Isothermal Titration Calorimetry: Principles and Applications


Molecular recognition is a fundamental phenomenon underpinning almost all aspects of biological processes. Associated energy changes result in release or absorption of heat that can be measured, providing valuable information about the interaction specific features. Isothermal titration calorimetry (ITC) is a label‐free binding assay which measures the affinity, stoichiometry, and thermodynamics of molecular interactions from the reaction heat. It is considered the gold‐standard technique owing to its unique capacity to provide a complete thermodynamic and even kinetic profile of the interaction in a single experiment, thus giving a detailed understanding of driving forces and underlying processes. Moreover, ITC can be used to characterise complex systems hardly amenable using other techniques and also enzyme‐catalysed reactions. Consequently, ITC is being used to study a wide range of interactions mediated by proteins, nucleic acids, lipids, oligosaccharides and other natural or synthetic molecules, and has become a key component of drug discovery platforms.

Key Concepts

  • Molecular recognition via noncovalent interactions is of fundamental importance to most processes occurring within living organisms.
  • ITC provides a general, quantitative approach to analyse the thermodynamics and kinetics of such recognition events, and to characterise enzyme‐mediated reactions.
  • Parsing of the free energy of binding (ΔG) in its two components (ΔH and ΔS) provides new insights into the molecular nature of molecular interactions being studied.
  • The thermodynamic signature provided by ΔH and ΔS is fundamental to not only understand naturally occurring binding interactions but is particularly useful in drug discovery studies.
  • The kinetics of binding events is also central to understand not only physiological processes but also drug efficacy since equilibrium states often may not be established in vivo.
  • Understanding the kinetic behaviour of enzymes is important to understand biochemical pathways and is also a yielding field for drug discovery and development, and biotechnology.

Keywords: isothermal titration calorimetry; molecular recognition; binding thermodynamics; binding kinetics; thermodynamic signature; drug design; enzyme kinetics; biomolecular interactions

Figure 1. (a) Schematic representation of an ITC instrument based on the dynamic power compensation principle showing the sample (S) and reference (R) cells enclosed in the thermostated jacket, the injection syringe, and the computer‐controlled thermostatic, feedback and output systems. (b) Representation of a typical multiaddition experiment (exothermic binding) showing 2′CMP titration into RNase A (25 °C; 20 mM KAc, 20 mM KCl, pH 5.0). Raw thermogram (each peak corresponds to one 2′CMP injection and peak area represents the heat released at the injection; top plot) and binding isotherm (normalised heat effect per injection versus [2′CMP]T/[RNase A]T molar ratio; bottom plot). The solid line through the points is the best fit of results to the one‐site model. The amplitude of the curve is proportional to ΔH, the inflection point gives n, and the slope Ka. (c) Binding experiment of 2′CMP to RNase A using the SIM method. The experimental signal is shown in the top panel and the continuous plot of normalised heats versus the total molar ratio in the bottom panel (30 °C; buffer as in (b)). The red line is the best fit of results to the one‐site model. (d) Representation of a typical ITC enzymatic assay using the multiple‐injection method. The thermal power deflection at a given injection (dQi/dt) is proportional to the reaction rate constant and the substrate concentration in the cell (top panel). The rate of product formation as function of [S], calculated from the top plot, is shown the bottom panel. (e) Thermogram of an enzymatic assay using the single‐injection method. The thermal power was monitored until complete depletion of the substrate in the absence (solid line) and presence (dashed‐dot line) of a competitive inhibitor (top plot), and continuous kinetic curves were generated by plotting the reaction rates measured in the absence (circles) and presence (triangle) of inhibitor versus the remaining concentration of substrate in the cell. Solid red lines in (d) and (e) represent the fit of the kinetic curves to the Michaelis–Menten model.
Figure 2. (a) Influence of the ‘c’ value (plot label) on the shape of ITC isotherms for receptors with a single‐set of identical sites (n). Simulations were performed assuming 100 μM of sites and the following Ka values (M−1): grey (103); blue (5 × 103); magenta (104); green (5 × 104); red (4 × 105) and black (5 × 106). (b) ITC isotherm for titration of lactose (lac; 15 mM) into a lectin‐like protein (364 μM in cell) at low ‘c’ value. Titration was optimised by doing two sequential sets of injections. The red line is the fit of the isotherm with the best parameters (Ka = 1.5 × 103 M−1 and ΔH = −7.06 kcal mol−1, fixing n = 1). (c) Simulated isotherms for a three‐experiment displacement protocol, showing titration of the high‐affinity ligand (HA‐lig; 130 μM) into the protein (M; 10 μM) in the absence (lower plot) and presence (upper plot) of a lower affinity competitor (LA‐lig; 200 μM), whose titration into M is shown in the central plot (KHA‐lig = 2.2 × 109 M−1; ΔHHA‐lig = 3.1 kcal mol−1; KLA‐lig = 2.3 × 106 M−1; ΔHLA‐lig = 8.0 kcal mol−1; n = 1). Red solid lines are the fits of the isotherms. (d) Simulation of a single‐experiment displacement titration of HA‐lig (130 μM in syringe) into M (10 μM) and LA‐lig (5 μM) in cell; binding parameters were as in (b). Differences between competition isotherms in upper plot of (c) and (d) are due to full (c) and partial (d) saturation of M sites with LA‐lig. (e) Simulation of a competition experiment for complete thermodynamic evaluation of poorly soluble tight ligands by single titration of M (400 μM in syringe) into a dilute mixture of HA‐lig (20 μM) and LA‐lig (7.4 μM) in cell (KHA‐lig = 2.2 × 109 M−1; ΔHHA‐lig = 12.2 kcal mol−1; KLA‐lig = 2.8 × 106 M−1; ΔHLA‐lig = −7.4 kcal mol−1; n = 1).
Figure 3. Global analysis of ITC isotherms from (a) dCRY (light‐responsive flavoprotein) titration into CaM (Ca2+/Calmolulin); (b) INAD (photo‐receptor specific protein) titration into CaM; and (c) competitive titration of dCRY into CaM incubated with INAD. The AFFINImeter models applied to each isotherm in the global fitting are indicated in respective panels. (d) Species distribution plot calculated from the competition experiment. Simultaneous analysis of all data sets unveiled that CaM has two independent sites of different affinities for dCRY (Kd in nanomolar and micromolar ranges). The lower‐affinity site showed also a weak interaction with INAD. Modified from Zhao H, Piszczek G and Schuck P (2015) SEDPHATa platform for global ITC analysis and global multi‐method analysis of molecular interactions. Methods 76: 137–148.
Figure 4. Dissection of the binding energetics of KNI‐764 and clinical inhibitors Indinavir, Nefilnavir, Sequinavir and Ritonavir to the HIV‐1 protease. Black bars, ΔG; white bars, −TΔS; grey bars, ΔH. Unlike the clinical inhibitors, which bind with unfavourable or slightly favourable enthalpy changes, binding of KNI‐764, effective against resistant mutants, is strongly exothermic. Modified from Velázquez‐Campoy et al. The binding energetics of first‐ and second‐generation HIV‐1 protease inhibitors: Implications for drug design. Archives of Biochemistry and Biophysics 390: 169–175.
Figure 5. ITC data of a 1:1 binding interaction used for thermodynamic and kinetic analysis with kinITC‐ETC. (a) The raw thermogram. (b) The processed thermogram after baseline correction and noise removal. (c) Binding isotherm resulting from integration of the processed thermogram as a function of [L]T/[M]T (total molar ratio) with integration error bars. (d) Equilibrium time curve showing the equilibration time for each injection as a function of [L]T/[M]T. Solid lines in (c) and (d) are the results of globally fitting the binding isotherm and the time equilibration curve with kinITC‐ETC. Best fitting parameters are shown in respective panels.
Figure 6. cAMP hydrolysis by CaM‐dependent PDE1 (3′,5′‐cyclic nucleotide phosphodiesterase1) determined by ITC. (a) PDE1 (0.04 units) was allowed to reach thermal equilibrium at 37 °C in the presence of 3.8 μM wild‐type CaM. Then, serial injections of cAMP (4.7 mM) were made every 120 s. To avoid distortions due to dilution events, the power change associated with substrate addition at injection ‘i’ was average over the 30 s immediately before the next injection. (Inset) close‐up view of baseline shift in the three first injections (1 × 1 μL; 2 × 2 μL). (b) Rate versus cAMP concentration curves, obtained from the baseline shifts using eqn and corrected for enzyme concentration dilution, were fit to the Michaelis–Menten equation (solid lines) to obtain kcat and Km from titrations carried out in the absence (black circles) and presence of CaM wild‐type (black triangles) and of phospho‐(Y)‐mimetic CaM Y99D/Y138D double mutant (grey circles). CaM increases Vmax without altering the Km, the effect being higher for phosphorylated CaM wild‐type. (c) The thermogram represents the instrument response produced by cAMP hydrolysis reaction. PDE1 (0.1 units) was loaded in the sample cell and 15 μL of cAMP (4.7 mM) was injected and the reaction was monitored until the baseline returned to the initial level. Numerical integration of the area under the peak gave ΔHapp. Two additional injections were made at intervals indicated by the arrows; the decrease in the maximal deflection of the heat flow signal in successive injections was indicative of partial product inhibition (see Stateva et al., for details).


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Menéndez, Margarita(Aug 2020) Isothermal Titration Calorimetry: Principles and Applications. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0028808]