Enzymes are proteins that function as biological catalysts, which play crucial roles in the biochemical processes that occur in living organisms. Understanding how enzymes function, and how to activate or inhibit their activity, is a core research focus for biochemists. Since many drug targets are enzymes, the development of new therapies requires understanding of how the target enzyme binds and catalyzes its natural substrate. Several drugs are therapeutic enzymes, which are injected into the patient to treat genetic disorders characterized by missing or defective enzymes. Enzymes are also important in other industries including food science, biofuels, and detergents.
Although enzymes were discovered in the mid-1800s, isolated and purified since the early 20th century, and cloned and expressed in recombinant systems since the 1970s, there is a continued need for efficient and detailed enzyme analysis to exploit the potential of enzyme-driven catalytic reactions. New enzymes are also being discovered. Reliable enzyme kinetics data are crucial to understand and control enzyme effectiveness and create next generation drugs.
Isothermal titration calorimetry (ITC) techniques have been successfully applied to study enzyme kinetics and inhibition. ITC is a well-established, versatile technique that is widely used for measuring reaction thermodynamics. In this review, we discuss how ITC generates real-time, enzyme kinetics data, comparable to other enzyme assays. In part 2, we discuss how to perform enzyme kinetics experiments with a MicroCal ITC system.
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Enzymes are proteins that function as biological catalysts, which play crucial roles in the biochemical processes that occur in living organisms. Understanding how enzymes function, and how to activate or inhibit their activity, is a core research focus for biochemists. Since many drug targets are enzymes, the development of new therapies requires understanding of how the target enzyme binds and catalyzes its natural substrate. Several drugs are therapeutic enzymes, which are injected into the patient to treat genetic disorders characterized by missing or defective enzymes. Enzymes are also important in other industries including food science, biofuels, and detergents.
Although enzymes were discovered in the mid-1800s, isolated and purified since the early 20th century, and cloned and expressed in recombinant systems since the 1970s, there is a continued need for efficient and detailed enzyme analysis to exploit the potential of enzyme-driven catalytic reactions. New enzymes are also being discovered. Reliable enzyme kinetics data are crucial to understand and control enzyme effectiveness and create next-generation drugs.
Isothermal titration calorimetry (ITC) techniques have been successfully applied to study enzyme kinetics and inhibition. ITC is a well-established, versatile technique that is widely used for measuring reaction thermodynamics. In this review, we discuss how ITC generates real-time, enzyme kinetics data, comparable to other enzyme assays. In part 2, we discuss how to perform enzyme kinetics experiments with a MicroCal ITC system.
When an enzyme interacts with its substrate, it forms an enzyme-substrate complex. This complex gets converted to the transition state, the transition state complex is then transformed into an enzyme-product complex, which finally dissociates to give product and free enzyme. In biological systems, where the concentration of substrate is much higher than that of the enzyme, these steps can be simplified and summarized in Equation 1:
[1]
where E is enzyme, S is substrate, ES is enzyme-substrate complex, P is product, k1 is the rate constant for the formation of ES, k-1 is the rate constant for dissociation of ES back into E + S, and kcat is the catalytic rate constant. The kinetics of simple reactions were first analyzed by Michaelis and Menten in their classic paper of 1913[1]. Their approach to analyzing enzyme-substrate kinetics is still the principal method for enzyme characterization. The Michaelis- Menten equation is given in Equation 2:
[2]
where Rt is the reaction rate, [S]t is the substrate concentration, Vmax is the maximum velocity if all the enzyme was present as ES, and KM is the Michaelis constant. Since Vmax = kcat . [E], the above can be written as Equation 3:
[3]
In the presence of a competitive inhibitor, the rate is:
[4]
Where [I] is the inhibitor concentration, and KI is the inhibitor constant.
The mathematical definition of KM means that it effectively quantifies the affinity of binding between the enzyme and its substrate when product formation is the rate-limiting step. The lower the KM, the lower the concentration of substrate required to achieve a given rate. This makes KM a valuable metric for comparing the performance of different enzymatic systems and for:
Most enzymes are now expressed as recombinant proteins, and their associated substrates can usually be produced in a similar way or chemically synthesized. With both components in place, the challenge is to identify an efficient assay for detailed study of the reaction. In systems where the substrate or product is detectable by a spectrophotometer, continuous assays are performed by adding the enzyme to solutions of different substrate concentrations and monitoring the resulting product formation or decreasing substrate concentration. Early in the assay, when the amount of substrate is in excess over the enzyme, the observed rate is linear with time and is referred to as the initial rate or velocity. Plotting initial velocity vs. substrate concentration results in a Michaelis-Menten curve like Figure 1 that can be used to determine KM and Vmax. This type of assay provides a powerful bioanalytical tool that is used to characterize native and engineered enzymes.
Figure 1: A simulated Michaelis-Menten curve (generated using Equation 3). Parameters were set as follows: KM = 1 μM and kcat = 5 s-1 and the enzyme concentration was fixed at 1 nM. At the substrate concentration denoted by the light blue line exactly half the enzyme molecules are in an ES complex at any instant and the rate is exactly one half of Vmax.
In many instances, the product or substrate does not have a convenient chromophore. Even if one product can be monitored spectroscopically, synthesized substrate analogues may not have such features. When designing substrate analogues in order to probe enzyme mechanism one should only have to consider the chemistry of the catalytic reaction rather than issues around performing assays. The latter can often complicate design strategies since the inclusion of a chromophore can be cumbersome, time consuming and expensive.
An alternative is a discontinuous assay, by mixing enzyme and substrate using stopped flow or quench flow techniques. The reaction proceeds for different times before quenching using a pH jump or a temperature change. The amount of formed product or remaining substrate in the reaction is quantified using mass spectrometry or chromatography. These are specialized techniques that are often very useful for measuring fast kinetics or isolating intermediates. However, for routine analysis these methods can be time consuming and expensive, since multiple experiments are needed to generate a single Michaelis-Menten plot.
There are also coupled enzymatic assays, where the product of enzymatic catalysis of interest is the substrate for a second assay. Sometimes these assays require multiple steps. The final product is then monitored by a continuous or discontinuous assay.
Even with these options, it is not always possible or straightforward to measure changes in substrate or product concentration in tradition assays, because:
Isothermal Titration Calorimetry (ITC) is a powerful and versatile technique that is widely used for measuring the binding affinity and thermodynamics of equilibrium association reactions [2-9]. ITC is also an accepted, universal label-free assay technique that can be applied to any enzyme-catalyzed system, provided that the reaction is associated with a change in enthalpy. ITC does not rely on a spectrophotometer, and the experiments are quick – ITC data for a Michaelis-Menten curve can be generated in 30 -90 minutes with current instrumentation - and only small amounts of material are required, making it an appealing alternative for enzyme kinetics and inhibition assays.
ITC measures enzyme kinetic parameters because the thermal power generated as the reaction proceeds is a direct and sensitive observable event. The use of ITC to measure the heat generated during an enzymatic reaction is a well-established assay [10-23]. The rate of an enzymatic reaction is directly proportional to the thermal power, defined as the heat (Q) produced as a function of time: Power = dQ/dt.
A typical experiment has the substrate in the ITC injection syringe, and enzyme in the ITC cell. After the substrate is added to the enzyme, there is a reaction which either takes in or releases heat, triggering a heat change in the ITC cell. The data generated shows a spike with a positive or negative area associated with each injection. (Figure 2). ITC directly measures dQ/dt after the injection of substrate, at different substrate concentrations. The change in thermal power is represented by a change in the Y axis position of the raw ITC data, compared to the Y axis position for a control titration where there is no enzymatic reaction (Figure 2).
The amount of heat involved with converting n moles of substrate to product is given by Equation 5:
[5]
where ∆Happ is the total molar enthalpy for the reaction, determined experimentally, [P]total the concentration of product generated and V0 is the ITC cell volume. It can be seen from Equation 5 that measuring the thermal power generated by the enzyme as it catalyzes conversion of substrate gives a measure of the reaction rate since:
[6]
where d[P]total/dt is equal to the rate of product formation, that is the rate of reaction, and Equation 6 can be rearranged to give:
[7]
A complete Michaelis-Menten curve is generated from a single ITC titration, because one experiment has different [S]t values. [S]t and [E] are calculated after each ITC injection, using initial substrate and enzyme concentrations and ITC injection volumes. dQ/dt for each [S]t is determined from the ITC experiment. Rt (from equation 7), [S]t and [E] are fit to equation 3 to solve for KM and kcat. The software provided with MicroCal PEAQ-ITC and earlier ITC systems have models for enzyme-substrate data analysis and calculates KM and kcat using non-linear least-squares regression analysis.
ITC has been used to characterize most classes of enzymes. Todd and Gomez[14] and Bianconi[16] discuss the use of ITC to characterize almost every class of enzyme. Other examples include:
A typical experiment for enzyme kinetics has the enzyme in the ITC cell and substrate in the ITC syringe. It is also helpful if there is some indication of approximate KM so that appropriate substrate concentrations can be used to yield a complete Michaelis-Menten curve with some points of the curve below KM and some above it.
Typical enzyme and substrate concentrations for ITC enzyme assays are in the range of spectroscopic assays. Enzyme in the ITC cell is in the sub-nanomolar to micromolar range (the higher the KM value, the higher the concentration needed). The required concentration is often less than that used for traditional ITC protein-ligand binding assays. Substrate in the syringe is in the micromolar to millimolar range and is higher than the KM value.
Enzyme kinetics parameters will vary due to salt, pH, etc. In order to avoid excessive heats of dilution it is necessary to dialyze the enzyme in the chosen buffer and then use the same buffer to make up the substrate solution.
There are two methods that can be used to measure enzyme kinetic parameters with ITC: the multiple-injection method, which uses pseudo-first order conditions, and the continuous assay/single injection method, which uses a single injection of substrate into enzyme. Descriptions for each method are given in the companion technical note [41]. For more details on experimental design and data analysis, refer to References 14,17, 18, 20, 21, 22, 23, 37 and 38.
ITC experiments for enzyme kinetics reactions are commonly conducted this way, with multiple injections of the substrate generating multiple rate determinations under pseudo-first order steady-state conditions in a single ITC titration experiment.
The rate data can be obtained by having relatively low amounts of enzyme in the ITC cell, relatively high amounts of substrate in the injection syringe and by leaving shorter gaps between injections. The aim here is to ensure that subsequent to each injection, steady-state conditions are maintained and no more than 5% of the injected substrate is depleted prior to the next injection. In addition, it is also necessary a control where the identical solutions of substrate used in enzyme assays are injected into the reaction buffer.
Figure 2 shows the raw ITC data for multiple injections of the substrate p-nitrophenyl phosphate (PNPP) into 0.68 μM serine/threonine phosphatase (PP1-γ) [18]. After initial equilibration, the first few substrate injections resulted in an initial endothermic peak corresponding to the heat of dilution. The baseline then stabilized at a lower power level than the pre-injection value, resulting in a change in thermal power. This is because some heat is generated by the enzymatic reaction and therefore correspondingly less power is supplied by the instrument in order to maintain thermal equilibrium. The rate of heat generated for each injection n (power: dQn/dt) at each substrate concentration is proportional to the rate of reaction. After a few injections, there is no change in power since the substrate concentration is now high enough for the enzyme to operate close to its maximum rate.
A complete Michaelis-Menten curve is generated from a single ITC experiment (Figure 3) since the substrate concentration increases with each injection. The data analysis software calculates the enzyme and substrate concentrations after each ITC injection. dQ/dt for each injection of substrate are determined from the ITC raw data, and the ITC cell volume is known, so the rate for each substrate concentration is determined from Equation 7. KM and kcat are solved using Equation 3.
Figure 2: Raw calorimetric data for the measurement of the reaction rate for the hydrolysis of PNPP by serine/threonine phosphatase (PP1-γ). The dashed line is a linear least square best fit to the pre-injection baseline. Data was generated with a MicroCal VP-ITC, and analysis was done with Origin 5 software (Reference 18)
Figure 3: Rate vs substrate concentration data for the hydrolysis of PNPP by PP1-γ. Two sets of data are shown (normalized for enzyme concentration): rate data from the ITC experiment in Figure 2 (blue) and rate data from spectrophotometric assays (red). The blue fit line is a non-linear least square best fit To the spectroscopic data. (Reference 18)
For the PNPP-PP1-g experiment, the kinetic parameters from ITC were compared to its spectroscopic results (Figure 3). There was excellent agreement between the two sets of experiments [18].
An alternative strategy for obtaining enzyme kinetic parameters involves continuous rate measurements after a single injection of substrate at concentrations higher than KM. In these experiments thermal power is monitored as the substrate is completely depleted (Figure 4). At any given time, the reaction rate can be determined using Equation 7 and the concentration of substrate at any given time can be determined from the integral of the heat evolved (Equation 8). Therefore, plots of rate vs. substrate concentration can be plotted to give a continuous kinetic curve.
[8]
An advantage to this method is ΔHapp is determined from the same experiment, by integration of the area under the peak. See Figure 5 for an example.
Figure 4: Raw ITC data (Top) and fit data (bottom) for the hydrolysis of BAEE with trypsin, collected on a MicroCal PEAQ-ITC. Initial concentration of BAEE in syringe was 4 mM, initial concentration of trypsin in cell was 20 nM. Injection volume was 5 μL. The integration baseline (red) is shown for the raw data. The bottom figure is the Michaelis-Menten plot, fit with the single-injection enzyme kinetics model included in the PEAQ-ITC software. MicroCal PEAQ-ITC software determined that DHapp = -10.9 kcal/mol, kcat = 18.2 s-1 and KM = 2.8 μM. (Data from Malvern Panalytical)
Enzyme kinetics experiments can be performed with the addition of a competitive inhibitor in the ITC cell, using the multiple injection or continuous method. See Figure 5 for continuous assays of the hydrolysis of BAEE by trypsin in the absence or presence of the inhibitor benzamidine. KI was determined to be 15 μM with the PEAQ-ITC data analysis software.
Figure 5: Raw ITC data (Top) and fit data (bottom) for the hydrolysis of BAEE with trypsin, collected on a MicroCal PEAQ-ITC. Black: control experiment, with 4 mM BAEE in the ITC syringe and buffer in the ITC cell; red: 4 mM BAEE in ITC syringe, and 20 nM trypsin in ITC cell (no inhibitor): green: 4 mM BAEE in ITC syringe, and 20 nM trypsin + 95 μM benzamidine (inhibitor) in ITC cell, injection volume was 5 mL. (Data from Malvern Panalytical).
The efficient and accurate measurement of enzyme kinetics is crucial to technical advancement and optimization in life sciences research, drug discovery and development, and across a number of industries. ITC is a powerful and flexible technique for studying molecular interactions and can provide the information needed to generate a full Michaelis-Menten kinetic curve in less than 2 hours. High sensitivity means that sample requirements are low and allow experimentation with very dilute systems. Relative to traditional strategies based on chemical-reducing-end assays, ITC can be faster, less manually intensive and more informative.
