Discoveries Through theComputational Microscope
Investigation of drug (Tamiflu) resistance of the “swine” flu virus demanded fast response! Klaus Schulten
Department of Physics and Theoretical and Computational Biophysics Group
University of Illinois at Urbana-Champaign
Accuracy • Speed-up • Unprecedented Scale
100 - 1,000,000 processors
The Computational
Microscope
Viewing the Morphogenesis of a Cellular Membrane from Flat to Tubular in 200 µs
DOE (INCITE)
Viewing the Morphogenesis of a Cellular Membrane from Flat to Tubular in 200 µs
Cell, 132:807 (2008)
Cryo-EM image
A. Arkhipov, Y. Yin, and K. Schulten. Four-scale description of membrane sculpting by BAR domains. Biophysical J., 95: 2806-2821 2008.
Ying Yin, Anton Arkhipov, and Klaus Schulten. Simulations of membrane tubulation by lattices of amphiphysin N-BAR domains. Structure 17, 882-892, 2009.
CPC-D-10-00292
Viewing How Proteins are Made from Genetic Blueprint
• Ribosome — Decodes genetic information from mRNA• Important target of many
antibiotics• Static structures of crystal forms
led to 2009 Nobel Prize• But one needs structures of
ribosomes in action!
protein-conducting channel
membrane
mRNA
new protein
ribosome
ribosome
protein-conducting channel
• Ribosome — Decodes genetic information from mRNA• Important target of many
antibiotics• Static structures of crystal forms
led to 2009 Nobel Prize• But one needs structures of
ribosomes in action!
Viewing How Proteins are Made from Genetic Blueprint
Low-resolution data High-resolution structure
Close-up of nascent protein
Viewing How Proteins Are Made from Genetic Blueprint
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CPU cores
NCSA Lincoln Cluster performance(8 Intel cores and 2 NVIDIA Tesla GPUs per node, 1 million atoms)
2 GPUs
4 GPUs8 GPUs
16 GPUs
~2.8
GPUs reduced time for simulation from two months to two weeks!
Molecular dynamics simulation of protein insertion process
Faster
GPU Solution 3: Molecular Dynamics Simulations
Viewing Nanopore Sensors
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methylated DNA un-methylated DNA
Genetics: Genes control our bodies and experiences!Epigenetics: Our bodies and experiences control the
genes!Epigenetics made possible through DNA methylation
small changebig effect
methylation
Related pathologies: obesity, depression, cancer
Detect methylation with nanopores
hard to move
easy to move
un-methylated DNA
methylatedDNA
Viewing Nanopore SensorsCreate a Better Nanopore with Polymeric Materials
nanopre-cipitationof ions
New materials, new problems: Nanoprecipitation Radial distribution functions
identify nanoprecipitation
Precipitate
Liquid
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Intel X5550, 4-cores @ 2.66GHz
6x NVIDIA Tesla C1060 (GT200)
4x NVIDIA Tesla C2050 (Fermi)
Faster
GPU Solution 4: Computing Radial Distribution Functions
• 4.7 million atoms• 4-core Intel X5550 CPU: 15 hours• 4 NVIDIA C2050 GPUs: 10 minutes• Fermi GPUs ~3x faster than GT200
GPUs: larger on-chip shared memory
Precipitate
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Collaborator: Bernard C. Lim (Mayo Clinic College of Medicine)
20ns SMD Simulation of fibrinogen, 1.06 million atoms, 1.2 ns/day with pencil decomposition, 15 days on PSC XT3 Cray (1024 processors)
A Blood ClotRed blood cells within a network of fibrin fibers, composed of polyme-rized fibrinogen mole-cules.
Inspecting the mechanical Strength of a blood clot
12
B. Lim, E. Lee, M. Sotomayor, and K. Schulten. Molecular basis of fibrin clot elasticity. Structure, 16:449-459, 2008.
Collaborator: Bernard C. Lim (Mayo Clinic College of Medicine)
20ns SMD Simulation of fibrinogen, 1.06 million atoms, 1.2 ns/day with pencil decomposition, 15 days on PSC XT3 Cray (1024 processors)
A Blood ClotRed blood cells within a network of fibrin fibers, composed of polyme-rized fibrinogen mole-cules.
12
B. Lim, E. Lee, M. Sotomayor, and K. Schulten. Molecular basis of fibrin clot elasticity. Structure, 16:449-459, 2008.
Inspecting the mechanical Strength of a blood clot
13
Petascale simulations will Permit Sampling For Example Carrying out a Second Simulation Required
by a Referee
model Szabo and Hummer, 2003.
Longest ever (1 us) SMD simulation closes gap between simulation and experiment!!!!!
Stretched is I91, one of the 300 domains of titin
Microsecond simulations of muscle elasticityReaching for Overlapping Time Scales
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Figure 1: Schematic representation of kinase sensor. (a) A single peptide is represented as a green line.After phosphorylation by protein kinase, a residue becomes negatively charged. The phosphorylated residueis pictured as red dot. A fluorescence marker is drawn as a blue star. (b) The sensor consists of peptidesthat are attached to a metallic gold surface. (c) When peptides are non-phosphorylated, spectroscopic signalis una↵ected by an applied electric field. (d) When peptides are phosphorylated, the negative charges bendthe peptides depending on the voltage polarity, enhancing the spectroscopic signal. (e) Experimental setupof the Raman spectroscopy. Kinase sensor is facing down. Peptides on SERS substrate are excited with alaser with wavelength of 785 nm, and the reflected Raman signal is collected with a spectrometer.
through an electrophoretic chamber, where they are detected by fluorescence spectroscopy.
We propose a modified sensor, where the peptides are covalently attached to a gold wafer
(Figure 1.b), immobilizing one end of the peptide to the metallic surface, while the other
peptide end is bound to a marker molecule. The functionality of the device relies on register-
ing di↵erent spectroscopy signatures for non-phosphorylated and phosphorylated peptides
under an external electric field. The intensity of the spectroscopic signal varies according to
the streching or coiling of the peptides. When a voltage biases is applied orthogonal to the
surface, non-phosphorylated peptides should emit a characteristic signal which is insensitive
to the changes in voltage (Figure 1.c). Conversely, phosphorylated peptides, due to their
extra negative charges, are sensitive to the voltage biases and change the conformation of
the peptides, either increasing or decreasing the spectroscopic signal (Figure 1.d).
Accordingly, we considered surface-enhanced Raman spectroscopy (SERS) as a sensitive
method to detect the chemical fingerprint of phosphorylation.The advantages of this method
are listed below: First, all optically excited molecules display a specific Raman shifts sig-
4
Design of Tyrosine Kinase Sensor
Rhodamine (R6g) Tyrosine residue interacts with kinases
by phosphorylation
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Figure 2: Proposed kinase sensor studied by experiments and simulations. (a) Optical image of sensor: A5⇥5 square array pattern are created by a SEPERISE method (see text). A thin layer of 5 nm titaniumfollowed by a layer of 80 nm gold is deposited on top of the nanocone structures to activate the sensorsurface. 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 probesare immobilized on the gold covered surface of nanocones. (c) Atomic model of kinase sensor. Peptides arecolored in green, metallic surface in yellow. A single peptide is highlighted in licorice representation. Blueand red arrows point to the rhodamine cap and phosphorylated tyrosine residue, respectively.
8
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�F� �G��E�)OXRUHVFHQFH�LPDJH�VKRZLQJ�SHSWLGH�SUREHV�ZHUH�VXFFHVVIXOO\�LPPRELOL]HG�RQ�WKH�QDQRFKLS�VXUIDFH
Figure 2: Proposed kinase sensor studied by experiments and simulations. (a) Optical image of sensor: A5⇥5 square array pattern are created by a SEPERISE method (see text). A thin layer of 5 nm titaniumfollowed by a layer of 80 nm gold is deposited on top of the nanocone structures to activate the sensorsurface. 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 probesare immobilized on the gold covered surface of nanocones. (c) Atomic model of kinase sensor. Peptides arecolored in green, metallic surface in yellow. A single peptide is highlighted in licorice representation. Blueand red arrows point to the rhodamine cap and phosphorylated tyrosine residue, respectively.
8
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Figure 2: Proposed kinase sensor studied by experiments and simulations. (a) Optical image of sensor: A5⇥5 square array pattern are created by a SEPERISE method (see text). A thin layer of 5 nm titaniumfollowed by a layer of 80 nm gold is deposited on top of the nanocone structures to activate the sensorsurface. 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 probesare immobilized on the gold covered surface of nanocones. (c) Atomic model of kinase sensor. Peptides arecolored in green, metallic surface in yellow. A single peptide is highlighted in licorice representation. Blueand red arrows point to the rhodamine cap and phosphorylated tyrosine residue, respectively.
8
•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.
Au surface, oligopeptide with 12 AA, bound via sulfide bond
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}
MD Simulations Revealed Problems in Design
•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.
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}
Initial sequence did not work due to surface adhesion and rhodamine aggregation
Current Design Improved
Rhodamine removed,Fluorescence signal discarded
MD Simulations show that
phosphorylated tyrosine bends the
peptide depending on voltage polarity
0 1 2 3 4 5 60
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Tyr-Gold /
A
time / ns
+60V-60V0V
700 1100 1500
0
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inte
nsity
/ A
U
+1.2V-1.2V
0VExperiments show that peptide bends
towards the surface at positive voltages,
increasing the intensity of Raman
shifts.Phosphorylated tyrosine
Peptide bending is now measured with Raman Spectroscopy. The detection relies on careful comparison of peak points
New sequence: rhodamin removed and measurement replaced by Raman spectroscopy
0 1 2 3 4 5 60
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ity /
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EGIYGVLFKKKC-AU EGI(pY)GVLFKKKC-AU
Unphosphorylated Phosphorylated
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Figure 4: Peptide phosphorylation revealed by molecular dynamics simulations. Panels (a) and (b) showthe distance from the tyrosine residue to the gold surface for di↵erent voltage polarities. Error bars represent± standard deviation. (a) Non-phosphorylated peptide sensor under +60 V (blue), -60 V (red) and 0 voltagebiases. (b) Phosphorylated peptide sensor under +60 V (blue), -60 V (red) and 0 voltage biases. Panels(i) and (ii) show snapshots of two peptides after 5 ns for +60 V (i) and -60 V (ii) voltage biases. Thepeptides are represented as gray tubes. Rhodamine and tyrosine residues are shown in blue and red colors,respectively.
14
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Figure 4: Peptide phosphorylation revealed by molecular dynamics simulations. Panels (a) and (b) showthe distance from the tyrosine residue to the gold surface for di↵erent voltage polarities. Error bars represent± standard deviation. (a) Non-phosphorylated peptide sensor under +60 V (blue), -60 V (red) and 0 voltagebiases. (b) Phosphorylated peptide sensor under +60 V (blue), -60 V (red) and 0 voltage biases. Panels(i) and (ii) show snapshots of two peptides after 5 ns for +60 V (i) and -60 V (ii) voltage biases. Thepeptides are represented as gray tubes. Rhodamine and tyrosine residues are shown in blue and red colors,respectively.
14
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Figure 5: Optimized sequence. A new sequence is proposed from molecular dynamics simulations. Thereis no rhodamine caps; avoiding aggregation. Unnecessary lysine residues changed by alanine residues. Thenon-phosphorylated peptide sensor is still insensitive to an electric field (a), while the phosphorylated oneis responsive to an external electric field (b); therefore it should produce distinctive raman signatures foropposite voltage polarities. Error bars represent ± standard deviation. Color blue, red an green represent+60 V, -60 V and 0 voltage biases.
16
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Figure 5: Optimized sequence. A new sequence is proposed from molecular dynamics simulations. Thereis no rhodamine caps; avoiding aggregation. Unnecessary lysine residues changed by alanine residues. Thenon-phosphorylated peptide sensor is still insensitive to an electric field (a), while the phosphorylated oneis responsive to an external electric field (b); therefore it should produce distinctive raman signatures foropposite voltage polarities. Error bars represent ± standard deviation. Color blue, red an green represent+60 V, -60 V and 0 voltage biases.
16
A new sequence is proposed from molecular dynamics simulations. There are no rhodamine caps, avoiding aggregation. Unnecessary lysine residues are changed to alanine residues. The resulting non-phosphorylated peptide sensor is still insensitive to an electric field, while the phosphorylated one is responsive to an 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.
Simulation-Optimized Sequence
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