Transcript Document

Quantitative properties of
protein-protein interactions
Ed Evans, T-cell biology group
[email protected]
www.t-cellbiology.org/teaching
Why this lecture?
• Protein/protein interactions are fundamental
to biology and therefore to medicine!
• In the past much of the focus has been on
qualitative information.
a) What proteins interact?
b) What is the function of the interaction?
• Now quantitative information is also
considered increasingly important
[email protected]
www.t-cellbiology.org/teaching
An example: T-cell co-stimulation
CD86
20mM
4mM
CD80
Affinity
Valency
Stoichiometry
3mM
0.2mM
~10,000
times
stronger
CD28
CD28
Co-stimulation
[email protected]
CTLA-4
CTLA-4
Inhibition
www.t-cellbiology.org/teaching
Why this lecture?
• Protein/protein interactions are fundamental
to biology and therefore to medicine!
• In the past much of the focus has been on
qualitative information.
a) What proteins interact?
b) What is the function of the interaction?
• Now quantitative information is also
considered increasingly important
a) Helps understand molecular mechanisms
b) Essential for modelling complex processes
c) Important for drug discovery
[email protected]
www.t-cellbiology.org/teaching
What we’ll cover (hopefully)
1. What binding properties are important?
2. How might we measure them?
(introduction – more tomorrow!)
3. Comparison of interactions
‘in solution’ vs. at the cell surface
[email protected]
www.t-cellbiology.org/teaching
1. What binding properties are important?
One protein soluble: Cell-cell interaction:
Affinity
Valency
(Kinetics)
(Thermodynamics)
Mechanical Stress
(unbinding force)
ATO M I C
[email protected]
Real membranes:
Lateral diffusion rate
Inter-membrane
distance (size)
Abundance => Avidity
STRUCTURE
www.t-cellbiology.org/teaching
Molecules involved … T-cell surface
CD45*
CD3*
TCR2* TCR1*
Galectin-1
CD225* CD247()*
CS1 CD2*
CD52*
TIRC7* CD6*
CD50*
CD48*
CD11a*‡
CD164
CD236R
CD39
CD82* 1*
HM1.24
CD184*
CD53*
?
P
CD96*
CD99 CD7*
CD122*
CD25
CD132*
Galectin-3
GPR68*
ESL-1
CD37*
CD44
FLJ23270*
CD71
CD47R
EDG4
CD244
CD5*
CD97*
NKp30*
CD195*
CD46
CD147
CD183
?
CD101
CD150
CD229
CD38 CD49c‡
CD226
CD230
?
CD70
CD49a‡
CD224
?
?
CD201
CD26
[email protected]
CXCR5
CD86
RAGE
CD146
CD44R
TR2
CD168
CD160
CCR4
MDC-L
CD58 CD72
?
TRAILR2
? ?
?
CD51‡ CD56
?
CCR2
?
?
CD178
CDw210*
CD63
CCR1
NTBA
CD59
Toso
Galectin-9
apoB48R
CD68
NKG2E/H*
CD94
? ?
CD107a
? CD120a CD127
CD100
CD132*
CD95
CD43
CD222
CD223
CD103‡
CD69*
CD162/R*
P
Galectin-11
IL-11R
NKG2-F ?
CRTH2
Galectin-13
MAFA-L
?
?
Porimin
SRCL
ST2L
OAP-1
www.t-cellbiology.org/teaching
Molecules involved … T-cell surface
Including less well characterised / housekeeping molecules...
[email protected]
www.t-cellbiology.org/teaching
Background: binding models
1.
The majority of protein:protein interactions are
simple 1:1 associations: A + B  AB
This is what we will focus on in this lecture.
2. The most common variation of this scheme is where
one protein (e.g. B) has additional binding sites.
e.g.
AB + A  A2B
or
AB + C  ABC
3. If these binding sites are independent then one
simply treats each interaction as a 1:1 association
and adds them together.
4.
If the binding sites are not independent then one
has positive or negative cooperativity (i.e. allosteric
effects) and more complex modeling is required.
[email protected]
www.t-cellbiology.org/teaching
What binding properties are important?
• Affinity
– KA (affinity constant) or KD (dissociation constant)
– KD= 1/KA
• Kinetics
– kass or kon (association rate constant or on rate)
– kdiss or koff (dissociation rate constant or off rate)
• Thermodynamic properties
– H (enthalpy change on binding)
– S (entropy change on binding)
– C (heat capacity change on binding)
[email protected]
www.t-cellbiology.org/teaching
Affinity
1. Measures how favourable an interaction is
2. Best expressed as affinity constant: KA
3. For A + B  AB
–
–
–
ABeq
1
KA 

Aeq Beq K D
Best thought of as the ratio of [products] vs.
[reactants] at equilibrium
Note the units (M-1)
Higher affinity = higher KA
[email protected]
www.t-cellbiology.org/teaching
Affinity
4. Also expressed as dissociation constant: KD
–
–
The inverse of KA
Usually thought of as concentration of A at which
half of B is bound ([B]=[AB]) at equilibrium
Aeq Beq
KD 
ABeq
–
–
Units are M
Higher affinity = lower KD
[email protected]
www.t-cellbiology.org/teaching
Measuring the affinity constant
1. One could simply measure [A],
[B] and [AB] at equilibrium and
calculate KD
DERIVATION
2. In practice this is difficult and
the following approach is used.
and

AB 
KD 
AB
B
(1)
 Btotal   AB
 ABmax  AB (2)
3. Increasing fixed concentrations
By substitution of (2) into(1)
of one molecules A ([A]) are
added to a fixed small amount of and rearranging, we get
its ligand B and you measure the

AABmax 
[ AB] 
amount of bound A (Bound)
A  K D
4. Plot the results and fit the 1:1
or
Langmuir equation to the data to

ABoundmax
Bound 
determine KD and Boundmax
A  K D
[email protected]
www.t-cellbiology.org/teaching
Measuring affinity constant
Bound (arbitrary units)
Bound 
•
•
KD = 19 mM
Boundmax=200
•
•
[A], mM
[email protected]
•
ABoundmax
A  K D
Data are circles
Line is non-linear fit of the
equation performed by a
computer (e.g. Origin, R)
Gives the indicated values
for KD and Boundmax
If the fit is good it
indicates that binding
follows the simple 1:1
model
Difficult to see if fit is poor
in this plot
www.t-cellbiology.org/teaching
Scatchard plot
•
•
Linear for a 1:1 interaction
If curved it indicates wrong
model and possible problem
with the experiment
Most commonly concave up
Usually caused by
experimental error
(often heterogeneity)
Sometimes due to negative
cooperativity
Far less common is to see
concave down
Usually caused by positive
cooperativity
•
•
[email protected]
DERIVAT ION
ABoundmax
Bound 
A  K D
Bound A  Bound K D  ABoundmax
Divide bot hsides by AK D and rearrange,giving
Boundmax
Bound
1

Bound
A
KD
KD
Bound
versus Bound
A
1
slope  
KD
P lotof
Bound/[A]
•
Bound
ve rsusA
A plot of
A
Y int ercept
Boundmax
KD
T herefore
X int ercept
Y int ercept
 Boundmax
 slope
Slope
= -1/Kd
Boundmax
www.t-cellbiology.org/teaching
Measuring 2D Kds
Experimental set-up
Data collection
T cell
bilayer
*
free
**** *
bound
Dustin et al. 1996 JCB 132, 465
[email protected]
www.t-cellbiology.org/teaching
Comparing 2D and 3D affinities
Kd > 2 mM
SLAM
CD4 CD8 rCD2
1000
100
47
rCD2
“barely adequate”
[email protected]
CD28
TCR
hCD2
10
1.1
hCD2
3D Kd (mM)
1
KIR
CTLA-4
0.1
2D Kd (mols/mm2)
strong interaction
www.t-cellbiology.org/teaching
Thermodynamics of binding
1. Binding is favoured if it leads to a net increase
in disorder or entropy.
2. This includes entropy of….
a) the system (interacting molecules and solvent)
• represented as change in entropy or S
b) the environment (everything else)
• as the system releases or absorbs heat it
changes the entropy of the surroundings
• heat release is measure as change in
enthalpy or H
[email protected]
www.t-cellbiology.org/teaching
Gibbs free energy change
1. The change in Gibbs free energy (G) is a measure of
the net change in universal entropy - i.e. the extent
to which binding is favoured.
G = H -T S
If G < 0 then binding is favoured.
2. G depends on concentration. At equilibrium G = 0
3. Go is the standard state G which assumes all
components are at the standard state concentration
of 1 M (mol.L-1)
4. It can be calculated from the affinity constant
Go = RTlnKD
R = Gas Constant (2 cal.mol-1.K-1) T = absolute temp.
in Kelvin (oC+273.18) and KD is expressed in units M
[email protected]
www.t-cellbiology.org/teaching
Origins of enthalpy and entropy changes
Go = H -TSo
1.
Change in enthalpy (H)
a) Release of heat (H <0) favours binding
b) This happens when bonds are formed
•
c)
2.
e.g. hydrogen bonds, salt bridges, van der Waals contacts
However bonds are also broken upon binding
•
•
displacement of water and ions (always)
conformational change (sometimes)
Change in entropy (TS)
a) Increase in entropy (S >0) favours binding
b) Protein/protein interactions leads to decrease in entropy
•
•
c)
Stabilise conformation at the binding interface
Decreased rotation/translation of proteins
However displacement of water from the binding interface
leads to an increase in entropy (the hydrophobic effect)
[email protected]
www.t-cellbiology.org/teaching
The key role of water
1. Water is present at very high concentrations (55 M) and
interacts with protein surfaces
2. Thus, many water bonds need to be broken, which has an
unfavourable enthalpic effect
3. Water can also act as glue filling in gaps between surfaces that
lack surface shape complementarity
binding
Hydrophilic patch
in binding site
4. Water is believed to form an organised shell over hydrophobic
surfaces. Ejection of water from these surfaces into free solution
has favourable entropic effect. This is the ‘hydrophobic effect’.
5. Note that there is a weak unfavourable enthalpic effect as well
since the water molecules in the shell interact weakly
binding
Hydrophobic patch
[email protected]
www.t-cellbiology.org/teaching
TCR and antibody binding have distinct
thermodynamic properties
(Data from Willcox et al 1999 and Stites 1997)
-25
Protein/
protein
(30)
Antibody/
protein
(11)
TCR/
pep-MHC
(2)
-20
kcal/Mol
-15
-10
Favourable
-5
0
5
H
10
G
15
-TS
[email protected]
Unfavourable
o
o
www.t-cellbiology.org/teaching
Changes in conformation at a T cell
receptor/peptide-MHC interface
Garcia et al (1998)
TCR
pMHC
[email protected]
www.t-cellbiology.org/teaching
Heat capacity change (C)
1. H and TS usually vary with temperature
2. The extent of this variation is given by C
3. This is a consequence of changes in water
with temperature
Low temp – binding disrupts water ‘shell’ with unfavourable effects
on H and favorable effects on S
binding
Hydophobic patch
High temp – water shell already ‘melted’ so both effects are lost
binding
Hydophobic patch
[email protected]
www.t-cellbiology.org/teaching
Why measure heat capacity change?
1. S includes
contributions from
changes in solvent
entropy
(hydrophobic
effect) and protein
entropy
2.The heat capacity
change can be used
to estimate solvent
entropy change,
enabling estimation
of the protein
entropy change.
[email protected]
DETAILED EXPLANATION
(Spolar and Record (1994) Science 263:777)
• C correlates with non-polar surface
area that is buried by binding (Anp) =>
• C can be used to estimate the
contribution of the hydrophobic effect
(She) to total entropy change (STotal)
• The change in rotational and
translational entropy (Srt) can be
calculated, and is same for all proteinprotein interactions.
• Thus Sother can be calculated since
STotal = She + Srot/trans + Sother
• Main contribution to Sother is thought
to be reductions in conformational
flexibility accompanying binding i.e. it’s
a measure of amount of conf. change
www.t-cellbiology.org/teaching
Measuring thermodynamic parameters
1. S can’t be measured directly
2. G and H are measured and
G = H -TS
3. H can be measured in 2 ways
a)calorimetry (see later) or
b)van’t Hoff analysis
1.
2.
3.
4.
5.
Van’t Hoff analysis
G is measured over a range of
temperature and plotted
The non-linear van’t Hoff
equation* is fitted to the data to
determine H, S and C
* Non  line arvan't Hoff e quation
The slope represents H
This plot is curved for
T 
G  H To  TSTo  C (T  To)  TC ln 
macromolecular interactions as
 To 
H varies with temperature
whe reTo is an abitraryre fe re ncete mpe ratur
e
The curvature represents C
[email protected]
www.t-cellbiology.org/teaching
Kinetics
Since biological systems are not at equilibrium,
the rate of binding and dissociation is critical
For a simple 1:1 interaction (A + B  AB)…
1. Rate of dissociation
a) d[AB]/dt = k diss[AB]
b)where kdiss is the dissociation rate constant (koff)
2. Rate of association
a) d[AB]/dt = kass[A][B]
b)where kass is the association rate constant (kon)
3. At equilibrium the rate of association must
equal the rate of dissociation
kdiss[AB] = kass[A][B]
=>
kdiss/kass = [A][B]/[AB] = KD
[email protected]
www.t-cellbiology.org/teaching
Dissociation
• Any reaction of the form d[AB]/dt ∞ [AB]
will be exponential so
a) i.e. [AB]t = [AB]oe-kdisst
b) kdiss determined directly by curve fitting
• The half life (t1/2) can be calculate as
follows:
Since at t = t1/2
[AB]t/[AB]o=0.5=e-kdisst1/2
It follows that
-kdisst1/2= ln(0.5) = 0.693
Thus t1/2 = 0.693/koff
[email protected]
Dissociation of
A from B
Symbols are data,
lines are fitted
curves
t1/2
www.t-cellbiology.org/teaching
Association
•
In most experimental system it is
impossible to follow association alone in
the absence of simultaneous dissociation
•
For the simple interaction A + B  AB
d[AB]/dt = kass[A][B] – kdiss[AB]
It follows that [AB]t=[AB]final (1-e-kobst)
where kobs = kass[A]+koff
Thus one needs to know koff and [A] as well
as measuring [AB] to calculate the kon
[email protected]
www.t-cellbiology.org/teaching
Determination of binding kinetics
JM22z (mM)
Binding (RU)
300
Dissociation phase (kdiss)
15
kobs = [A]kass+ kdiss
200
7.5
100
kass 42000 M-1.s-1
Association phase (kobs)
kdiss 0.5 s-1
1.75
0
KD 1.2 x 10-5 M
20
0
-20
Residuals plot (difference
between data and fitted
curve)
0
5
[email protected]
10
15
www.t-cellbiology.org/teaching
Factors affecting kinetics
1. The association rate constant does not vary that much
a) Association requires two proteins to collide in the correct
orientation and in the correct conformation
b) Depends on diffusion so will be similar for most proteins
c) The basic rate is about 105 M-1.s-1
d) Can be accelerated by long range electrostatic forces
• Increased rate of collision
• Steer binding sites into correct orientation
• E.g. barnase/barnstar interaction
2. The dissociation rate constant varies considerably and
is responsible for most variation in affinity constants
a) It is determined by the number and strength of bonds in
the contact interface
b) Depends on size of interface and the degree of surfaceshape and electrostatic complementarity
[email protected]
www.t-cellbiology.org/teaching
Summary of average affinity and kinetic
constants for biological interactions
Interaction
kon (M-1s-1)
koff (s-1)
KD (M)
Cell-cell
recognition
molecules
105
1-10
10-5 to
10-4
Antibody/antigen
105
10-3
10-8
Cytokine/receptor
105
10-4
10-9
Enzyme/inhibitor
108
10-3
10-11
(eg barnase/barnstar)
[email protected]
www.t-cellbiology.org/teaching
Transition state theory
1. Proposes that when two
molecules interact they
traverse a mountain-like
energy ‘landscape’.
2. Highest energy point is
the putative transition
state (AB‡).
3. The height of this point
determines rate constant
4. Transition state then
‘relaxes’ into the final
complex (AB).
[email protected]
Potential energy (kcal.Mol-1)
(or activation complex theory)
AB‡
‡Gass
‡Gdiss
 G
A+B
‡
diss
G
AB
Binding coordinate
www.t-cellbiology.org/teaching
Transition state theory
•Assume reactants are in equilibrium with transition state
=> it is possible to apply thermodynamic principles
A + B↔AB‡ => ‡G = ‡H-T‡S
‡G : calculate from kass
‡H : determine by measured temperature dependence of kass
=> T‡S can be calculated
•Analyse in the same way as described previously
•Application: to study the structure of the transition state
complex (i.e. how binding occurs)
e.g. by examining the effect of mutations on these values
one can determine which residues in the binding site
interact in the transition state complex
•The same approach can be used for dissociation
AB↔AB‡
[email protected]
www.t-cellbiology.org/teaching
The importance of bond length
• Breaking a bond analogous to pulling a cart up a hill
height of hill = work required = bond energy
slope of hill = force required = mechanical strength
• Force = work/distance
Mechanical strength = bond energy/bond length
slope = mechanical strength
long bond
length
bond energy
short bond
length
bond length
[email protected]
www.t-cellbiology.org/teaching
The mechanical strength of a bond is only
indirectly related to affinity
Mechanical strength = bond energy/bond length =>
1. The bond energy is likely to be more closely
related to the enthalpy change (ΔH) than the
affinity (ΔG) since ΔH measures net number
of bonds broken
2. Since some bonds reform during dissociation
mechanical strength is related to the number
of bonds broken to reach the transition state
of dissociation
3. This is given by the activation enthalpy of
dissociation or Δ‡Hdiss
[email protected]
www.t-cellbiology.org/teaching
The mechanical strength of a bond is only
indirectly related to affinity
IMPLICATIONS:
• Mechanical strength should be measured
directly if possible.
• This can be done using techniques such
atomic force microscopy
[email protected]
www.t-cellbiology.org/teaching
2. How can we measure them?
SPR
(BIAcore)
AUC
Surface
Plasmon
Resonance
Analytical
Ultracentrifugation
[email protected]
ITC
(microcalorimetry)
Isothermal
Calorimetry
www.t-cellbiology.org/teaching
Measuring key properties of
protein-protein interactions
Property
Affinity
AUC
+
BIAcore Calorimetry
++
+
Enthalpy
no
+
++
Entropy
no
+
++
Heat capacity
no
+
++
Kinetics
no
++
no
Stochiometry
+
+
++
Size & Shape
+
no
no
[email protected]
www.t-cellbiology.org/teaching
3. Comparison of interactions
‘in solution’ vs. at the cell surface
•For most interactions of soluble proteins,
want strong, specific interactions
•But what about on the cell surface?
• How can you get TRANSIENT adhesion?
(especially given number of molecules on a cell!)
•PROBLEM: how to reduce affinity
without reducing specificity?
• Reducing area of interaction will reduce both
[email protected]
www.t-cellbiology.org/teaching
The range of affinities seen for
transient interactions at the cell surface
Selectins
Inactive LFA-1
fully active LFA-1
fully active Mac-1
SLAM
CD28
CD8
TCR
CD4
rCD2 hCD2 KIR
1000
100
10
CTLA-4 Ab:Ag
1
0.1
3D Kd (mM)
[email protected]
www.t-cellbiology.org/teaching
Best studied example:
CD2:ligand interactions
• CD2: cell adhesion molecule
• enhances antigen recognition by T-cells
LFA-3
CD48
CD2
human: Kd = 15 mM
[email protected]
CD2
2B4
murine: Kd ~ 65 mM
www.t-cellbiology.org/teaching
Clues from the Rat sCD2 structure
T86
R87
K43
E41
E33
structure elec. potential
[email protected]
mutations
www.t-cellbiology.org/teaching
Charged residues & binding specificity
CD48:
R31 to ?
E44 to ?
CD2:
E41A
K43A
[email protected]
www.t-cellbiology.org/teaching
A new protein recognition paradigm?
mAb:lysozyme
Kd = 1 nM
CD2:LFA-3
Kd = 15 mm
J Bloggs
4351 6683 4798 1211
[email protected]
www.t-cellbiology.org/teaching
The value of electrostatic interactions
•Specificity is generated by electrostatic rather
than surface/shape complementarity
•BUT this does not result in high affinity
because salt bridges are approximately energy
neutral.
•This is because binding energy from these
interactions is counteracted by the need to
disrupt the interactions between the charged
residues and the solvent (i.e. water) before
binding.
[email protected]
www.t-cellbiology.org/teaching
The value of electrostatic interactions
hydration of the charged
residues in the unliganded
receptor
[email protected]
exclusion of water from
the interface
www.t-cellbiology.org/teaching
Electrostatic complementarity predicts
CD2:LFA-3 complex topology
Oxford prediction
Ikemizu et al.
[email protected]
Harvard complex
Wang et al.
www.t-cellbiology.org/teaching
But it’s not all like that...
B7-1:CTLA-4 ~ High surface complementarity
Complementarity (S) = 0.75-0.76
(antibodies = 0.64-0.68; CD2 = 0.58; TCR = 0.45)
[email protected]
www.t-cellbiology.org/teaching
kcal/mole of injectant
Thermodynamics of sB7-1/CTLA-4:
some compensation?
0
-4
H = -11.6
G = -8.9
TS = -2.7
kcal/mol-1
-8
-12
0
[email protected]
1
2
molar ratio
3
4
www.t-cellbiology.org/teaching
Interactions are NOT uniform!
•All transient cell surface interactions are
relatively weak but mechanism varies.
•Range of affinities/avidities is still large
•Precise affinity/avidity and structural
mechanisms used to determine it (and
specificity) depend on FUNCTION.
[email protected]
www.t-cellbiology.org/teaching
Another reminder: TCR entropy barrier –
recognitionof TCR?
linked TCR
to function/nature
Willcox et al. (1999) Immunity 10:357
[email protected]
www.t-cellbiology.org/teaching
Interactions are NOT uniform!
•All transient cell surface interactions are
relatively weak but mechanism varies.
•Range of affinities/avidities is still large
•Precise affinity/avidity and structural
mechanisms used to determine it (and
specificity) depend on FUNCTION.
•Oligomerisation state and valency are
key factors in determining avidity
(remember full e.g. at start)
[email protected]
www.t-cellbiology.org/teaching
Summary of the nature of
recognition at the cell surface
• Interactions between two cell surface proteins are
generally weak but remain highly specific
• A variety of structural mechanisms underlie this
e.g. Clustered charged residues allow weak specific
recognition by CD2 and its ligands
• In general, it is more important that these interactions
are weak than how this is achieved – but there are
other functional constraints.
• Co-operative, avidity-driven interactions can profoundly
alter the strength of signalling
• Hierarchical affinities may determine the sequence of
events in key processes such as T-cell activation
[email protected]
www.t-cellbiology.org/teaching
What we’ve covered
1. What binding properties are important?
a)
b)
c)
d)
e)
Affinity
Thermodynamics
Kinetics
Stoichiometry, avidity etc.
Mechanical binding strength
2. How might we measure them?
a) SPR (BIAcore)
b) ITC / microcalorimetry
c) AUC
3. Comparison of interactions
‘in solution’ vs. at the cell surface
[email protected]
www.t-cellbiology.org/teaching