Using the MicroCal VP-ITC … some brief notes. Version 1.06, Last modified Sep 06, 2010, by Richard (rl.kingston@auckland.ac.nz) And with thanks to Jacqui Matthews for assistance. Isothermal Titration Calorimetry (ITC) is a basic quantitative technique for studying molecular interactions, in which the tiny amount of heat released or absorbed during binding is accurately measured. From this raw data, a very complete description of binding can be achieved. There’s been some good book chapters and reviews written about ITC (see e.g. {Indyk, 1998, p09394},{Freire, 1990, p06334},{Ladbury, 1998, p05149},{Harding, 2001, p05148},{Pierce, 1999, p05074},{Leavitt, 2002, p00838}, {Perozzo, 2004, p06447},{Velazquez-Campoy, 2004, p05064},{Velázquez Campoy, 2005, p05058;Buurma, 2007, p06531},{Freyer, 2008, p07431}). These cover both the theoretical and practical aspects of the technique, and you should read some of them for proper understanding. ITC can also be used to study the kinetics of enzyme-catalyzed reactions (see e.g. {Freyer, 2008, p07431},{Todd, 2001, p06994}). However these notes consider only the study of binding processes. The absolute basics of the experimental method Generally, for studying hetero-complex formation, you fill the sample cell with a solution of one of your molecules. You then stepwise inject a much more concentrated solution of the binding partner into the cell. As the binding reaction progresses, heat is generated or consumed, and this is measured. The VP-ITC, like all commercial microcalorimeters, is a differential instrument. Inside the VP-ITC there are two identical cells … sample and reference. The reference cell plays no part in the titration and, since we work with aqueous solutions, is filled with water. The ITC signal actually results from the power required to maintain thermal equilibrium between the reference and sample cells. The raw data is a series of heat “pulses” associated with each injection. Integrating these peaks generates a titration curve, where the ordinate axis is the heat generated per mol of injectant. This data can be fitted to a mathematical model of the binding process … most often a simple 1:1 binding scheme as discussed below. Here’s some good quality ITC data, and a fitted model to give you the general idea … Binding of the nucleocapsid-binding domain from the measles virus P protein (P457-507) to a peptide from the measles virus N protein (N477-505). (A) Raw ITC binding data for 23 automatic injections of P457-507 (each injection 12 µL, protein concentration 1.44 mM, 240s interval between injections), into a cell containing N477-505 (initial protein concentration 0.12 mM). Proteins were suspended in 10 mM Na2HPO4/NaH2PO4 buffer (pH 7.0); 100 mM NaCl, 0.01%(wt/vol) Sodium azide. Sample cell temperature was 20 °C. Baseline mixing heats were determined from duplicate injections of P457-507 into buffer. These were subtracted from the binding heat data before model fitting. (B) The integrated titration curve obtained from the raw data in (A), following baseline subtraction. The solid squares represent the experimental data while the solid line corresponds to the multiple independent binding site model that was fit to the data. Binding parameters were determined to be n=0.93, KD = 13 µM, ΔH= -1.1 x104 cal/mol, ΔS = -14 cal/K.mol. Models of binding Hetero-dimerization Typically we characterize the interaction between two different binding partners. The simplest scheme involves a 1:1 association. This can be written as follows A + B ? AB The equilibrium dissociation (KD) and association (KA) constants governing this reaction are … KD = 5A ?5B ? 5AB ? 5AB ? 1 KA = K = 5A ?5B ? D The change in Gibbs free energy associated with binding is related to the equilibrium association constant KA … DG =- RT ln KA = DH - TDS Where R is the gas constant, and T is the temperature in Kelvin. ITC can provide us with estimates for KA (or equivalently KD) as well as for ΔH and ΔS, the enthalpic and entropic contributions to the free energy of binding. Most ITC data are analyzed in terms of this simple binding scheme. And for now, this is what these notes mostly discuss More complicated hetero-complex formation Obviously many more complicated binding schemes exist, and for some of these, the equations have been worked which enable fitting to ITC data. Worth noting are the following 1. One set of binding sites, all identical and independent. Binary complex formation is usually treated using this general model (since this represents a special case where there is just one binding site). Using the more general model you determine, in addition to KD, ΔH, and ΔS, the stoichiometry (n), which should be 1 for a binary complex. 2. Two sets of binding sites, the members of each set identical and independent. 3. Sequential binding models. 4. Cooperative binding models for ternary complexes{Velazquez-Campoy, 2006, p00503}. 5. Binding to one-dimensional lattice-like macromolecules e.g. nucleic acids or carbohydrates (McGhee-von Hippel model){Velazquez-Campoy, 2006, p05057}. The range of binding processes that can be studied using ITC continues to expand. Protein self-association In certain circumstances ITC can also be used to study protein self-association (homocomplex formation). In this case the binding partners are identical, and the experimental procedure is different. The protein is put into the syringe and then injected into the sample cell containing only buffer. With the correct concentration regime, dilution will cause the complex to dissociate, and heat will be generated. Let’s consider the simplest case … homo-dimerization. The basic reaction scheme is A + A ? A2 The equilibrium dissociation (KD) and association (KA) constants governing this reaction are … KD = 5A ?2 6A2 @ 6A @ 1 KA = K = 2 2 5A ? D Assuming this simple binding scheme, a model for the heat generated during the experiment is worked out in the appendix. Coupling of binding to protonation/deprotonation Many protein-protein and protein-ligand interactions show strong pH dependence, indicating that binding is coupled to proton uptake and release. Any change in the observed binding enthalpy (ΔH) or binding affinity (KA) with pH, is indicative of proton linkage. Proton-linked equilibria are more complicated to study by ITC, because the protons absorbed or released will be given to, or taken from, the buffer, and that generates heat! A full investigation of binding then requires that you take buffer-related contributions into account. To establish if your binding process is proton linked, the obvious path is to do repeat titrations at a different pH’s (i.e. at different proton concentrations), being careful to keep the other solution variables the same (e.g. ionic strength). Less obviously you can perform replicate titrations at the same pH, using buffers with different (and known) ionization enthalpies. In either case if the observed binding parameters shift beyond the experimental uncertainty, there’s a degree of proton linkage in your binding reaction. These two experimental approaches (varying the pH, and/or varying the buffer) provide the means to investigate proton linkage. The data can be analyzed in terms of the linkage theory of Wyman{wyman, 1990, p06529}. In general, binding could be linked to a single protonation event, which is pretty simple to model, or to multiple protonation events, which is a bit of a nightmare. There are lots of elegant papers that show you how to proceed{Kresheck, 1995, p06433},{Gómez, 1995, p06357},{Baker, 1996, p06391},{Baker, 1997, p06445},{Xie, 1997, p06438},{Parker, 1999, p06440}, {Velazquez-Campoy, 2000, p06441},{Bruylants, 2007, p04780}. There is also a comprehensive recent review of the enthalpies associated with ionization of buffers {Goldberg, 2002, p06449}, very useful when you are planning these sorts of experiments. Planning an ITC experiment Hetero-dimerization. Too strong, too weak, just right. There are some limitations in the range of binding affinities that can be studied by ITC. Generally with KA in the range 104 -108 M-1 (KD = 10-4 – 10-8 M) things are fairly straightforward. With weaker or stronger binding than this, you will experience some difficulty. The titration curves will either be too flat, or too “step-like” too allow routine fitting of the model. However, if a competing moderate affinity ligand is available, then it’s possible to design competition experiments (“displacement titration”) to allow binding to be fully characterized in these extreme cases. Consult the literature for details. Otherwise you need to make some assumptions, and fix some model parameters to allow estimation of the others. More on this later. Concentration and volume requirements Generally the solubility of the molecules will dictate which is to be put into the cell (low concentration sample) and which is to be put into the syringe (high concentration sample). To fill the sample cell, without risking introduction of bubbles, you’ll need 2ml of the low concentration sample. To fill the syringe you’ll need 400 µL of the high concentration sample. But actually you need at the minimum 2 x 400 = 800 µL of the high concentration sample, because there’s a critical control you must run (see below). This assumes that nothing goes wrong, and you don’t need to replicate. Everything works first time in science. So the short story … ITC requires a truckload of material. But you do get it all back undamaged afterwards! For an uncharacterized binding reaction you can’t know how much heat will be generated, so you must guess the concentrations required. Generally the low concentration sample should be somewhere between 5-100 µM. Once the titration is complete, you should have a 2-3 fold molar excess of the injectant in the cell (more for very weak interactions). For the VP-ITC this requires that the molecule in the syringe is 10 -15 x more concentrated than the molecule in the cell. Sample preparation The main requirements are that the molecules are pure, and are in the same buffer. For relatively big molecules this can be achieved through exhaustive dialysis. The safest strategy is to prepare a very large batch of buffer (5L) that will allow dialysis of both samples, with multiple buffer changes. This is not the place to take short cuts. Always keep some uncontaminated buffer aside to allow washing of the ITC cell, and to perform any dilutions that might be needed. Small ligands, which cannot be effectively retained by a dialysis membrane, will have to be directly dissolved in buffer. In that case you may want to check the pH and conductivity of the resulting solution. We don’t currently have a microprobe pH and conductivity meter, but we are working on it. Some suggest you should avoid volatile buffers such as formic and acetic acid, as these may cause problems (samples have to be degassed prior to data collection). You must know the concentrations of your reactants accurately. The best way to do this for proteins is by UV-spectroscopy at 280 nm, calculating the extinction coefficients from the amino acid composition. Running the ITC These notes are just to remind you what to do. New users must receive training from Graham Bailey (gbai015@ec.auckland.ac.nz). Setting up * Turn on the monitor and the computer * Turn on the ITC (switch at back left) * Click on the VPViewer icon on the desktop (this starts the ITC controller) * In the Thermostat/Calibration Tab, enter your ITC run temperature and hit “Set Jacket Temperature” (it takes some time to equilibrate the cells, particularly if the run is some way from room temperature) Degassing samples Before loading the samples, they need to be degassed, to reduce the possibility of bubble formation. This is particularly critical for low temperature work. * Place the sample in the opaque plastic tubes. Add clean magnetic stir bars. * Turn on the ThermoVac. * Set the temperature to the ITC run temperature … or if you’re running below room temperature set the temperature to 3 °C < ITC run temperature. * Place the samples in the ThermoVac, and seat the vacuum chamber over top. * Connect tubing between the vacuum chamber and the left plug at the rear of the ThermoVac (“Vacuum”) * Set the stir speed to Low. * Make sure the valve on top of the vacuum chamber is open, * Turn on the vacuum pump by flicking the switch to the timer position (this runs the pump continuously for 8 minutes). * Slowly evacuate the chamber by closing the valve, while pressing gently on the chamber. The pump will change pitch. Watch the samples as you do this. If they begin to spit, or bubble over, you’ve gone too far! Fill the sample cell Using the long Hamilton syringe … * Withdraw the water from the sample cell. * Rinse the cell with water. * Rinse the cell 4 times with buffer. * Remove as much buffer from the syringe as you can. * Draw the degassed, low concentration sample into the syringe. * Lower the syringe into the cell until it touches the bottom, and raise 1-2 mm. * Push the sample into the cell in three short bursts. Hopefully this will dislodge any air bubbles trapped at the top of the cell. * Using the syringe, withdraw excess solution from the filling tunnel, until the liquid sits just above the entrance port. * Clean the syringe thoroughly with distilled water. Filling the injector With the injector seated in its holder, on the side of the ITC … * Transfer the high concentration sample into a glass tube. * Using the control software, close the Fill port * Temporarily remove the injector. * Place the glass tube securely in the holder. * Carefully replace the injector. The bottom of the stir paddle should be near the bottom of the glass tube. * Attach the syringe with flexible tubing to the Fill port of the injector. * Using the control software, open the Fill port. * Using the syringe, draw sample into the injector. You may end up with a small air bubble trapped at the top, just below the Teflon plug. This is okay … you’ll probably introduce more air if you attempt to dislodge it. Very large or multiple air bubbles are bad news. * Using the control software, close the Fill Port * Using the control software, Hit Purge/Refill twice (to appease the ITC fairies) * Now remove the injector from its holder and carefully wipe any excess solution from the stir paddle with a Kimwipe. *Eject a tiny amount of solution. Using the control software, change the distance to 0.01 (of an inch) and click “Dn” (Down). A drop of liquid should appear at the injector outlet. Blot this drop away on a Kimwipe. * Lower the injector carefully into the cell. The fit is tight. Make sure it’s properly seated. Now set the run parameters and you’re off. In the absence of prior information try for 24 injections of 12 uL each, spaced around 5-7 minutes (300 - 420s) apart. If the signal is weak, estimating the baseline accurately becomes more important, so leave at least 400s between injections. Also adjust the delay time to ~150s, so you get a decent baseline preceding the first injection. In this case you’ll probably want to integrate the raw data with our local analysis software, which will work better than the Origin default. It’s been standard practice to initiate each run with a small “throwaway injection”, because the heat observed in the first injection is systematically smaller than expected. Joel Tellingheusen’s lab have shown that this “first injection anomaly” arises from backlash in the plunger mechanism following the Purge/Refill steps{Mizoue, 2004, p05043}. If you make sure to push the plunger down a short distance (as detailed above), the throwaway injection should not be necessary. Leave the stirring speed at 300 rpm Reference power of 20 µCal/sec (may need to adjust for highly exothermic or endothermic reactions … see pg 47 of the manual) Cleaning up It is critical that the instrument be left spotless. I would repeat that for emphasis but this would be boring. Cleaning the Cell 1. Set up the vacuum pump for cleaning the cell. Instructions are on Page 51 of the VP-ITC Manual, if you’re nervous. 2. Flush 10-20 mls of cold water through the cell. 3. Heat 250 mls of water in a microwave until it’s hot (but not boiling). Add 1.25 mls of LA2 detergent, and flush the cell with ~240mls of the hot detergent solution (Leave a little behind for cleaning the injector) 4. Flush the cell with 500 mls of cold water. Leave the cell filled with water. Cleaning the Injector 1. Using the control software, open the Fill port. 2. Set up the vaccum pump and connect it to the Fill port using the drying adapter 3. Place the stir paddle of the injector in a beaker of cold water 4. Draw 5 -10 mls of water through the injector 5. Then consecutively, using the same procedure, draw through 5-10 mls of the detergent solution, 5-10 mls water, and 5-10 mls of methanol. 6. Finally pull air through the injector for five minutes. Controls Heat-of-dilution experiments are necessary controls. Let’s say we have component A in the syringe and component B in the sample cell. Properly we need to run the following controls 1. Component A is injected into Buffer. 2. Buffer is injected into Component B. 3. Buffer is injected into Buffer. These heats should be used to correct the binding data in the following way Heat from binding of A to B = Heat from titration of A and B - (1) - (2) + (3) In practice contributions from (2) and (3) are usually small and self-cancelling, so are usually neglected. But contributions from (1) can be considerable and this control must be performed, and used to correct the raw data before model fitting. Analyzing the data The data analysis can be done using the Origin Software package, which allows integration of the raw data and fitting of some common models. There is a fairly decent manual. More on this later … Common problems and things to avoid Baseline drifts often indicate slow reactions (usually not binding reactions). Slow oxidation of DTT is a good example (If you must include a reducing agent, you might consider the less reactive TCEP.HCl instead). Poor buffer matching between the solutions in the syringe and in the sample cell will give rise to large heats of mixing and dilution. These can easily obscure binding heats for the reaction of interest (see notes on sample preparation above). Particularly troublesome is a change in solution pH, since heats associated with protonation and deprotonation can be large. Booking and paying for use of the instrument. There is a booking sheet posted near the instrument. Use this to indicate the dates you intend to use it. The University of Auckland seeks full cost recovery on all pieces of scientific equipment over $100000. Some of us think this is a bad idea. No matter. The outcome is an $80 per day charge for using the VP-ITC. This money goes to the SBS to offset the depreciation payments the university requires for the instrument. There is a spreadsheet on the desktop of the computer running the ITC. Please enter your usage and a university account number. The minimum billing time is a day. You may use the full 24 hours if it pleases you. Support at Microcal and Beckman-Coulter 1.Application support Verna Frasca: vfrasca@microcal.com Dr William Peters: wpeters@microcal.com 2.Service Queries service@microcal.com 3.Sales cshorten@beckman.com Appendix: Models used for fitting data A1. One set of binding sites, all identical and independent. The derivation is discussed in detail in several places ({Freire, 1990, p06334}{Indyk, 1998, p09394},{Velazquez-Campoy, 2004, p05064}). See also the appendix of MicroCal’s Data Analysis guide. Component A is in the calorimeter cell; Component B is in the syringe. If there were just a single binding site then the basic reaction scheme could be written A + B ? AB With the equilibrium dissociation (KD) and association (KA) constants governing the reaction … KA = 5AB ? 5A ?5B ? 5A ?5B ? 1 KD = K = 5AB ? A (1) (2) In the case of A having n equivalent and independent binding sites for B we need to describe the system in terms of the fractional occupation of the binding sites Θ , or the binding function v (the molar ratio of the amount of ligand bound to the total amount of acceptor). See chapter 15 of Biophysical Chemistry, by Cantor & Schimmel, for the development of this idea. We will make use of Θ. By definition v = nΘ In this case it can be shown that. kD = ]1 - H g5B ? H (3) Where kD is the microscopic dissociation constant that characterizes all of the sites. The total concentrations of A and B (which we know from experiment) are related to the concentration of free B as follows. 6BT @ = 5B ? + nH [AT ] (4) Now if we combine (3) and (4) to eliminate [B] we get n 6AT @H 2 - (kD + 6BT @ + n 6AT @) H + 6BT @ = 0 (5) This is a quadratic in Θ, with the relevant solutions given by (kD + 6BT @ + n 6AT @) - (kD + 6BT @ + n 6AT @) 2 - 4n 6AT @6BT @ (6) 2n 6AT @ The experiment consists of performing a series of injections of B into A. We need to develop a model describing the heat generated by injection i. First … we need to know the total concentrations of A & B in the cell after injection number i. When injecting a certain volume, v, into the cell, the same volume is lost. If we employ the simplest model of this process … that the solution is ejected before any mixing can take place then. 6BT @C,i = vi 5B ?s + b 1 - vi l6BT @C,i-1 (7) 6AT @C,i = b 1 - vi l6AT @C,i-1 (8) VO VO VO Where [AT] C,i = Total concentration of A in the cell after injection i. [B] S = Concentration of B in the syringe. [BT] C,i = Total concentration of B in the cell after injection i. vi = Volume of the i’th injection. Vo = Active volume of the calorimeter cell. Now the heat generated on each injection results from the change in the occupancy of the binding sites, and the heats resulting from dilution of the individual components. v qi = VO ;DHA (nH C,i 6AT @C,i - nH C,i-1 6AT @C,i-1 b 1 - Vi l) + QA + QB E O (9) Where Qa & Qb = The heats of dilution associated with component A and component B. ΔHA = The molar enthalpy of binding to a single site. The corrective factor (1- vi/Vo) arises because of the ligand/acceptor complex that is ejected from the active volume of the calorimeter cell upon injection of more ligand. We can measure Qa and Qb by performing suitable experiments (Injecting A and B into buffer,or vice versa). Usually these will be subtracted from the measured heats qi , before model fitting. We can calculate [AT]C,i and [BT]C,i using expressions (7) and (8). Θ can be calculated using expression (6). Therefore non linear least squares fitting of the experimental qi using expression (9) can be used to determine n, kd and ΔHA. A2. Two sets of binding sites, the members of each set identical and independent. To be completed A3. Homo-dimerization For this experiment we have a molecule in reversible monomer-dimer equilibrium in the syringe, which we inject into the cell. The accompanying dilution causes dissociation of the dimer, and this generates or consumes heat. The basic monomer-dimer reaction scheme can be written A + A ? A2 The equilibrium dissociation (KD) and association (KA) constants governing this reaction are … KD = 5A ?2 (1) 6A2 @ 6A @ 1 KA = K = 2 2 5A ? D (2) First, let’s be clear on what’s happening in both the syringe and cell. There will generally be both monomer and dimer present, as dictated by the equilibrium constant. What we generally know is the total concentration of A … [AT] 6AT @ = 5A ? + 2 6A2 @ (3) Or equivalently 5A ? = 6AT @ - 2 6A2 @ (4) 6A2 @ = 1 ^6AT @ - 5A ?h 2 (5) Now, using (4) or (5), we can eliminate either [A] or [A2] from expression (1). For example if we substitute (4) into (1) and rearrange we get 4 6A2 @2 - ^ 4 6AT @ + KDh6A2 @ + 6AT @2 = 0 (6) This is a quadratic in [A2], and the solutions are given by 6A2 @ = (7) ^ 4 6AT @ + KDh ! ^ 4 6AT @ + KDh2 - 16 6AT @2 ^ 4 6AT @ + KDh ! 8 6AT @KD + KD2 = 8 8 Expression (7) gives the dimer concentration [A2] in terms of KD and the total protein concentration [AT]. Also recall from high school math, that although there are two apparent solutions to the quadratic equation, only one will be real and have physical meaning. If we substitute (5) into (1) and rearrange we get the corresponding expression for the monomer concentration [A]. 2 5A ? = -KD ! 8 64AT @KD + KD (8) Okay – that’s the warm up. Now we need to think about what happens when we perform a series of injections. For a start – we need to know the total concentration of A in the cell, after injection number i. This question is a little more complicated than it appears, because the VP-ITC is a perfusion instrument. For every volume that’s injected into the cell a corresponding volume is ejected. It is assumed that the ejected material is never again involved in mixing or in the production of heat. We are going to employ the simplest model of this process, which is that the solution exiting the cell is expelled before any mixing with the injected solution. In this case the total concentration of A in the cell, following injection i, is given by … 6AT @C,i = b vi l6AT @S + b 1 - vi l6AT @C,i-1 VO (9) VO Where [AT] C,i = Total concentration of A in the cell after injection i. [AT] S = Total concentration of A in the syringe. vi = Volume of the i’th injection. Vo = Active volume of the calorimeter cell. From total concentrations of A in the cell we can calculate the concentrations of monomer and dimer using (7) and (8). Now we are in a position to consider the heat generated on each injection i. This is proportional to the change in the concentration of monomer in the active volume of the cell, following the injection, due to dimer dissociation. This too, is a little tricky to consider, so we’ll draw a diagram. Once again we’re employing the simplest model of the injection, assuming no mixing of the introduced and ejected material Now by inspection v v Dmonomer = 5A ?C,i - b 1 - Vi l5A ?C,i-1 - b Vi l5A ?S O O (10) Where [A]C,i = Concentration of monomers in the cell after injection i. [A]S = Concentration of monomers in the syringe vi = Volume of the i’th injection. Vo = Active volume of the calorimeter cell. The heat generated on each injection (i) is then qi = VO DHD Dmonomer + the heat of dilution (11) v v qi = VO ' DHD c5A ?C,i - b 1 - Vi l5A ?C,i-1 - b Vi l5A ?S m + ^6AT @C,i - 6AT @C,i-1 h qdilute 1 O O Where ΔHd is the enthalpy of dimer dissociation (per mol of monomer). The last term is just the heat of dilution. In contrast to the study of hetero-complex formation, this quantity is difficult to measure experimentally by running suitable control experiments, so we have to include it explicitly in the model. It is assumed that the heat of dilution is a linear function of the change in protein concentration(mol/L). This is an approximation, but a reasonably decent one. Now we have everything we need to characterize binding. First for our injection series, we can determine the total concentration of A in the cell, [AT]C,i using expression (9). The concentrations of monomer in cell and syringe ([A]C,i and [A]S) can be calculated from the total concentrations ([AT]C,i and [AT]S) using expression (8). Hence from nonlinear fitting of qi as a function of [AT]C,i, we can get Kd and ΔHd. References