ion channels are the valves of cells ion channels are the main controllers of biological function
DESCRIPTION
+. ~ 30 Å. Ion Channels are the Valves of Cells Ion Channels are the Main Controllers of Biological Function. Ions in Water are the. Selectivity Different Ions carry Different Signals. Liquid of Life. Na +. Hard Spheres. Ca ++. Chemical Bonds are lines - PowerPoint PPT PresentationTRANSCRIPT
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Ion Channels are the Valves of CellsIon Channels are the Main Controllers of Biological Function
Chemical Bonds are linesSurface is Electrical Potential
Red is negative (acid)Blue is positive (basic)
Selectivity
Different Ions carry
Different Signals
Figure of ompF porin by Raimund Dutzler
~30 Å
0.7 nm = Channel Diameter
+
Ions in Water are the
Liquid of Life
3 Å
K+
Na+
Ca++
Hard Spheres
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Ion Channels are the Valves of CellsIon Channels are the Main Controllers of Biological Function
Chemical Bonds are linesSurface is Electrical Potential
Red is negative (acid)Blue is positive (basic)
Selectivity
Different Ions carry
Different Signals
Life occurs in
~130 mM salt solutions
Figure of ompF porin by Raimund Dutzler
~30 Å
Flow time scale is 0.1 msec to 1 min
0.7 nm = Channel Diameter
+
Averaging is in time, from 10-16 atomic scale to 10-4 biological scale!
Averaging is in number over 1012 water molecules needed to
specify 10-7M concentrationsand other ‘thermodynamic’ variables
Multiscale
3 Å
K+
Na+
Ca++
Hard Spheres
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Multiscale Issuesmore later
Biological Scales Occur Togetherso must be
Computed TogetherThis may be impossible in simulations
Physicists and Engineers have not tried
Computational Scale
Biological Scale
Ratio
Time 10-15 sec 10-4 sec 1011
Space 10-11 m 10-5 m 106
Spatial Resolution 1018
Solute Concentration 1011
Three Dimensional (106)3
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Natural nano-valves* for atomic control of biological function
Ion channels coordinate contraction of cardiac muscle, allowing the heart to function as a pump
Ion channels coordinate contraction in skeletal muscle
Ion channels control all electrical activity in cells
Ion channels produce signals of the nervous system
Ion channels are involved in secretion and absorption in all cells: kidney, intestine, liver, adrenal glands, etc.
Ion channels are involved in thousands of diseases and many drugs act on channels
Ion channels are proteins whose genes (blueprints) can be manipulated by molecular genetics
Ion channels have structures shown by x-ray crystallography in favorable cases
*nearly pico-valves: diameter is 400 – 900 picometers
Ion Channels are Biological Devices
~30 Å
K+
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Thousands of Molecular Biologists
Study Channels every day,
One protein molecule at a timeThis number is not an exaggeration.
We have sold >10,000 AxoPatch amplifiers
5
Ion Channel Monthly
AxoPatch 200B
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Channels are parts of Machines, e.g., Excitation-Contraction Coupling
L type Ca Channel RyR ryanodine receptor
L-type Ca Channel
RyR
Thanks for the figure toLászló Csernoch, Debrecen, HungaryIsabelle Marty, Grenoble, France
2Ca
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Function of SINGLE isolated RyR Channelsin Artificial Planar Lipid Bilayers
AxoPatchPatch-Clamp
Amplifier
ExperimentalChamber
PlanarBilayer
80-100 µMDiameter
TeflonSepta
FusedVesicle
Ca
Single Channel Current
open
closed
Designed at Rush
Slide from Mike Fill
Thanks!
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.
Open
ClosedCurrent
Amplitudein Picoamps (pA)
OpenDuration
in Milliseconds (ms)
current
# e
ven
ts
log time
ClosedDuration
in Milliseconds (ms)
log time
time time
# e
ven
ts
# e
ven
ts
# e
ven
ts
# e
ven
ts
Gating is Time Behavior
Selectivity,Permeation
are Amplitude
Gating and Permeation
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1 100
2
4
6
8
10
Open Duration /ms
Ope
n Am
plitu
de, p
ALowpass Filter = 1 kHz Sample Rate = 20 kHz
Ca2+ Release Channel of Inositol Trisphosphate Receptor : slide and data from Josefina Ramos-Franco. Thanks!
Typical Raw Single Channel Records
Current vs. time Amplitude vs. Duration
Channel Structure Does Not Changeonce the channel is open
5 pA
100 ms
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3 Å
K+
Na+
Ca++
Channels are SelectiveDifferent Ions Carry Different Signals through Different Channels
Figure of ompF porin by Raimund Dutzler
~30 ÅFlow time scale is 0.1 msec to 1 min
0.7 nm = Channel Diameter
+
ompF porin
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Different Types of Channelsuse
Different Types of Ions for
Different Information
Channels are Selective
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Central Problem*
How does the channel control selectivity?
*an example of “Reverse Engineering”
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For Modelers and Mathematicians: This is an inverse problem!
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Goal:
Understand Selectivity well enough to
Fit Large Amounts of Data* and to
Make a Calcium Channel
*from many non-ideal solutions
Atomic Scale Macro Scale
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Mutants of ompF Porin
30 60
-30
30
60
0
pA
mV
LECE (-7e)
LECE-MTSES- (-8e)
LECE-GLUT- (-8e)ECa
ECl
WT (-1e)
Calcium selective
Experiments have built
Two Synthetic Calcium Channels
As density of permanent charge increases, channel becomes calcium selective Erev ECa
Unselective
Wild Type
built by Henk Miedema, Wim Meijberg of BioMade Corp.,Groningen, Netherlands
Miedema et al, Biophys J 87: 3137–3147 (2004)
MUTANT ─ Compound
Glutathione derivativesDesigned by Theory
Atomic Scale
Macro Scale
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Central Problem*
How does the channel control selectivity?
“Reverse Engineering”
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This is an inverse problem
Closely related inverse problemshave been solved by mathematics used to design blast furnaces
Burger, Eisenberg, and Engl (2007) SIAM J Applied Mathematics 67:960-989
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Selective Binding CurveL type Ca channel
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Wolfgang Nonner
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Inverse Problem for SelectivityBadly posed,
simultaneously over and under determined
with noise and systematic error
has actually been solvedusing methods for the
Inverse Problem of a Blast Furnace
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Burger, Eisenberg and Engl (2007) SIAM J Applied Math 67: 960-989
For Modelers and Mathematicians: This is reverse engineering!
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Channels are only HolesWhy can’t we understand and build them?
Helpful to know physical basis of functionif we want to build one and improve it
Where do we start?
Not with Molecular Mythology
Not with gas phase models of traditional channologyLiquids are not Gases; biological solutions are not ideal
Not with guesses about trajectories of structural biologistsCounting, Statistics, and Averaging are Essential
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Why can’t we understand and build channels?
Uncalibrated Simulations will not make devices that
actually work
Unpopular view because Calibration is Hard Work
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Multiscale Issuesare the key
if we want to actually build channels that work
Computational Scale Biological Scale Ratio
Time 10-15 sec 10-4 sec Action Potential 1011
Space 10-11 m 10-5 m Side Chains of Proteins 106
Spatial Resolution 1018
Solute Concentration 10-11 to 20 Molar 1012
Three Dimensional (106)3
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Multiscale Issues
Biological Scales Occur Togetherso scales must be
CALIBRATED TOGETHERin real biological solutions
that are
MIXTURES OF IONS
Computational Scale Biological Scale Ratio
Time 10-15 sec 10-4 sec Action Potential 1011
Space 10-11 m 10-5 m Side Chains of Proteins 106
Spatial Resolution 1018
Solute Concentration 10-11 to 20 Molar 1012
Three Dimensional (106)3
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Multiscale Issues
Calibrations of Molecular Dynamics
in Real Solutions are just starting!
Unpopular Reality: hard work
It may not be possible to deal with scale ratios of1011 , 106 ,1018 , 1012 all at once
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Physicists and Engineers do not even try!
It may not be possible to deal withRatios of Scales
of
1011 106 1018 1012
all at once
Multiscale Issues
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Channels are only HolesWhy can’t we understand and build them?
Where do we start?
Science as Usual
Guess and Check
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Working Hypothesis
Biological Adaptation is
Crowded Ions and Side Chains
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Active Sites of Proteins are Very Charged 7 charges ~ 20 M net charge
Selectivity Filters and Gates of Ion Channels are
Active Sites
= 1.2×1022 cm-3
-
+ + + ++
--
-
4 Å
K+
Na+
Ca2+
Hard Spheres
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Figure adapted from Tilman
Schirmer
Pure water is 55 M OmpF Porin
Physical basis of function
Induced Fit of
Side Chains
Ions are Crowded
K+ Na+
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Working Hypothesis
Biological Adaptation is
Crowded Ions and Side Chains
Everything interactsClassical Models and Force Fields of Molecular Dynamics
assume no interactions with ion concentrations
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Finite Size EffectsWorking Hypothesis
‘Primitive Implicit Solvent Model’ learned from Doug Henderson, J.-P. Hansen, Stuart Rice, among others…
Thanks!
Chemically Specific Properties
of ions (e.g. activity = free energy per mole) come from interactions of their
Diameter and Charge
and dielectric ‘constant’ of ionic solutionAtomic Detail
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Ions in Water are the Liquid of Life. They are not ideal solutions
Chemically Specific Properties of Ionic Solutions come from
Interactions
Molecular Dynamics Force Fields are Calibrated assuming no interactions with concentrations
Force Fields must be REcalibrated in each Biological Solution
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Ions in Water are the Liquid of Life. They are not ideal solutions
Chemically Specific Properties of Ionic Solutions come from
Interactions
Chun Liu’s Energetic Variational Principle deals with Interactions
EnVarA
12 0
E
Dissipative 'Force'''Conservative Force
x u
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Everything Interacts
with
Everything
Ions in Water are the Liquid of Life
They are not ideal solutions
For Modelers and MathematiciansTremendous Opportunity for Applied Mathematics
Chun Liu’s Energetic Variational Principle EnVarA
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Ions in Water not ideal solutions
Chemically Specific Properties Come from Interactions
12 0
E
Dissipative 'Force'''Conservative Force
x u
Variational Principles Deal with Multiple Scales and Interactions Consistently and Automatically
Chun Liu’s Energetic Variational Principle deals with Interactions
EnVarA
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12 0
E
Dissipative 'Force'''Conservative Force
x u
Variational Principles Deal with Multiple Scales Consistently and Automatically
New Component or Scaleof Energy or Dissipation implies
New Field Equations (Euler Lagrange)
by Algebra AloneNo new Assumptions
EnVarA
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Energetic Variational ApproachEnVarA
Chun Liu, Yunkyong Hyon, and Bob Eisenberg
Mathematicians and Modelers: two different ‘partial’ variationswritten in one framework, using a ‘pullback’ of the action integral
12 0
E
'' Dissipative 'Force'Conservative Force
x u
Action Integral, after pullback Rayleigh Dissipation Function
Field Theory of Ionic Solutions that allows boundary conditions and flow and deals with Interactions of Components self-consistently
Composite
Variational Principle
Euler Lagrange Equations
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Energetic Variational Analysis EnVarA
being developed by Chun Liu with
creates a newMultiscale Field Theory of Interacting Components
that allows boundary conditions and flow and deals with
Ions in solutions self-consistently
(1) Yunkyong Hyon, Bob Eisenberg. Ions in Channels
(2) Rolf Ryham, Bob Eisenberg and Fred Cohen. Virus fusion to Cells
(3) Yoichiro Mori and Bob Eisenberg. Water flow in Tissues
Multiple Scales
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We have already established that the
Implicit Solvent (“Primitive”) Model of
Ionic SolutionsDescribes Calcium and Sodium Channels
quite well at Equilibriumwithout
Preformed Structure
Structure is the Computed Consequence of the Model
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O½
Selectivity Filter
Selectivity Filter Crowded with Charge
Wolfgang Nonner
+
++
L type Ca Channel
“Side Chains”
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Dielectric Protein
Dielectric Protein
6 Å
μ μmobile ions mobile ions =
Ion ‘Binding’ in Crowded Channel
Classical Donnan Equilibrium of Ion Exchanger
Side chains move within channel to their equilibrium position of minimal free energy. We compute the Tertiary Structure as the structure of minimal free energy.
Boda, Nonner, Valisko, Henderson, Eisenberg & Gillespie
MobileAnion
MobileCation
MobileCation
‘SideChain’
‘SideChain’
MobileCation
MobileCation
large mechanical forces
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Solved with Metropolis Monte Carlo MMC Simulates Location of Ions
both the mean and the variance
Produces Equilibrium Distribution of location
of Ions and ‘Side Chains’
MMC yields Boltzmann Distribution with correct Energy, Entropy and Free Energy
Other methods give nearly identical results:
Equilibrium Multiscale
MSA (mean spherical approximationSPM (primitive solvent model)
DFT (density functional theory of fluids),Non-equilibrium Multiscale
DFT-PNP (Poisson Nernst Planck)EnVarA…. (Energy Variational Approach)
etc
Multiscale Analysis at Equilibrium
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Key idea MMC chooses configurations with a Boltzmann probability and weights them evenly
instead of choosing them from uniform distribution and then weighting them with exp(−E/k BT)
Metropolis Monte Carlo Simulates Location of Ions
both the mean and the variance
1) Start with Configuration A, with computed energy EA
2) Move an ion to location B, with computed energy EB
3) If spheres overlap, EB → ∞ and configuration is rejected
4) If spheres do not overlap, EB → 0 and configuration is accepted
5) If EB < EA : accept new configuration.
6) If EB > EA : accept new configuration with probability
Details:
exp A B BE E k T
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Ion Diameters‘Pauling’ Diameters
Ca++ 1.98 Å
Na+ 2.00 Å
K+ 2.66 Å
‘Side Chain’ Diameter
Lysine K 3.00 Å
D or E 2.80 Å
Channel Diameter 6 Å
Parameters are Fixed in all calculations in all solutions for all mutants
Boda, Nonner, Valisko, Henderson, Eisenberg & Gillespie
‘Side Chains’ are Spheres Free to move inside channel
Snap Shots of Contents
Crowded Ions
6Å
Radial Crowding is Severe
Experiments and Calculations done at pH 8
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Na Channel
Concentration/M
Na+
Ca2+
0.004
0
0.002
0.05 0.10
Charge -1e
DE KA
Boda, et alEEEE has full biological selectivity
in similar simulations
Ca Channel
log (Concentration/M)
0.5
-6 -4 -2
Na+
0
1
Ca2+
Charge -3e
Occ
upan
cy (
num
ber)
EE EA
Mutation
Same Parameters
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Na, K, Li, Ca, Ba Binding in Calcium Channel
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Calcium Channelhas been examined in ~35 papers, e.g.,
44
Most of the papers are available at
ftp://ftp.rush.edu/users/molebio/Bob_Eisenberg/Reprints
http://www.phys.rush.edu/RSEisenberg/physioeis.html
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45Next, the Sodium Channel
Summary Simulation Paper (and target for new experiments!) in Experimental JournalCalcium Channel
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DEKA Sodium Channel 6 Å
Next, the
Sodium Channelspecifically, the
Aspartate D Acid NegativeGlutamate E Acid NegativeLysine K Basic PositiveAlanine A Aliphatic Neutral
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DEKA Sodium Channel has very different properties from Ca channel,
e.g., ‘binding’ curve,Na+ vs Ca++ selectivity
Na+ vs K+ selectivity
47
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Challenge from leading biophysicists
Walter Stühmer and Stefan HeinemannMax Planck Institutes, Göttingen, Leipzig
Can a physical theory explain the mutation DEEA into DEKA?
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Ca Channel
log (Concentration/M)
0.5
-6 -4 -2
Na+
0
1
Ca2+
Charge -3e
Occ
upan
cy (
num
ber)
EE EA
Boda, et al
Same Parameters
Mutation
Same Parameters
Mutation
EEEE has full biological selectivity
in similar simulations
Na Channel
Concentration/M
Na+
Ca2+
0.004
0
0.002
0.05 0.10
Charge -1e
DE KA
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Nothing was changed from the
EEEA Ca channelexcept the amino acids
50Calculations and experiments done at pH 8
Calculated DEKA Na Channel Selects
Ca 2+ vs. Na + and also Na+ vs. K+
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How?
How does the DEKA Na Channel Select Na+ vs. K+ ?
51Calculations and experiments done at pH 8
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Size Selectivity is in the Depletion Zone
Depletion Zone
Boda, et al
[NaCl] = 50 mM
[KCl] = 50 mM
pH 8
Channel Protein
Na+ vs. K+ Occupancy
of the DEKA Na Channel, 6 Å
Con
cent
rati
on
[M
olar
]
K+
Na+
Selectivity Filter
Na Selectivity because 0 K+
in Depletion Zone
K+Na+
Binding SitesNOT SELECTIVE
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Selectivity Filter
Selectivity FilterSelectivity Filter
Selectivity Filter
Selectivity Filter
Selectivity Filter
Selectivity FilterSelectivity Filter
log C/Cref
Binding Sites *Binding Sites are outputs of our INDUCED FIT
Model of Selectivity, not structural inputs
Boda, et al
Ion Diameter
Ca++ 1.98 Å
Na+ 2.00 Å
K+ 2.66 Å
‘Side Chain’ Diameter
3.00 Å
pH 8
2.80 Å
pH 8
Na Channel DEKA 6 Å
1 2O
+4NH
Lys or K
D or E
[NaCl] = [KCl] = 50 mM
Size Selectivity
NOT selective
BLACK = Depletion=0
Na vs K Size Selectivity is in Depletion Zone
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Na, K, Li, Cs Binding in Sodium channel
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Inverse Problem
We discover Control Variables* in simulations of the Na channel,
but not the Ca channel.
*These emerge as outputs. They are not inputs.
55
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Control Variables
discovered in DEKA Na channel
Selectivity Na+ vs K+ depends only on pore diameter
Diameter controls Selectivity
56
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Na+ vs K+ (size) Selectivity (ratio)
Depends on Channel Size,not Protein Dielectric Coefficient*
Selectivity for small ion
Na+ 2.00 Å
K+ 2.66 Å
*in DEKA Na Channel
K+ Na+
Boda, et al
Small Channel Diameter Large in Å
6 8 10
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• Selectivity Na+ vs K+ depends only on pore diameter
• Conductance* depends on protein polarization
Protein Dielectric Coefficient
controls
Conductance58*Gillespie & Boda (2008) Biophysical Journal 95:2658
Control Variables
discovered in DEKA Na channel
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80solvent
protein protein
Occ
upan
cy
Boda, et al
DEKA Na Channel, 6 Å
Channel Contentsoccupancy
Na+ 2.0 Å
K+ 2.66 Å
Control Variable
Channel Contents (occupancy) depends on
Protein Polarization (dielectric)
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Selectivity comes from
Electrostatic Interactionand
Steric Competition for SpaceRepulsion
Location and Strength of Binding Sites Depend on Ionic Concentration and
Temperature, etc
Rate Constants are Variables
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Channel and Contents form a
Self-Organized Structure
with Side Chains at position of Minimum Free Energy
Protein Fits the Substrate
“Induced Fit Model of Selectivity”
61
What does the protein do?
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Certain MEASURES of structure are
Powerful DETERMINANTS of Functione.g., Volume, Dielectric Coefficient, etc.
Induced Fit Model of SelectivityAtomic Structure is not pre-formed
Atomic Structure is an important output of the simulation
Nonner and Eisenberg
What does the protein do?
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Protein maintains Mechanical Forces*
Volume of PoreDielectric Coefficient/Boundary
Permanent Charge
What does the protein do?
Nonner and Eisenberg
* Driving force for conformation changes ??
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Binding Sites* are outputs of our Calculations
64
*Selectivity is in the Depletion Zone,NOT IN THE BINDING SITE
of the DEKA Na Channel
Our model has no preformedstructural binding sites
but
Selectivity is very Specific
Induced Fit Model of Selectivity
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We can actually compute the Structures that determine Selectivity
65
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Can EnVarA actually compute the Function of these systems?
66
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Vaccination against
Traditional Models
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Traditional Biochemistryand
Traditional Molecular DynamicsAssume
Ideal Solutions
Ions in Water are the Liquid of Life
Life Occurs in ~130 mM salt solutions
Ions in Water and Life are NOT ideal
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No gas phase models of traditional channel biochemistry
Liquids are not Gases
No discussions of individual trajectories of Structural Biologists
Counting and Statistics are essential
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Selectivity comes from
Electrostatic Attractionand
Steric Competition for SpaceRepulsion
Location and Strength of Binding Sites Depend on Ionic Concentration and
Temperature, etc
Rate Constants are Variables
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Conclusion
Selectivity can be understood by
Reduced Models
K channels Benoît Roux Susan Rempe
Na & Ca channels Nonner, et al,
RyR channels Gillespie & Meissner
Best Evidence
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Induced Fit Model
• Selectivity depends on Induced Fit of Side Chains and
Ions
• Induced Fit is the Self-Organized Structure
withMinimal Free
Energy73
Energy and Entropy
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Induced Fit Model
• Monte Carlo computes the Structure of Minimal Free Energy
• Monte Carlo computes the Induced Structure ‘perfectly’*
*but the model itself is far from perfect
74
Energy and Entropy
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Selectivity Depends Sensitively on Self-organized Structure and their Flexibility
Induced Structure is Different in Different Solutions
so
Structure must be Computed!
Rate constants are variables that change dramatically with conditions
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Computation Starts From Crystal Structure
when availablebut
Crystal Structures cannot determine Selectivity because
1) Crystal Structures are measured in only one unphysiological solution
2) Crystal Structures are not accurate enough3) Crystal Structures do not give entropy
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Miedema et al, Biophys J 87: 3137–3147 (2004)
30 60
-30
30
60
0
pA
mV
LECE (-7e)
LECE-MTSES- (-8e)
LECE-GLUT- (-8e)ECa
ECl
WT (-1e)
Calcium selective
Unselective
Wild Type
MUTANT ─ Compound
Remember
We can build them(reasonably well)
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Energetic Variational Analysis EnVarA
being developed by Chun Liu
Yunkyong Hyon and Bob Eisenberg
creates a
Field Theory of Ionic Solutions that allows boundary conditions and flow
and deals with
Interactions of Components Self-consistently
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Energetic Variational Analysis EnVarA
Chun Liu, Yunkyong Hyon, and Bob Eisenberg
12 0
E
'' Dissipative 'Force'Conservative Force
x u
Variational Principle
Rayleigh Dissipation Function
Field Theory of Ionic Solutions
that allows boundary conditions and flow and deals with Interactions of Components Self-consistently
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EnVarA
212 ( log log )B n n p pk T c c c c E dx
Microscopic
Finite Size EffectElectrostatic Entropy
(atomic)
Solid Spheres
212 ( )IPE t u w
Hydrodynamc Potential EnergyHydrodynamicEquation of StateKinetic Energy
(hydrodynamic)
Primitive Phase;
Macroscopic
Generalization of Chemical Free
Energy
Eisenberg, Hyon, and Liu
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Page 82
Ca2+ and Na+ Binding CurvesDEEA Calcium Channel
Eisenberg, Hyon, and Liu
Eisenberg, Hyon, and Liu
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Layering: Classical Interaction EffectComparison between PNP-DFT and MC
Anion PNP-PNP-DFTDFTCationAnion MCCationMC
Cha
rge
Den
sity
Position
IonDiameter
Eisenberg, Hyon, and Liu
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Energetic Variational Analysis EnVarA
Chun Liu, Yunkyong Hyon and Bob Eisenberg
New Interpretationslikely to be
Controversial but
Quantitative and Testable
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Energetic Variational Approach EnVarA
*if they define an energy and its variation :Energy defined by simulations or theories or experiments is OK.
Full micro/macro treatment is needed for an Atomic Model,
with closure.
Eisenberg, Hyon, and Liu
New mechanisms* can be added
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RyR ReceptorGillespie, Meissner, Le Xu, et al,
not Bob Eisenberg
More than 120 combinations of solutions & mutants
7 mutants with significant effects fit successfully
Best Evidence is from the
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Selectivity Filter• is 10 Å long and 8 Å
in diameter• confines four D4899
negative amino acids.
Four E4900 positive amino acids are on lumenal side, overlapping D4899.
Cytosolic distributed charge
The Geometry
D. Gillespie et al., J. Phys. Chem. 109, 15598 (2005).
Protein
Protein
Cytoplasm Lumen
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Nonner, Gillespie, Eisenberg
DFT/PNP vs Monte Carlo Simulations
Concentration Profiles
Misfit
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Divalents
KClCaCl2
CsClCaCl2
NaClCaCl2
KClMgCl2
Misfit
Misfit
Error < 0.1 kT/e
2 kT/e
Gillespie, Meissner, Le Xu, et al
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KCl
Misfit
Error < 0.1 kT/e
4 kT/e
Gillespie, Meissner, Le Xu, et al
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Theory fits Mutation with Zero ChargeNo parameters adjusted
Gillespie et al J Phys Chem 109 15598 (2005)
Protein charge densitywild type* 13 M
Water is 55 M
*some wild type curves not shown, ‘off the graph’
0 M in D4899
Theory Fits Mutant in K + Ca
Theory Fits Mutant in K
Error < 0.1 kT/e
1 kT/e
1 kT/e
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