Rebecca C. Wade
M
C
M
4/20/2007
M
C
M
Animation: Inner Life of the Cell
Exploring Biomolecular
Recognition by Modeling and
Simulation
„
Biomolecular 3D Structures
Multimedia Production Site at Harvard Univ.
‹ http://multimedia.mcb.harvard.edu/
Rebecca C. Wade
‹ http://multimedia.mcb.harvard.edu/media.html
Molecular & Cellular Modeling Group,
EML Research gGmbH
‹ Conception
) Alain
Heidelberg
and scientific content :
Viel and Robert A. Lue.
‹ Animation
) John
Liebler/XVIVO.
E
rebecca.wade@eml-r.villa-bosch.de M
B
http://projects.villa-bosch.de/mcm/ L
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From Individual Molecules to
Cellular Systems
Biomolecular 3D Structures:
2007:
42750
Structures
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Exploring Protein Interactions via
the Computer
ƒSmall molecules
ƒProteins
ƒNucleic acids
ƒ….
2007:
1054
Folds
Protein
Interactions
ƒIndividual proteins –
in-depth
ƒPhysics-based
ƒBio/ChemoInformatics
ƒStability
ƒFolding
ƒCatalysis
ƒElectron transfer
ƒ….
Methods
Applications
ƒProteins “in context” –
biochemical pathways
ƒComparison across
large protein families
www.rcsb.org – 04.07
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Protein-Ligand Interactions in silico
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D. Goodsell
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Rebecca C. Wade
4/20/2007
Biomolecular recognition:
„
Why compute?
‹
Do the molecules bind?
Biomolecular recognition:
Cellular
localization
Diffusional
association
Affinity?
Kinetics?
) Specificity?
) Selectivity?
)
How do biomolecules
recognize each other?
Protein
conformational
dynamics
)
‹
Binding mechanism?
Docking mode?
Effects of mutations?
) Effects of changes of environment?
)
)
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Molecular concentrations
and lifetimes
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Computational Problems:
Biomolecular recognition:
Cellular
localization
„
Sampling
„
Scoring
Molecular concentrations
and lifetimes
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Protein-Ligand Interactions in silico
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Diffusional
association
Inhibition of barnase by barstar
Protein
conformational
dynamics
Barnase catalyses
cleavage of single stranded RNA
(outside the cell)
Barstar blocks the active
site, and catalysis by
barnase
(in the cell)
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Rebecca C. Wade
4/20/2007
Barnase-Barstar:
Electrostatic Complementarity
„
Barnase : +2e
extracellular
ribonuclease
‹ Bacillus
amyloliquefaciens
‹
„
Bimolecular Diffusional
Association Rate
Brownian Dynamics Simulations
„
The rate to form Encounter Complex
Smoluchowski rate
„
„
A
Smoluchowski
1927
B
kon , sm = 4π ⋅ (D A + D B ) ⋅ (R A + R B ) ~ 10 M s
intracellular inhibitor
„
Patches
„
„
Contacts
A
B
„
„
−1 −1
10
Barstar : – 6e
‹
„
„
A
Kd ~ 10-14 M
Kon ~ 1010 M-1s-1
(zero ionic
strength)
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„
θ
„
B
„
Northrup &
Erickson, 1992
k a >> kon , sm ⋅ f (θ )
Solc & Stockmayer, 1973
H.-X. Zhou, 1993
ka ~ 106 M-1s-1
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Barnase:Barstar:
Dependence of Kon on mutation
„
„
Measured on-rates
of wild-type and 11
mutants
reproduced by
simulations
End point of
diffusion :
formation of 2
polar contacts
observed in crystal
structure of
complex
„
„
Start a large number of
trajectories from b-surface
Monitor reaction
β - fraction of reactive
trajectories
β
kon = kb , sm ⋅
1 − (1 − β )kb , sm / k c ,sm
Rigid molecules
Atomic detail
No overlaps
Electrostatic forces
Δr=(DΔt/kT)F + R; etc
<Ri>=0, <Ri2> =2DΔt (i= x,y,z)
Timestep Δt ≥1 ps
‹
‹
„
Sufficient to compute:
‹
‹
„
Rigid proteins
Shape exclusion
Poisson-Boltzmann electrostatics with effective charge
representation (interaction + electrostatic desolvation
terms)
Absolute rates
Effects of mutation, ionic strength, pH…
Unless protein conformational change is important:
‹
E.g. gating by loop motion
„
„
„
„
„
„
„
„
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Gabdoulline & Wade,
Biophys. J. (1997) 72,
1917-29
Protein-Ligand Interactions in silico
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B
b-surface
B
c-surface
Protein-protein binding:
Association Rates
Model with:
‹
A
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To compute diffusional association rate
constants for two proteins:
„
B
Observed
Diffusion limit (no orientational requirement)
Orientational restriction
- reduces 1000 times
Electrostatic steering
- may increase 1000 times
Conformational gating
- may reduce 100 times
„
103 -1010
„
1010
„
107
„
107 -1010
„
105 -1010
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Rebecca C. Wade
4/20/2007
Superoxide diffusion to superoxide
dismutase and peroxidases
„
„
SOD:
‹ Kcat/Km ~ 1010 M-1s-1 (theoretical limit)
‹ Rate enhanced by electrostatic interactions
‹ Simulations: many; reproduce effects of charge mutations
Peroxidases:
‹ Myeloperoxidase: rate (107 M-1s-1) depressed by electrostatic
interactions!
Bridging from molecular
simulation to biochemical
networks:
Oscillations and cell function:
e.g. activated neutrophils
Biochemical network simulation
d
[coI] = k1 ⋅[H 2O2 ][per3+] − k−1[coI]
dt
…
d
−
[coIII] = k4 ⋅ [O2 ][per3+]
dt
Simulations
show
oscillations
Observed oscillation
of cellular metabolite
concentrations
Structure-based molecular simulation
per3+ using electrostatic interactions
NAD(P)H oscillation in
activated neutrophils:
Enhanced by
melatonin in activated
neutrophils
Olsen et al, BJ, 2003
Per3+
pSOD
bSOD
mpo
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Peroxidase-oxidase (PO) Reaction
„
‹
Myeloperoxidase PO Reaction
Petty et al
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Myeloperoxidase PO Reaction
NAD(P)H and O2 concentration
Period : ~10 s
PO reaction catalyzed by myeloperoxidase
‹
‹
‹
‹
„
Stein, Gabdoulline, Wade Curr. Op. Struct. Biol. (2007) 17, 166-172
Oscillations in activated neutrophils:
‹
„
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Most abundant protein in neutrophils
Delivered to phagosome
Cofactor: melatonin
NADPH oxidase in the phagosome membrane
Overall reaction:
‹ 2NAD(P)H + 2H+ + O2 → 2NAD(P)+ + 2H2O
Olsen, Kummer, Kindzelskii, Petty, Biophys. J (2003) 84, 69-81
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Protein-Ligand Interactions in silico
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Myeloperoxidase: Active site access
Gabdoulline, Kummer, Olsen, Wade, Biophys. J (2003) 85, 1421-1428
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I= ∞
pH8 pH5
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Bridging from molecular
simulation to biochemical
networks:
Computed
values of
kinetic
constants
Oscillation dependence on
elementary step rate for MPO:
Biochemical
network
simulation
d
an ns
ing tio
ain ula
-gr im
rse ale s
a
Co ltisc
mu
Structure-based
molecular
simulation
Model for
cellular level
phenomenon
Electrostatics of 3 Peroxidases
myeloperoxidase
lactoperoxidase
pH 5
pH 5
myeloperoxidase
horseradish peroxidase
pH 8
pH 5
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Similarity-Based Estimate of Kinetic
Constants
Similarity-Based Estimate of Kinetic
Constants
measurement
@ condition 1
measurement
@ condition 1
measurement
@ condition 2
3D model 1
kinetic constant 1
kinetic constant 2
Yeast@35oC
Molecular level
insights providing
basis for design
of altered cellular
behavior
Cow@5oC
??
kinetic constant 3
Protein-Ligand Interactions in silico
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kinetic constant 2
Yeast@35oC
??
metabolic network simulations @ condition 3
Stein, Gabdoulline, Wade Curr. Op. Struct. Biol. (2007) 17, 166-172
3D model 2
kinetic constant 1
Cow@5oC
3D model 3
??
Human@37oC
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measurement
@ condition 2
kinetic constant 3
??
Human@37oC
metabolic network simulations @ condition 3
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Rebecca C. Wade
4/20/2007
PIPSA:
Protein Interaction Property Similarity
Analysis
Triose Phosphate Isomerase
T. brucei
Φ (i, j, k)
1
Φ (i, j, k)
2
Protein 1
Protein 2
• Interaction fields are calculated on a set of points
• Field values on corresponding points are
compared
• Φ = electrostatic potential, shape, probe
interaction field, ...
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SYCAMORE:
Systems
biology's
Computational
Analysis and
Modeling
Research
Environment
V.marinus
Comparative Systems Biology: Across
Organisms
Drosphila
•40/55% sequence identity/homology
•same fold
•very similar active site
•factor of 3 difference in kcat/Km
Anopheles
Arabidopsis
Homo sapiens
12 species:
Giardia lambli
Spinach
Chicken
E. coli
Human
L. mexicana
P. falciparum
Rabbit
T. brucei
T. cruzi
V. marinus
Yeast
Chicken
Rice
Mouse
Orangutan
African claw frog
Rat
The Glycolytic Pathway
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Biomolecular recognition:
Cellular
localization
Zebrafish
Diffusional
association
Cytochrome P450s:
Heme monooxygenases
Protein
conformational
dynamics
•Activate oxygen
•Insert 1 O atom
into substrate
RH + O2 + 2H+ + 2e¯ → ROH + H2O
Molecular concentrations
and lifetimes
Cysteine-Iron
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Protein-Ligand Interactions in silico
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Rebecca C. Wade
4/20/2007
Drug Metabolism (simplified)
Multiple interactions of CYP
P450 enzymes
Using Activated Oxygen
Oxygen added
P450
CYP-R
P450 2C5
P
e- ?
Medicine
Insoluble
in water
OH
?
? Pi
?
S
Lipid bilayer
Liver
Progesterone
Kidneys&bladder
21 hydroxy progesterone
Urine
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Cytochrome P450: Active Site
Sequestered in Protein
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Wade, Motiejunas, Schleinkofer, Sudarko,
Winn, Banerjee, Kariakin, Jung, BBA
(2005) 1754, 239-244.
Random Acceleration
Molecular Dynamics (RAMD)
Simulation of Protein in Water
P450eryf
P450cam
SAPHYR Model:
Lounnas, Lüdemann & Wade, Biophys. Chem. (1999) 78, 157-182
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Protein-Ligand Interactions in silico
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„
Artificial randomly oriented force is applied to center of mass of ligand
„
Force magnitude; application time; threshold distance
„
Reduces time required for ligand to leave protein
„
Probe for weak spots in protein through which ligand can egress
Lüdemann, Lounnas & Wade, J. Mol. Biol. (2000) 303, 797-811, 813-830
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Rebecca C. Wade
4/20/2007
Cytochrome P450-BM3
Substrate Exit Mechanisms: pathway 2a
Substrate Exit
Pathways
P450cam
P450cam
3
P450BM-3
P450BM-3
Winn, Lüdemann, Gauges, Lounnas
& Wade, PNAS (2002) 99, 5361
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Mammalian Cytochrome P450:
rabbit CYP2C5
Egress of substrates
and products in
RAMD simulations
predominantly via
pathway 2c
Charged sidechain –
backbone carbonyl interaction
Protein-Ligand Interactions in silico
Schleinkofer, Sudarko,
Winn, Lüdemann, &
Wade, EMBO Reports
(2005) 6, 584-589.
P450eryF
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Cytochrome P4502C5
CYP2C5
P450 2C5: exit pathway 2c
2a residues hydrophobic
WLQVYNNFPALLDYFPGI
2a unlikely to open in water
(towards the reader)
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P450eryF
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3
N.B.: K241:V106 CO H-bond loss
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Rebecca C. Wade
4/20/2007
CYP2C5 + Membrane
Multiple ligand access pathways
and ligand channelling in
mammalian P450s?
CYP2C5 + Membrane
2a residues in membrane
2a might open
2c still accessible
1-way route
Schleinkofer, Sudarko, Winn,
Lüdemann, & Wade, EMBO
Reports (2005) 6, 584-589.
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P450s: Conclusions
P450s In Cellular Systems?
„
Channelling?
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2-way route
Adaptation of P450 fold to
different cellular locations and
different substrate specificity
profiles
Biomolecular recognition:
„ Simulation of 5 cytochromes P450
‹ RAMD
CYP79A1 CYP79E1
?
Heme
Reductase
Protein
UDPGglucosyl transferase
)Unbiased search for egress routes
)Egress can occur by channels not present in the crystal structure
even if other channels are present
)Provides mechanistic insights into egress mechanisms
)Does NOT sample low frequency motions of protein
„
Why compute?
‹
Affinity?
Kinetics?
) Specificity?
) Selectivity?
)
Substrate
Reductase
Protein
„ Gating mechanisms are optimised for their natural
substrates
‹ Filter for substrate specificity
‹ Filter for product release
„
Sampling
„
Scoring
)
Heme
2c?
Do the molecules bind?
‹
Binding mechanism?
Docking mode?
Effects of mutations?
) Effects of changes of environment?
)
„ Multiple channels
‹ Access from membrane
‹ Access from solution
‹ Channeling between proteins
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Protein-Ligand Interactions in silico
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)
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Rebecca C. Wade
The MCMers …….
4/20/2007
Acknowledgements:
L → R:
Sulaiman Faisal
‹
Razif Gabdoulline, Matthias Stein, Vlad Cojocaru, Peter Winn
‹
Karin Schleinkofer (Univ. Würzburg), Sudarko (University of Jember,
Indonesia), Tim Johann (BCB, EML Research), Susanna Lüdemann
(EPO), Valere Lounnas, Ralph Gauges (BCB, EML Research), Ting
Wang (UCDavis)
‹
Ursula Kummer (EML/Univ. Heidelberg)
Anna Feldman-Salit
‹
Vlad Cojocaru
‹
Danni Harris (Molecular Research Institute, USA)
Michal Otyepka, Martin Petřek, Jiři Damborský (Brno, Czech
Republic)
Eric Johnson (Scripps, USA), Duncan McRee (Syrrx, USA)
Jung-Hsin Lin & Andrew McCammon (UCSD, USA)
Amit Banerjee (Wayne State Univ, USA)
Georgi Pachov
Razif Gabdoulline
Domantas Motiejunas
Matthias Stein
Stefan Henrich
Stefan Richter
Rebecca Wade
Peter Winn
Outi Salo-Ahen
+Divita Garg
+ Michael Martinez
$$$: Klaus Tschira Foundation, EU, DAAD, AVH, BMBF, BIOMS, DFG
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Protein-Ligand Interactions in silico
+ Matthias Janke
‹
‹
‹
$$$: Klaus Tschira Foundation, BMBF, DAAD, NIH, EU, DFG
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10