Isothermal titration calorimetry (ITC)

Peter.gimeson@malvern.com

• Native molecules in solution (biological relevance)

• Very sensitive to accomodate range of affinities

Why microcalorimetry?

Label-free Broad dynamic range Ease-of-use

• Direct measurement of heat change (ITC)

• Direct measurement of melting transition temperature to predict thermal stability (DSC)

• No labeling or immobilzationnecessary

• No assay development

• Wide range of solvent/buffer conditions

Information rich

• All binding parameters (affinity, stochiometry, enthalphy and entropy) in a single ITC experiment

0 1 2

-1 2

-9

-6

-3

0

X t/M t

ND

H, k

cal/m

ole

of in

ject

ant

Microcalorimetry in life sciences

› Two major techniques

MicroCal iTC200

MicroCal Auto-iTC200

MicroCal VP-Capillary DSCMicroCal VP-ITC

MicroCal™ VP-DSC

Differential scanning calorimetry (DSC) Isothermal titration calorimetry (ITC)

MicroCal PEAQTM ITC

….MicroCal PEAQ ITC Automated

With isothermal titration calorimetry you can…

› Get quick KDs for secondary screening/hit validation

› Measure target activity

› Confirm drug binding to target

› Use thermodynamics to guide lead optimization

› Characterize mechanism of action

› Validate IC50 and EC50 values

› Measure enzyme kinetics

› CMC

How do they work?

Reference Calibration Heater

Cell Main HeaterSample Calibration Heater

DPT

SampleThe DP is a measured power differential between the reference and sample cells to maintain a zerotemperature between the cells

T~0DP = Differential power∆T = Temperature difference

Reference

Performing an ITC assay

› “Ligand” in syringe

› “Macromolecule” in sample cell

Reference cell Sample cell

Syringe

S R

Reference power

Reference power supplied to the reference cell

1

Reference power

Reference power supplied to the reference cell activates feedback to sample cell

12

Reference power

How much energy needs to be applied to the sample cell in order to get zero output from peltier element = same temperature in reference and sample cell

3

The signal we see, DP is this energy in uCal/sec

= 0

Reference power

An exothermic reaction in the sample cell will cause an temperature offset, activating the peltier sensor. The feedback is regulated accordingly until zero output.

4 = 0

Reference power

After equilibrium have been reached, the system relaxes to reference power level and system is ready for next injection

5 = 0

Basics of ITC experiment

Integration of heats are used to extract affinity (KD), stoichiometry (N) and binding enthalpy (H) using appropriate binding model

Universal technique based on heat detection

-4

-2

0

0 0.5 1.0 1.5 2.0

H

N

KD

kcal

mol

-1of

inje

ctan

t

Molar ratio

µcal

s-1

Time ->

The energetics

-14

-12

-10

-8

-6

-4

-2

0

kcal

/mol

e of

inje

ctan

t

0 1 2 3 4

›The same affinity and stoichiometry but different enthalpy (heat)›This tells us we have different binding mechanisms

Ligand A into compound X

Ligand B into compound X

Molar ratio

The energetics

G = RT ln KD

G = H –TS

∆G = Gibbs free energy∆ H = Enthalpy∆ S = EntropyR = Gas constant = 1.985 cal K-1 mol-1

T = Temperature in Kelvin = 273.15 + t 0CKD = Affinity

ΔH, enthalpy is indication of changes in hydrogen and van der Waals bonding

-TΔS, entropy is indication of changes in hydrophobic interaction and/orcomformational changes

N, stoichiometry indicates the ratio of ligand-to-macromolecule binding

The energetics

›Elucidation of binding mechanisms:

Primary Enthalpic Contributions• Hydrogen bonding and van der

Waals interactions

Primary Entropic Contributions• Hydrophobic effect-water

release (favorable)• Conformational changes and

reduction in degrees of freedom (unfavorable)

KD

Macromolecule

Waters, ions, protons

Ligand

Freire (2007) A new era for microcalorimetry in drug development. Eur. Pharm. Rev. 5, 73-78

Affinity is just part of the pictureAll three interactions have the same binding energy (∆G)

-20

-15

-10

-5

0

5

10

kcal

/mol

e ∆G∆H -T∆S

Favorable

Unfavorable

A. Good hydrogen bonding with unfavorable conformational change

B. Binding dominated by hydrophobic interaction

C. Favorable hydrogen and hydrophobic interaction

∆G

DG = DH –TDS

Molar Ratio

Kca

l/mol

inje

ctan

t

1.0 1.5 2.00.50.0

0

-2

-4

-6

-8

Assess protein quality

Clearly distinguish between genuine SAR and batch to batch variations in protein quality

‘Fully active’

50%‘Fully active’

Different binding

mechanism

Measuring bioactivity with ITC:affinity and stoichiometry

Assessment of protein quality byMicroCal™ iTC200 system

›100% of Batch 1 protein activebased on stoichiometry

›23% of Batch 2 protein active based on stoichiometry

Presented by L.Gao (Hoffmann-La Roche), poster at SBS 2009

Peptide binding to proteinBatch #1 Peptide binding to protein Batch #2

Protein-ligand interactionsTobromycin binding to aminoglycoside nucleotidyltransferase (2”) in the absence and presence of cofactor

WithoutMgAMPCPP

WithMgAMPCPP

KD = 0.64 MH = -18.2 kcal/moleS = -34 cal/mole/oK

KD = 0.21 MH = -12.6 kcal/moleS = -12.3 cal/mole/oK

Wright and Serpersu, Biochemistry 44, 11581-11591 (2005) ∆S = binding entropy

The cofactor has little impact on the affinity, larger impact on enthalpy and entropy

C-terminal domain of nuclear RNA auxiliary factor (U2AF65-UHM) binding to spliceosomal component mutant SF3b155-W7 (shown) or wild-type SF3b155

Protein-protein interactions

Thickman et al, J. Mol. Biol. 356, 664-683 (2006)

SF3b155-W7 Wild-type SF3b155

KD (M) 2.50 2.83

G (kcal/mol) -7.8 -7.7

H (kcal/mol) -14.9 -9.4

S (cal/mole/oK) -23.4 -5.6

RNA = Ribonucleic acids∆G = Gibbs free energy

Mutant has little impact on affinity but does impact the interaction

30 uM bi-valent Ab in syringe, 4 uM antigen in cell

Antibody – Antigen interactions

Protein-DNA interactions

›Energetics of telomere complex assembly›ITC results confirmed complex formation

DNA binding to subunit -DNA complex bindingto subunit

Buczek and Horvath JBC 281 40124-40134 (2006)

Protein-metal ion

›ITC shows differentialbinding of Mn(II) ions toWT T5 5’ nuclease

-2

0

2

4-10 0 10 20 30 40 50 60 70 80 90 100

Time (min)

µcal

/sec

-0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

0

2

Molar Ratio

kcal

/mol

eKa = 3.0 x 105 M-1

H = -0.59 kcal mol-1

Ka = 1.0 x 104 M-1

H = +1.6 kcal mol-1

Feng, et al, Nat. Struct. Mol. Biol. 11, 450-456 (2004)

0.0 0.5 1.0

-26.3-23.9-21.5-19.1-16.7-14.3-11.9-9.6-7.2-4.8-2.40.02.4

-0.14

-0.10

-0.05

0.00

0 10 20 30 40 50 60

Time (min)

µcal

/sec

50 uM protein in syringe9 uM LMW ligand in cell

Data: D139Gal3zz_NDHModel: TwoSitesChi 2 = 1.860E5N1 2.35 ±0.00832 SitesK1 8.18E9 ±3.88E9 M-1

H1 -8671 ±53.4 cal/molS1 16.6 cal/mol/degN2 6.39 ±0.242 SitesK2 5.41E6 ±2.61E6 M-1

H2 -945.6 ±50.9 cal/molS2 27.7 cal/mol/deg

Molar Ratio

kcal

mol

-1 o

f inj

ecta

nt

High resolution binding data

› Multiple substrate injections Low enzyme concentration Steady state conditions

› Continuous assay Higher enzyme concentration Single injection of substrate

Enzyme Kinetics

Look out for...

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

KP156Gal3e_NDH

Molar Ratio

kcal

mol

-1 o

f inj

ecta

nt

!

C = 10-100 Great

C = 5-500 Good

C = 1-5 and 500-1000 OK

C = < 1 and > 1000 competition ITC

0.0 0.5 1.0 1.5 2.0

-16

-14

-12

-10

-8

-6

-4

-2

0

Molar Ratiokc

al/m

ole

of in

ject

ant

C = 0.05

C = 0.5

C = 5

C = 50

C = 500

C = [Protein]/KD

How much sample is required?The experiment

-0.4

-0.2

0

0 4 8 12 16

[Protein]/KD < 1N fixed

Fitted: KD, H -4

-2

0

0 0.5 1.0 1.5 2.0

10< [Protein]/KD <500

Fitted: N, KD, H

kcal

mol

-1of

inje

ctan

t

-4

-2

0

0 0.5 1.0 1.5 2.0

[Protein]/KD >> 1000Fitted: N, H

Molar ratio

Low c High c

0 1 10 500 1000

BAD GOOD OPTIMAL GOOD BAD

How much sample is required?The experiment

DialyzeSample preparation

› The cell and syringe buffers must be carefully matched. This is best accomplished by dialyzing both the macromolecule and the ligand in the same buffer.

› If the ligand is too small for dialysis then dialyze the macromolecule and then dissolve the ligand in the dialyze buffer

Poor sample preparation leads to poor dataSample preparation

›The data shown here shows before and after dialysis

›The large peaks were dueto differences in the NaClconcentration betweenbuffers

0 20 40 60 80 100 120 140 160 180-0.5

0.0

0.5

1.0

1.5

2.0

2.5

without dialysis

with dialysis

Time (min)µc

al/s

ec

With dialysis

Without dialysis

MicroCal PEAQ ITC

MicroCal PEAQ ITC

MicroCal PEAQ ITC MicroCal PEAQ ITC Automated

MicroCal PEAQ ITC

› The latest and 5th generation ITC from MicroCal Guided workflows, experimental design software and

fully integrated wash module for consistently high quality data

Robust and rapid data analysis Improved signal to noise

Experiment design and simulation software

› Aids experiment optimization saving time and sample.

Input: known parameters-if any

Output: Recommended concentrations

Output:Predicted binding isotherm

Output: Advice for experimental set up

Experiment design and simulation software

› Qualifies user sample concentration suggestions and provides warnings if necessary

Realistic scatter to represent real low heat data

Warning: Heat signal too low

Experiment design and simulation software

› Complex models- multi site and competition experiments supported

Slide bar simulation tool to help designbest experiments for testing complex models

Experimental set up

› Guided workflows and in-built videos for step by step tutorials to help infrequent users through the process

Ideal in multi-user environment

Maintenance alerts

› Links to in-built videos to demonstrate how to perform straightforward maintenance tasks

Consistent, high quality data

Click on alert for guidance

Fully integrated wash module

› Choice of cleaning methods available- including a high temperature soak with detergent for very ‘sticky’ samples

New data analysis software

› Automated data qualification› Robust automated data

analysis.› Robust batch analysis of

multiple data sets› Multiple inbuilt tools to

graphically visualize the data

› New features to support common applications such as SAR

Automated data qualification

› Data is automatically categorized as 1/showing binding 2/ showing no binding or 3/ data of questionable quality

Binding

Check data

No Binding

Robust, automated data analysis

› Robust, automated data analysis

Binding

No binding

Check data

Robust baseline algorithm

Automatic control subtraction

50 experiments analyzed in under 3 seconds

Multiple data visualization tools

› Easy to compare data sets using graphical display software.

Multiple data visualization tools

› Automatically generates complete results table

Multiple data visualization tools

› Automatically generates multiple ‘Final Figure’ plots with raw and analyzed data

Multiple data visualization tools

› Automatically generates ‘signature’ plots

Multi binding site and hit validation

› High sensitivity allows for the analysis of complex binding interactions

› High quality data needed to resolve 2 transitions such as 2 site and enantiomeric interactions

Raw and normalized heat plots for the titrations of the 1:1 mixture of EZA and FUR into BCAII. The titrations were carried out at 160 µM total ligand concentration and 10 µM concentration of protein in the cell . These represent the type of data seen in hit validation experiments when the compound is a racemate

Summary

› Best signal to noise of any ITC on the market› Robust HW/SW with focus on reproducibility and

multi user environment› Guided workflows and fully integrated wash

module for consistent, high quality data› Robust automated batch analysis for instant, non

subjective data analysis

MicroCal PEAQ ITC

Thank you