC http://www.ks.uiuc.edu/Wednesday, August 1, 2012