Isothermal titration calorimetry (ITC) Peter.gimeson@malvern.com
Why microcalorimetry? Label-free Broad dynamic range Information rich Ease-of-use Direct measurement of heat change (ITC) Direct measurement of melting transition temperature to predict thermal stability (DSC) Native molecules in solution (biological relevance) Very sensitive to accomodate range of affinities All binding parameters (affinity, stochiometry, enthalphy and entropy) in a single ITC experiment No labeling or immobilzation necessary No assay development Wide range of solvent/buffer conditions 0 NDH, kcal/mole of injectant -3-6 -9-12 0 1 2 Xt/M t
Microcalorimetry in life sciences Two major techniques Differential scanning calorimetry (DSC) Isothermal titration calorimetry (ITC) MicroCal VP-DSC MicroCal VP-Capillary DSC MicroCal VP-ITC MicroCal itc 200 MicroCal PEAQ TM ITC MicroCal Auto-iTC 200. MicroCal PEAQ ITC Automated
With isothermal titration calorimetry you can Get quick K D s for secondary screening/hit validation Measure target activity Confirm drug binding to target Use thermodynamics to guide lead optimization Characterize mechanism of action Validate IC 50 and EC 50 values Measure enzyme kinetics CMC
How do they work? Sample Reference The DP is a measured power differential between the reference and sample cells to maintain a zero temperature between the cells DP T T~0 Reference Calibration Heater Sample Calibration Heater Cell Main Heater DP = Differential power T = Temperature difference
Performing an ITC assay Syringe Ligand in syringe Macromolecule in sample cell Reference cell Sample cell
S R
Reference power supplied to the reference cell 1 Reference power
Reference power supplied to the reference cell activates feedback to sample cell 2 1 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 = 0 Reference power The signal we see, DP is this energy in ucal/sec
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 Reference power
Basics of ITC experiment Universal technique based on heat detection 0 µcal s -1 kcal mol -1 of injectant -2-4 H N K D Time -> 0 0.5 1.0 1.5 2.0 Molar ratio Integration of heats are used to extract affinity (K D ), stoichiometry (N) and binding enthalpy ( H) using appropriate binding model
The energetics 0 kcal/mole of injectant -2-4 -6-8 Ligand A into compound X The same affinity and stoichiometry but different enthalpy (heat) This tells us we have different binding mechanisms -10-12 Ligand B into compound X -14 0 1 2 3 4 Molar ratio
The energetics G = RT ln K D G = H T S ΔH, enthalpy is indication of changes in hydrogen and van der Waals bonding -TΔS, entropy is indication of changes in hydrophobic interaction and/or comformational changes N, stoichiometry indicates the ratio of ligand-to-macromolecule binding G = Gibbs free energy H = Enthalpy S = Entropy R = Gas constant = 1.985 cal K -1 mol -1 T = Temperature in Kelvin = 273.15 + t 0 C KD = Affinity
The energetics Elucidation of binding mechanisms: Primary Enthalpic Contributions Hydrogen bonding and van der Waals interactions K D Primary Entropic Contributions Hydrophobic effect-water release (favorable) Conformational changes and reduction in degrees of freedom (unfavorable) 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 picture All three interactions have the same binding energy ( G) 10 A. Good hydrogen bonding with unfavorable conformational change B. Binding dominated by hydrophobic interaction C. Favorable hydrogen and hydrophobic interaction DG = DH TDS 5 Unfavorable kcal/mole 0-5 -10-15 G H -T S Favorable -20 G
Measuring bioactivity with ITC: affinity and stoichiometry Kcal/mol injectant 0-2 -4-6 50% Fully active Different binding mechanism Assess protein quality Clearly distinguish between genuine SAR and batch to batch variations in protein quality Fully active -8 0.0 0.5 1.0 1.5 2.0 Molar Ratio
Assessment of protein quality by MicroCal itc 200 system Peptide binding to proteinbatch #1 Peptide binding to protein Batch #2 100% of Batch 1 protein active based on stoichiometry 23% of Batch 2 protein active based on stoichiometry Presented by L.Gao (Hoffmann-La Roche), poster at SBS 2009
Protein-ligand interactions Tobromycin binding to aminoglycoside nucleotidyltransferase (2 ) in the absence and presence of cofactor Without MgAMPCPP With MgAMPCPP K D = 0.64 M H = -18.2 kcal/mole S = -34 cal/mole/ o K K D = 0.21 M H = -12.6 kcal/mole S = -12.3 cal/mole/ o K The cofactor has little impact on the affinity, larger impact on enthalpy and entropy Wright and Serpersu, Biochemistry 44, 11581-11591 (2005) S = binding entropy
Protein-protein interactions C-terminal domain of nuclear RNA auxiliary factor (U2AF 65 -UHM) binding to spliceosomal component mutant SF3b155-W7 (shown) or wild-type SF3b155 SF3b155-W7 Wild-type SF3b155 K D ( M) 2.50 2.83 G (kcal/mol) -7.8-7.7 H (kcal/mol) -14.9-9.4 S (cal/mole/ o K) -23.4-5.6 Mutant has little impact on affinity but does impact the interaction Thickman et al, J. Mol. Biol. 356, 664-683 (2006) RNA = Ribonucleic acids G = Gibbs free energy
Antibody Antigen interactions 30 um bi-valent Ab in syringe, 4 um antigen in cell
Protein-DNA interactions DNA binding to subunit -DNA complex binding to subunit Buczek and Horvath JBC 281 40124-40134 (2006) Energetics of telomere complex assembly ITC results confirmed complex formation
Protein-metal ion ITC shows differential binding of Mn(II) ions to WT T5 5 nuclease µcal/sec Time (min) -10 0 10 20 30 40 50 60 70 80 90 100 4 2 0-2 kcal/mole 0 K a = 1.0 x 10 4 M -1 H = +1.6 kcal mol -1 K a = 3.0 x 10 5 M -1 H = -0.59 kcal mol -1-0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Molar Ratio Feng, et al, Nat. Struct. Mol. Biol. 11, 450-456 (2004)
High resolution binding data Time (min) 0 10 20 30 40 50 60 0.00-0.05 µcal/sec -0.10 kcal mol -1 of injectant -0.14 2.4 0.0-2.4-4.8-7.2-9.6-11.9-14.3-16.7-19.1-21.5-23.9-26.3 50 um protein in syringe 9 um LMW ligand in cell Data: D139Gal3zz_NDH Model: TwoSites Chi^2 = 1.860E5 N1 2.35 ±0.00832 Sites K1 8.18E9 ±3.88E9 M -1 H1-8671 ±53.4 cal/mol S1 16.6 cal/mol/deg N2 6.39 ±0.242 Sites K2 5.41E6 ±2.61E6 M -1 H2-945.6 ±50.9 cal/mol S2 27.7 cal/mol/deg 0.0 0.5 1.0 Molar Ratio
Enzyme Kinetics Multiple substrate injections Low enzyme concentration Steady state conditions Continuous assay Higher enzyme concentration Single injection of substrate
Look out for... KP156Gal3e_NDH 0.0-2.0-4.0 kcal mol -1 of injectant -6.0-8.0-10.0! -12.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Molar Ratio
How much sample is required? The experiment C = [Protein]/K D 0-2 -4 C = 0.05 C = 0.5 C = 10-100 Great C = 5-500 Good C = 1-5 and 500-1000 OK kcal/mole of injectant -6-8 -10-12 C = 5 C = < 1 and > 1000 competition ITC -14-16 C = 50 C = 500 0.0 0.5 1.0 1.5 2.0 Molar Ratio
How much sample is required? The experiment BAD GOOD OPTIMAL GOOD BAD 0 1 10 500 1000 Low c High c 0 0 0 kcal mol -1 of injectant -0.2-0.4-2 [Protein]/K D < 1 N fixed Fitted: K D, H -4 10< [Protein]/K D <500 Fitted: N, K D, H -2-4 [Protein]/K D >> 1000 Fitted: N, H 0 4 8 12 16 0 0.5 1.0 1.5 2.0 0 0.5 1.0 1.5 2.0 Molar ratio
Dialyze Sample 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 data Sample preparation The data shown here shows before and after dialysis 2.5 2.0 The large peaks were due to differences in the NaCl concentration between buffers µcal/sec 1.5 1.0 0.5 0.0-0.5 With dialysis with dialysis without dialysis 0 20 40 60 80 100 120 140 160 180 Time (min) Without dialysis
MicroCal PEAQ ITC
MicroCal PEAQ ITC MicroCal PEAQ ITC MicroCal PEAQ ITC Automated
MicroCal PEAQ ITC The latest and 5 th 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: Predicted binding isotherm Output: Recommended concentrations 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 design best 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 Click on alert for guidance Consistent, high quality data
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 No Binding Check data
Robust, automated data analysis Robust, automated data analysis Robust baseline algorithm Binding No binding Automatic control subtraction Check data 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 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 High quality data needed to resolve 2 transitions such as 2 site and enantiomeric interactions
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
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