• 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