Isothermal titration calorimetry (ITC)

Transcription

1 Isothermal titration calorimetry (ITC)

2 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 Xt/M t

3 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

4 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

5 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

6 Performing an ITC assay Syringe Ligand in syringe Macromolecule in sample cell Reference cell Sample cell

7 S R

8 Reference power supplied to the reference cell 1 Reference power

9 Reference power supplied to the reference cell activates feedback to sample cell 2 1 Reference power

10 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

11 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

12 After equilibrium have been reached, the system relaxes to reference power level and system is ready for next injection 5 = 0 Reference power

13 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 -> Molar ratio Integration of heats are used to extract affinity (K D ), stoichiometry (N) and binding enthalpy ( H) using appropriate binding model

14 The energetics 0 kcal/mole of injectant Ligand A into compound X The same affinity and stoichiometry but different enthalpy (heat) This tells us we have different binding mechanisms Ligand B into compound X Molar ratio

15 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 = cal K -1 mol -1 T = Temperature in Kelvin = t 0 C KD = Affinity

16 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

17 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 G H -T S Favorable -20 G

18 Measuring bioactivity with ITC: affinity and stoichiometry Kcal/mol injectant % Fully active Different binding mechanism Assess protein quality Clearly distinguish between genuine SAR and batch to batch variations in protein quality Fully active Molar Ratio

19 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

20 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 = kcal/mole S = -34 cal/mole/ o K K D = 0.21 M H = kcal/mole S = cal/mole/ o K The cofactor has little impact on the affinity, larger impact on enthalpy and entropy Wright and Serpersu, Biochemistry 44, (2005) S = binding entropy

21 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) G (kcal/mol) H (kcal/mol) S (cal/mole/ o K) Mutant has little impact on affinity but does impact the interaction Thickman et al, J. Mol. Biol. 356, (2006) RNA = Ribonucleic acids G = Gibbs free energy

22 Antibody Antigen interactions 30 um bi-valent Ab in syringe, 4 um antigen in cell

23 Protein-DNA interactions DNA binding to subunit -DNA complex binding to subunit Buczek and Horvath JBC (2006) Energetics of telomere complex assembly ITC results confirmed complex formation

24 Protein-metal ion ITC shows differential binding of Mn(II) ions to WT T5 5 nuclease µcal/sec Time (min) 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 = kcal mol Molar Ratio Feng, et al, Nat. Struct. Mol. Biol. 11, (2004)

25 High resolution binding data Time (min) µcal/sec kcal mol -1 of injectant um protein in syringe 9 um LMW ligand in cell Data: D139Gal3zz_NDH Model: TwoSites Chi^2 = 1.860E5 N ± Sites K1 8.18E9 ±3.88E9 M -1 H ±53.4 cal/mol S cal/mol/deg N ±0.242 Sites K2 5.41E6 ±2.61E6 M -1 H ±50.9 cal/mol S cal/mol/deg Molar Ratio

26 Enzyme Kinetics Multiple substrate injections Low enzyme concentration Steady state conditions Continuous assay Higher enzyme concentration Single injection of substrate

27 Look out for... KP156Gal3e_NDH kcal mol -1 of injectant ! Molar Ratio

28 How much sample is required? The experiment C = [Protein]/K D C = 0.05 C = 0.5 C = Great C = Good C = 1-5 and OK kcal/mole of injectant C = 5 C = < 1 and > 1000 competition ITC C = 50 C = Molar Ratio

29 How much sample is required? The experiment BAD GOOD OPTIMAL GOOD BAD Low c High c kcal mol -1 of injectant [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 Molar ratio

30 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

31 Poor sample preparation leads to poor data Sample preparation The data shown here shows before and after dialysis The large peaks were due to differences in the NaCl concentration between buffers µcal/sec With dialysis with dialysis without dialysis Time (min) Without dialysis

32 MicroCal PEAQ ITC

33 MicroCal PEAQ ITC MicroCal PEAQ ITC MicroCal PEAQ ITC Automated

34 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

35 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

36 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

37 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

38 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

39 Maintenance alerts Links to in-built videos to demonstrate how to perform straightforward maintenance tasks Click on alert for guidance Consistent, high quality data

40 Fully integrated wash module Choice of cleaning methods available- including a high temperature soak with detergent for very sticky samples

41 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

42 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

43 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

44 Multiple data visualization tools Easy to compare data sets using graphical display software.

45 Multiple data visualization tools Automatically generates complete results table

46 Multiple data visualization tools Automatically generates multiple Final Figure plots with raw and analyzed data

47 Multiple data visualization tools Automatically generates signature plots

48 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

49 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

50 Thank you