Studying protein-protein interactions

Ed Evans, T-cell biology group edward.evans@ndm.ox.ac.uk

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Studying Protein-Protein Interactions

A. INDIRECT (looking for functional association) 1. Correlated mRNA Expression 2. Computational Approaches 3. Phylogenetic Profiling 4. Synthetic Lethality B. QUALITATIVE 1. The Two-Hybrid Method 2. Mass Spectrometry of Affinity-Purified Complexes 3. FRET & BRET C. QUANTITATIVE 1. SPR (BIAcore) 2. AUC 3. Calorimetry edward.evans@ndm.ox.ac.uk

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Indirect detection of interactions

(looking for implied functional association NOT direct interaction) edward.evans@ndm.ox.ac.uk

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A. 1. Correlated mRNA expression

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A. 2. Computational approaches

e.g. “Rosetta Stone” edward.evans@ndm.ox.ac.uk

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A. 2. Computational approaches

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A. 3. Phylogenetic Profiling

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A. 4. Synthetic Lethality

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Qualitative detection of protein-protein interactions

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B. 1. The Two-Hybrid Method

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B. 2. Mass Spectrometry of Affinity Purified Complexes

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Basic Workflow

•Immunoaffinity •TAP tagging •2D gel •Formaldehyde crosslinking •etc…..

Gel MS compatible Silver stain, SYPRO stain Coomassie stain >100 fmol protein Specific Protease

e.g.

trypsin LC MSMS PROTEIN IDENTIFICATION Q-ToF Micro Mass Spectrometer – LC MSMS Protein Digest Quadrupole Peptides Time-of-flight mass spectrometer Peptide fragments CID Data acquisition Nano HPLC system edward.evans@ndm.ox.ac.uk

Nanospray Ion source Peptide sequence www.t-cellbiology.org/teaching

“Mass-fingerprint” Indentification

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Cross-linking the interaction

Non covalent protein complex Thiol cleavable cross-linker Covalently cross-linked complex Digest with Protease MALDI MS Non reduced Thiol reagent MALDI MS Reduced

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Differential peptide mapping

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Summary of current effort in yeast

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...and the bad news

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=> BE WARNED!

These techniques (along with e.g. Co-immuniprecipitation) give lots of false positives edward.evans@ndm.ox.ac.uk

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B. 3. a. FRET

Förster (Fluorescence) Resonance Energy Transfer (FRET)

In this strategy, excitation of GFP will result in emission from a nearby protein such as blue fluorescent protein (BFP) if it is physically close enough. The best FRET pairs are actually the cyan and yellow mutants of GFP, referred to as CFP and YFP. edward.evans@ndm.ox.ac.uk

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Power of FRET

1. Probe macromolecular interactions Interaction assumed upon fluorescence decay 2. Study kinetics of association / dissociation between macromolecules 3. Estimation of distances (?) 4. In vitro OR on live cells 5. Single molecule studies edward.evans@ndm.ox.ac.uk

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FRET

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Live cell FRET imaging

Does CD4 specifically associate with the TCR/CD3 complex on triggering?

Non-specific peptide Specific peptide * marks contacts between cells. High FRET signal between CD4 and CD3 when correct antigen is present but not with non-specific antigen.

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B. 3. b. BRET: Bioluminescence Resonance Energy Transfer DeepBlueC

hf 1 hf 2

GFP 2 Luciferase >10nm edward.evans@ndm.ox.ac.uk

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BRET vs FRET

• BRET analysis can be achieved at physiological levels of protein expression • No problems with photobleaching or photoconversion as seen in FRET techinques (no laser stimulation) • Both methods involve the same physical processes and so can be analysed in a similar manner • BRET cannot be used in microscopy-based techniques such as FRAP or FLIP, or FACS-based analysis edward.evans@ndm.ox.ac.uk

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Construction of Fusion Proteins

• The gene of interest is fused to both luciferase (donor) and GFP (acceptor) in two separate vectors • A positive control is used to determine maximal BRET edward.evans@ndm.ox.ac.uk

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e.g. B7-1 BRET

B7-1luc B7-1YFP substrate hu2 (530 nm) hu1 (470 nm) B7-1luc:B7-1YFP CTLA-4luc:CTLA-4YFP edward.evans@ndm.ox.ac.uk

B7-1luc B7-1luc:CTLA-4YFP www.t-cellbiology.org/teaching

e.g. BRET on B7 family

Energy transfer can occur solely by random interactions

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Comparison to T cell surface molecules with known oligomerisation status!

Strong dimers Weak dimer Monomers edward.evans@ndm.ox.ac.uk

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Ligand binding causes specific increase in dimerisation Specific ligand engagement can be observed when receptor is presented in solution or cell-surface bound 0.5

0.4

0.3

0.2

0.1

0.0

0 1 2 GFP / Rluc 3 hCD80 - CTLA-4 hCD80 + CTLA-4 hCD86 - CTLA-4 hCD86 + CTLA-4 4 5 edward.evans@ndm.ox.ac.uk

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Measure Quantitative Properties SPR (BIAcore)

Surface Plasmon Resonance

AUC

Analytical Ultracentrifugation edward.evans@ndm.ox.ac.uk

ITC

(microcalorimetry) Isothermal Calorimetry www.t-cellbiology.org/teaching

Measuring key properties of protein-protein interactions Property Affinity Enthalpy AUC + no Entropy no Heat capacity no Kinetics Stochiometry Size & Shape no + + BIAcore Calorimetry ++ + + ++ + + ++ + no ++ ++ no ++ no edward.evans@ndm.ox.ac.uk

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C. 1. SPR / BIAcore

(Surface Plasmon Resonance) edward.evans@ndm.ox.ac.uk

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Advantages of SPR on the BIAcore 1. No labelling is necessary 2. Real-time analysis allows equilibrium binding levels to be measured even with extremely rapid off-rate.

3. Small volumes allow efficient use of protein. Important when very high concentrations are required.

4. No wash steps => weak interactions OK 5. All types of binding data obtained – including kinetics as its real-time.

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Principle of Surface Plasmon Resonance Angle of ‘dip’ affected by: 1) Wavelength of light 2) Temperature 3) Refractive index n 2 edward.evans@ndm.ox.ac.uk

Dip in light intensity www.t-cellbiology.org/teaching

Surface Plasmon Resonance in the BIAcore edward.evans@ndm.ox.ac.uk

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Immobilisation

2 Main options: • Direct: Covalently bind your molecule to the chip • Indirect: First immobilise something that binds your molecule with high affinity e.g. streptavidin / antibodies

Direct: Indirect:

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Sensorgram for ligand binding

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“Specific” Binding

• Each chip has four ‘flow-cells’ • Immobilise different molecules in each flow-cell • Must have a ‘control’ flowcell • ‘Specific binding’ is the response in flow-cell of interest minus response in the control flowcell Specific response in red flowcell Measured response edward.evans@ndm.ox.ac.uk

Response in control / empty flowcell due to viscosity of protein solution injected – therefore ‘control’ response DOES increase with concentration (this is NOT binding!!) Is it specific?

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Equilibrium Binding Analysis

Binding curve can be fitted with a Langmuir binding isotherm (assuming a 1:1 binding with a single affinity)

Bound

 [

R Max

[

A

] 

A K d

] edward.evans@ndm.ox.ac.uk

Scatchard plot: rearrangement of binding isotherm to give a linear plot. Not so good for calculating Kd, as gives undue weight to least reliable points (low concentration)

Bound

[

A

] 

R Max K d



Bound K d

Plot Bound/Free against Bound Gradient = 1/K d www.t-cellbiology.org/teaching

Kinetics

Harder Case: 2B4 binding CD48 edward.evans@ndm.ox.ac.uk

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Potential pitfalls

• Protein Problems: Aggregates (common) Concentration errors Artefacts of construct (eg Fc linked) • Importance of controls: Bulk refractive index issues Control analyte Different levels of immobilisation • Mass Transport: • Rebinding: Use both orientations (if pos.) Rate of binding limited by rate of injection: k on will be underestimated Analyte rebinds before leaving chip k off will be underestimated Last two can be spotted if measured k on and k off vary with immobilisation level (hence importance of controls) edward.evans@ndm.ox.ac.uk

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Less common applications

1. Temperature dependence of binding van’t Hoff analysis: edward.evans@ndm.ox.ac.uk



G

 

RT

ln(

K a

)  

H



T



S

ln(

K a

)   

R H

 1  

T

 

S R

Gradient Intercept www.t-cellbiology.org/teaching

Less common applications

1. Temperature dependence of binding Non-linear van’t Hoff analysis: 

G

 

H vH

,

T

0 

T



S vH

,

T

0  

C p

,

vH

(

T



T

0 ) 

T



C p

,

vH

ln   

T T

0    edward.evans@ndm.ox.ac.uk

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Less common applications

2. Combination with mutagenesis Binding of CD2 by CD48 mutants at 25 °C (WT K d = 40 m M) Immobilised Ligand WT CD48-CD4 L35A Q30R R87A Q30K Q40R E55R Immobilisation Level (RU) Replicate 2000 1950 1900 1950 2000 2000 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 rsCD2 concentration range ( m M) 0.6-320 0.8-407 0.7-348 10-320 0.8-407 0.7-348 Q40K 0.8-407 0.7-348 1.2-320 0.8-408 0.8-409 20-320 0.8-408 1.6-409 1.2-320 0.8-408 0.8-409

K d (

m

M)

Mean K d ( m M) s.e.m.

( m M)

30 49 46 1200* 1700* 1500*

41.7

1455* 5.9

144.4

33500*

Do not affect binding

3760*

Not tested

37

34.0

2.5

36 411 431 474 13 18 19

438.7

16.7

18.6

1.9

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Less common applications

3. Estimation of valency edward.evans@ndm.ox.ac.uk

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Less common applications

4. Screening Newer BIAcore machines are capable of high throughput injection. With target immobilised, many potential partners / drugs can be tested for binding.

5. Identification of unknown ligands Mixtures e.g. cell lysates, tcs, food samples etc. can be injected over a target and bound molecules can then be eluted into tandem mass spectroscopy for identification.

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One last warning: take care

CD48 binding to immobilised CD2 (van der Merwe et al.) (straight out of the freezer) edward.evans@ndm.ox.ac.uk

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2. AUC

(Analytical Ultracentrifugation) edward.evans@ndm.ox.ac.uk

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Theory: The Svedberg equation

1. Consider a particle m in a centrifuge tube filled with a liquid.

2. The particle (m) is acted on by three forces: a) F C : the centrifugal force b) F c) F B f : the buoyant force (Archimedes principle) : the frictional force between the particle and the liquid 3. Will reach constant velocity where forces balance: edward.evans@ndm.ox.ac.uk

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Theory: The Svedberg equation

•

Define s, the sedimentation coefficient:

s =

• • •

s is a constant for a given particle/solvent, has units of seconds, but use Svedberg (S) units (10 –13 s).

Cytochrome c has s=1S, ribosome s=70S, composed of 50S and 30S subunits (s does not vary linearly with M r ) Values for most biomolecules between 1 and 10000 S

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Theory: The Svedberg equation

S =

f



RT ND

D = diffusion coefficient, N = Avogadro’s number

s



m

0 (1   )

RT ND



or

RTs



NDm

0 (1   )

M r



RTs D

(1   )

(Because M r = Nm 0 )

  • Therefore can directly determine

M r

in solution by measuring physical properties of the particle (

s

under known experimental conditions (

D

,

T

and and

v

)  ), • c.f. PAGE, chromatography – comparative & non-native edward.evans@ndm.ox.ac.uk

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AUC – analytical ultracentrifugation

•Spin down protein at various concentrations and follow its distribution in the cell by OD.

•Equilibrium Analysis: Spin slowly - centrifugal force and back diffusion reach equilibrium. Distribution depends on average mass. If this increases with concentration then association is occurring and affinity can be estimated.

•Velocity Analysis: Spin fast & follow speed of boundary descent. Depends on mass and shape– can fit multiple distributions to estimate number of species and their properties. Dependence on concentration again gives affinity.

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AUC – analytical ultracentrifugation

• Generally less precise than others.

• Key advantages are: 1. Works well for homomeric association, which is hard to follow with other techniques 2. Estimates size & shape – useful. In its own right and also for quality assessment edward.evans@ndm.ox.ac.uk

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Equilibrium sedimentation

Meniscus Cell bottom 1. Moderate centrifuge speed 2. After sufficient time, an equilibrium is reached between sedimentation & diffusion, resulting in a montonic solute distribution across the cell • Non-linear curve fitting can rigorously determine: – the solution molecular weight – association state – equilibrium constant for complex formation edward.evans@ndm.ox.ac.uk

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Data modeling

1.

A plot of ln(c) vs r 2 should be a straight line with a slope proportional to molecular weight Single ideal homogeneous species edward.evans@ndm.ox.ac.uk

M p (1  ) =

d

ln(c) 2RT

d

r 2 w 2 www.t-cellbiology.org/teaching

Testing for monomorphic protein

10 ºC, 200 mM NaCl 40 ºC, 100 mM NaCl 19K 26K 31K 40K little or no curvature edward.evans@ndm.ox.ac.uk

obvious curvature = variation in mass i.e. unstable protein leading to aggregation www.t-cellbiology.org/teaching

B7-1 : an equilibrium dimer

6 5 4 3 sB7-1 2 0 1.0

2.0

Protein concentration (mg/ml) edward.evans@ndm.ox.ac.uk

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B7-2 and LICOS are monomeric

80 60 40 sB7-2 20 0 0 1 2 3 Concentration (mg/ml) 4 edward.evans@ndm.ox.ac.uk

80 60 40 sLICOS 20 0 0 1 2 3 Concentration (mg/ml) 4 www.t-cellbiology.org/teaching

Velocity sedimentation

• High centrifuge speed • Forms a sharp boundary between solute depleted region (at top) and a region of uniform solute conc n (at bottom) • The concentration gradient (dc/dr) defines the boundary position Non-linear curve fitting can rigorously determine: • number of mass species • molecular weight • shape information for a molecule of known mass edward.evans@ndm.ox.ac.uk

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Velocity sedimentation - data analysis

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g(s*) distribution www.t-cellbiology.org/teaching

The example of SLAM (CD150)

1. Claimed to self-associate with nM K d raising serious problems for models of cell surface protein interactions 2. Equilibrium data can’t be fitted – high concentrations!

3. Velocity data confirmed shape of complex and approximate strength of association edward.evans@ndm.ox.ac.uk

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3. ITC

(Isothermal Titration Calorimetry) edward.evans@ndm.ox.ac.uk

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Isothermal Titration Microcalorimetry: Using the heat of complex formation to report on a binding interaction.

The Basic Experiment:

1. Fill the upper syringe with ligand at high concentrations.

2. Fill the larger lower reservoir with protein at a lower concentration.

3. Titrate small aliquots of ligand into protein.

4. After each addition, the instrument returns the reservoir temperature to the temperature of the control cell and measures the heat required to cause this change.

5. Typically, subtract appropriate blank titrations (ligand into buffer & buffer into protein) to control for heats of dilution.

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Microcalorimetry

1. Two proteins are mixed and the heat release upon binding is measured 2. Provides a direct measure of the  H (whereas van’t Hoff analysis is indirect) 3. Allows more accurate measurement of  C 4. Can also determine  G and => T  S 5. Its disadvantage compared with the BIAcore is that very large amounts of protein are required and no kinetic data are provided edward.evans@ndm.ox.ac.uk

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ITC Data Analysis

Get a plot of heat ( m J or m Cal) / s following each injection, integrate peaks for total heat released and plot against concentration of protein injected – binding isotherm.

c = conc n / K d edward.evans@ndm.ox.ac.uk

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Data Analysis – e.g. of B7-1 & CTLA-4

1. Curve fitting gives values for  H (enthalpy) and  G (Gibbs free energy, related to affinity) – from these one can also calculate  S (entropy).

0 -4 -8 -12 0 edward.evans@ndm.ox.ac.uk

1 2  H = -11.6  G = -8.9 T  S = -2.7

kcal/mol -1 3 4 molar ratio www.t-cellbiology.org/teaching

Calculating heat capacity

1.

 H and  S are not constant with temperature, hence direct measurement by ITC is better than deriving them from binding data across several temperatures (e.g. by SPR) 2. Relationship of  H to temperature can be used to calculate heat capacity change on binding (  C p ) edward.evans@ndm.ox.ac.uk

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Studying Protein-Protein Interactions

A. INDIRECT 1. Correlated mRNA Expression 2. Computational Approaches 3. Phylogenetic Profiling 4. Synthetic Lethality Bulk screening e.g. For database NEED TESTING AFTERWARDS B. QUALITATIVE 1. The Two-Hybrid Method 2. Mass Spectrometry of Affinity-Purified Complexes 3. FRET & BRET C. QUANTITATIVE 1. SPR (BIAcore) 2. AUC 3. Calorimetry When looking for/at a (or a few) specific interactions edward.evans@ndm.ox.ac.uk

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