This article focuses on the use of electronic absorption spectroscopy and isothermal titration calorimetry to probe and quantify the thermodynamics of Cu(II) binding to peptides and proteins.
Copper(II) is an essential metal in biological systems, conferring unique chemical properties to the biomolecules with which it interacts. It has been reported to directly bind to a variety of peptides and play both necessary and pathological roles ranging from mediating structure to electron transfer properties to imparting catalytic function. Quantifying the binding affinity and thermodynamics of these Cu(II)-peptide complexes in vitro provides insight into the thermodynamic driving force of binding, potential competitions between different metal ions for the peptide or between different peptides for Cu(II), and the prevalence of the Cu(II)-peptide complex in vivo. However, quantifying the binding thermodynamics can be challenging due to a myriad of factors, including accounting for all competing equilibria within a titration experiment, especially in cases where there are a lack of discrete spectroscopic handles representing the peptide, the d-block metal ion, and their interactions.
Here, a robust set of experiments is provided for the accurate quantification of Cu(II)-peptide thermodynamics. This article focuses on the use of electronic absorption spectroscopy in the presence and absence of chromophoric ligands to provide the needed spectroscopic handle on Cu(II) and the use of label-free isothermal titration calorimetry. In both experimental techniques, a process is described to account for all competing equilibria. While the focus of this article is on Cu(II), the described set of experiments can apply beyond Cu(II)-peptide interactions, and provide a framework for accurate quantification of other metal-peptide systems under physiologically relevant conditions.
Biology has evolved to utilize the diverse chemistry of metal ions needed for life to adapt and survive in its surrounding environment. An estimated 25%-50% of proteins use metal ions for structure and function1. The particular role and redox state of the metal ion is directly related to the composition and geometry of the biological ligands that coordinate it. In addition, redox-active metal ions such as Cu(II) must be tightly regulated lest they interact with oxidizing agents via Fenton-like chemistry to form reactive oxygen species (ROS)2,3,4. Understanding the binding modes and affinity that drive its biochemistry should help elucidate the biological role of the metal ion.
Many techniques are used to study the binding interactions of metals and peptides. These are mostly spectroscopic techniques but also include computer simulations using molecular dynamics, as seen through Cu(II) interactions with a fragment of amyloid beta (Aβ)5. A widely used spectroscopic technique that is accessible to many universities is nuclear magnetic resonance (NMR). By using the paramagnetic nature of Cu(II), Gaggelli et al. were able to show where the metal ion binds on a petide through relaxation of nearby nuclei6. Electron paramagnetic resonance (EPR) may also be utilized to probe the location and mode of the paramagnetic metal ion binding7. Other spectroscopic techniques such as circular dichroism (CD) can describe the coordination about Cu(II) in systems such as tripeptide systems8, and mass spectrometry can show stoichiometry and to which residues the metal ion is coordinated through fragmentation patterns9,10.
Some of these techniques, such as NMR, are label-free but require large concentrations of peptide, posing challenges for study. Another common technique called fluorescence spectroscopy has been utilized to relate the position of a tyrosine or tryptophan with quenching from a Cu(II)11,12. Similarly, this technique can show structural changes as a result of Cu(II) binding13. However, challenges with these metal-peptide binding studies are that they probe chromophoric amino acids such as tyrosine which not all systems have, that the metal ion binds under a classical model, and that the technique may not be conducive under physiological conditions. Indeed, several peptides are emerging that do not contain such chromophoric amino acids or bind under classical models, precluding the use of these techniques14,15. This article details approaches for assessing binding properties in these scenarios under physiologically relevant conditions.
Biological ligands may adopt different protonation states that can affect metal ion binding such as the imidazole ring on histidine. If pH is not consistently maintained, the results can be convoluted or conflicting. For this reason, buffers are an essential component in the study of metal-protein/peptide interactions. However, many buffers have been shown to favorably interact with metal ions16,17. In addition to competing with the biological molecule of interest, the buffer may have similar coordinating atoms that may be difficult to distinguish from the coordinating atoms of the peptide or protein. In this study, the focus is on electronic absorption spectroscopy and isothermal titration calorimetry (ITC) as two complementary techniques for studying Cu(II)-peptide interactions, with special considerations concerning buffer choice.
Electronic absorption spectroscopy is a rapid, widely accessible technique for studying metal-binding interactions. Irradiation with light in the ultraviolet (UV) or visible wavelengths can lead to absorption of metal-centered d-d bands, which provide valuable information on ligand classification, metal geometries, and apparent binding affinities18,19. For these complexes, direct titrations of metal ions into protein or peptide solutions can quantify binding stoichiometries and apparent binding affinities. In some cases, such as d5 or d10 electron configurations, the complex does not absorb light (i.e., is spectroscopically silent). In these spectroscopically silent transition metal complexes, these limitations can be circumvented by using a competing ligand that, upon coordinating to the metal ion, yields detectable charge transfer bands. In either case, this approach is limited to quantifying only stoichiometry and apparent binding affinity, and no insight into binding enthalpy is provided without approximations.
Complementing information obtained from electronic absorption spectroscopy, ITC is an attractive technique for direct and rigorous quantification of the binding enthalpy20. ITC directly measures the heat released or consumed during a binding event and, since the titration takes place at constant pressure, the heat measured is the enthalpy of all equilibria (ΔHITC). In addition, the stoichiometry of the binding event (n) and the apparent binding affinity (KITC) are quantified. From these parameters, the free energy (ΔGITC) and entropy (ΔSITC) are determined, providing a thermodynamic snapshot of the binding event. As it does not rely on light absorption, ITC is an ideal technique for spectroscopically silent species, for example, d5 or d10 metal ion complexes. However, since calorimetry measures heat, any unmatched buffer systems and unaccounted-for equilibria may adversely affect the analysis to accurately determine the metal ion binding thermodynamics, and great care must be taken to address these factors20. If performed with the appropriate rigor, ITC is a robust technique for determining the thermodynamics of metal-protein/peptide complexes.
Here, a chromophorically silent copper-binding peptide, C-peptide, is used to demonstrate the complementary use of the two techniques. C-peptide is a 31 residue cleavage product (EAEDLQVGQVELGGGPGAGSLQPLALEGSLQ) formed during insulin maturation; it lacks chromophoric residues but has been shown to bind Cu(II) with physiologically relevant affinity14,15. The Cu(II) binding site consists of the side chains of a glutamate and an aspartate as well as the N-terminus of the peptide14,15. These coordinating atoms closely resemble those of many commonly used buffered systems. Here, the tandem use of the d-d and charge transfer bands in electronic absorption spectroscopy and ITC in quantifying the Cu(II) binding thermodynamics to C-peptide is shown. The approach from studying Cu(II) binding to C-peptide can be applied to other metal ions and protein/peptide systems.
1. Electronic absorption spectroscopy: direct titration with buffer competition
2. Electronic absorption spectroscopy: peptide competition with chromophoric ligand
3. Isothermal titration calorimetry
The goal was to quantify and corroborate the thermodynamics of Cu(II) binding to C-peptide using the complementary techniques of electronic absorption spectroscopy and ITC. Due to the robust nature of electronic absorption spectroscopy, a direct titration of Cu(II) into 300 µM C-peptide was performed (Figure 1). Addition of 150 µM of Cu(II) caused an immediate increase in the band at 600 nm, attributed to the d-d band of Cu(II), and continued to increase until 300 µM Cu(II) was added. Further addition above 300 µM Cu(II) did not increase the absorption of the d-d band, indicating saturation and that Cu(II) binds to C-peptide in a 1:1 complex. Furthermore, due to the relatively high affinity of bis-Tris for binding to Cu(II) (log K = 5.27)16, the Cu(II)/C-peptide affinity should have a lower limit in the micromolar range (log K > 6) to chelate the metal ion away from the buffer. In this system, precise quantification of the absorption bands is challenging due to the small extinction coefficient.
To circumvent the small extinction coefficient of the d-d band, a chromophoric ligand, phen, was used as described above. C-peptide was titrated into ≈10 µM [Cu(phen)3]2+ (Figure 2A), and the absorption from the charge transfer band at 265 nm decreased (Figure 2B), indicating that C-peptide was able to chelate Cu(II) from the phen ligand. Upon closer inspection, a large concentration of C-peptide (up to 140 µM) was needed to modestly outcompete the phen ligand (Table 1). This is not surprising given the large sequential formation constants for [Cu(phen)3]2+ (9.0, 15.7, and 20.8 for log K1, β2, and β3, respectively)22. Analysis of the spectra show that the Cu(II)/C-peptide binding affinity is in the range of log K = 7.4-7.8 (Table 1).
The gold standard of measuring the complete thermodynamics of a binding interaction is ITC. Figure 3 provides a representative thermogram of Cu(II) titrated into C-peptide in 15 mM MOPS, pH 7.4, which provides the competition for the Cu(II). Here, MOPS buffer was used instead of bis-Tris buffer because KCu(II)-MOPS < KCu(II)-bis-Tris16,25, and would provide less competition so that the ITC can accurately measure the affinity (see discussion). Through measuring the heat consumed or evolved among all equilibria, the thermogram provides a sigmoidal shape. Initial injections of Cu(II) before the peptide is saturated result in large amounts of heat compared to the heat of dilution. The difference in these values of heat are ΔHITC. The inflection point describes two useful pieces of information. The first is the binding stoichiometry of the species in the syringe compared to the species in the cell (i.e., Cu(II):C-peptide is 1:1). Second, the slope of the fit at the inflection point is directly proportional to KITC. After collecting data in triplicate and in multiple buffers, a post hoc analysis20 where all competing equilibria are accounted for was performed (Table 2), and the buffer-independent thermodynamics are determined. For Cu(II) binding to C-peptide, KCu(II)/C-peptide = 1 (± 1) x 108 and ΔHCu(II)/C-peptide = -8 (± 4) kJ mol-1. From these, other binding thermodynamic parameters are determined where ΔG°Cu(II)/C-peptide = -46 (± 4) kJ mol-1 and ΔSCu(II)/C-peptide = 120 (± 10) J mol-1 K-1 (see 15).
Figure 1: Monitoring of the Cu(II) d-d band upon addition of 150, 300, 450, 600, 900, and 1,500 µM Cu(II) to 300 µM C-peptide in 50 mM bis-Tris, pH 7.4. The d-d band of Cu(II) increases at 600 nm until a 1:1 Cu(II):C-peptide complex is formed. Previously, Magyar and Godwin reported that Cu(II)-bis-Tris affinity is log K = 5.2716. This titration shows that C-peptide can outcompete the buffer to bind Cu(II) and indicates a Cu(II)/C-peptide affinity in the micromolar range. This figure is reprinted with permission from Stevenson et al.14. Please click here to view a larger version of this figure.
Figure 2: Formation of [Cu(phen)3]2+ and representative electronic absorption spectra monitoring the decrease of the charge transfer band upon addition of C-peptide. (A) The reaction scheme showing the formation of [Cu(phen)3]2+ and its charge transfer band centered at 265 nm (ε° ≈ 90,000 M-1 cm-1)22. The formation constants are 9.0, 15.7, and 20.8 for log K1, β2, and β3, respectively22. (B) Representative electronic absorption spectra monitoring the decrease of charge transfer band from [Cu(phen)3]2+ as C-peptide chelates Cu(II). The titration was conducted in 50 mM bis-Tris, pH 7.4. Data analysis is shown in Table 1. This figure is reprinted with permission from Stevenson et al.14. Abbreviation: MLCT = metal to ligand charge transfer. Please click here to view a larger version of this figure.
Figure 3: Representative thermograms of Cu(II) titrated into C-peptide and buffer. (A) This representative thermogram shows the titration of 1.4 mM Cu(II) into 154 µM C-peptide in 15 mM MOPS, pH 7.4. The data are fit to a one-site model with the following parameters: n = 1.2 ± 0.1; Kd,ITC = 6 (± 3) x 10-7; ΔHITC = −3.4 ± 0.2 kJ mol-1. (B) A representative control titration of 1.4 mM Cu(II) into 15 mM MOPS, pH 7.4, in the absence of C-peptide showing no binding thermogram which is seen in panel A. Please click here to view a larger version of this figure.
[C-peptide] | A265 | [Cu(phen)3]2+ | [phen]free | [C-peptide]free | [Cu(II)/C-peptide] | Kex (x108) | KdCu(II)/C-peptide | log KdCu(II)/C-peptide | |
0.0 | 1.0649 | 11.83 | 4.5 | 0 | 0.00 | ND | ND | ND | |
9.7 | 0.9948 | 11.05 | 6.84 | 7.45 | 0.78 | 0.0543 | 1.84E-08 | 7.75 | |
18.7 | 0.9785 | 10.87 | 7.38 | 16.00 | 0.96 | 0.0367 | 2.73E-08 | 7.56 | |
27.3 | 0.9651 | 10.72 | 7.83 | 24.09 | 1.11 | 0.0310 | 3.23E-08 | 7.49 | |
35.3 | 0.9475 | 10.53 | 8.42 | 31.55 | 1.30 | 0.0307 | 3.26E-08 | 7.49 | |
42.8 | 0.9374 | 10.42 | 8.75 | 38.79 | 1.42 | 0.0288 | 3.48E-08 | 7.46 | |
50.0 | 0.9283 | 10.31 | 9.06 | 45.64 | 1.52 | 0.0275 | 3.63E-08 | 7.44 | |
56.7 | 0.9134 | 10.15 | 9.55 | 51.92 | 1.68 | 0.0288 | 3.47E-08 | 7.46 | |
63.1 | 0.9025 | 10.03 | 9.92 | 57.97 | 1.81 | 0.0291 | 3.43E-08 | 7.46 | |
69.2 | 0.8927 | 9.92 | 10.24 | 63.73 | 1.91 | 0.0294 | 3.40E-08 | 7.47 | |
75.0 | 0.878 | 9.76 | 10.73 | 69.04 | 2.08 | 0.0314 | 3.19E-08 | 7.50 | |
85.7 | 0.8542 | 9.49 | 11.53 | 78.99 | 2.34 | 0.0341 | 2.93E-08 | 7.53 | |
95.4 | 0.8316 | 9.24 | 12.28 | 88.01 | 2.59 | 0.0371 | 2.69E-08 | 7.57 | |
104.3 | 0.8151 | 9.06 | 12.83 | 96.38 | 2.78 | 0.0387 | 2.58E-08 | 7.59 | |
112.4 | 0.7976 | 8.86 | 13.41 | 103.98 | 2.97 | 0.0411 | 2.44E-08 | 7.61 | |
126.9 | 0.7809 | 8.68 | 13.97 | 117.87 | 3.16 | 0.0411 | 2.44E-08 | 7.61 | |
139.2 | 0.7586 | 8.43 | 14.71 | 129.53 | 3.40 | 0.0437 | 2.29E-08 | 7.64 | |
Range | 7.4-7.8 |
Table 1: Representative calculations for the concentrations of species in solution from Figure 2B. All concentrations are in micromolar. This table is reprinted with permission from Stevenson et al.14.
Equlibria | n | ΔH (kJ mol-1) | n × ΔH (kJ mol-1) |
Cu(II)-MOPS → Cu(II) + MOPS | 1 | 5.4 | 5.44 |
MOPS + H+ → MOPS-H+ | 0.097 | −21.0 | −2.04 |
Cu(II) + C-peptide-H+ → Cu(II)/C-peptide + H+ | 1 | X | X |
ΔHITC = Χ + 3.40 | |||
Χ = ΔHITC − 3.40 | |||
Χ = ΔHCu(II)/C-peptide = −6.8 kJ mol-1 |
Table 2: Post hoc analysis to determine ΔHCu(II)/C-peptide. As shown in Figure 3,1.4 mM Cu(II) was titrated into 154 µM C-peptide in 15 mM MOPS, pH 7.4, in triplicate. After accounting for all competing equilibria shown in the table, ΔHCu(II)/C-peptide is found to be −8.66 kJ mol-1. The number of protons displaced from C-peptide by Cu(II) is determined from the slope of (ΔHITC + ΔHCu(II)-Buffer) vs ΔHBuffer-H where data are collected in multiple buffers. All enthalpy values are found in NIST25 or determined elsewhere in literature15. All competing equilibria are accounted for as described by Grossoehme et al.20 This figure is reprinted with permission from Stevenson et al.15.
Supplemental File: Relevant equations used in the protocol section and additional parameters for electronic absorption spectrophotometer and ITC setup. Please click here to download this File.
This article provides a robust method for quantifying the affinity and thermodynamics of Cu(II) binding to peptides. Complexes with Cu(II) are ideally suited to monitor the d-d absorption band at the metal site due to its d9 electron configuration. Although the extinction coefficient is small, thus requiring larger concentrations of the complex to yield a reliable signal, titrations of Cu(II) into peptide can quickly provide insight into the binding stoichiometry and approximate binding affinity. However, it can be challenging to discern a difference in spectra if the metal is coordinated by similar atoms and geometries from both buffer and peptide. To determine the binding affinity quantitatively, chromophoric ligands such as phen are often used due to their symmetry-allowed charge transfers with metal ions21. Through setting up a competition between a chromophoric ligand and a nonchromophoric peptide and by knowing the affinity of the metal to the ligand, the metal-peptide affinity can directly be determined. Further thermodynamic analysis using electronic absorption spectroscopy is challenging. To quantify the binding enthalpy using a technique such as UV-Vis spectroscopy, the binding affinity must be determined at multiple temperatures and a van't Hoff analysis performed. This analysis assumes that the specific heat of binding is zero, which is rarely true and thus only provides an estimation of the binding enthalpy20.
Isothermal titration calorimetry is better suited to elucidate a more complete overview of the thermodynamics of metal-peptide interactions. In this technique, a metal ion is titrated into the peptide solution and the heat of various equilibria are directly measured. Since the experiment is conducted at constant pressure, the heat measured by the ITC is equal to enthalpy. ITC can overcome some of the limitations of electronic absorption spectroscopy such as studying spectroscopically silent metal ions and not needing to make the assumptions as done in the van't Hoff analysis. However, sometimes, the reactions taking place in the ITC generate little to no heat and are therefore not observed. This can be bypassed through either increasing concentrations of metal and peptide or by using a different buffer with a different enthalpy of protonation. The latter is especially useful if the metal ion displaces multiple protons upon binding to the peptide. However, both techniques — ITC and electronic absorption spectroscopy — provide orthogonal methods to study the same system and complement each other well.
There are many challenging aspects in both techniques when studying metal ions binding to peptides. Many metal ions are insoluble or sparingly soluble in water. This is further exacerbated by precipitation of the metal ion when added to buffer. In the ITC, this can be manifested by a slow, gradual shift in the baseline and large amounts of heat generated when the metal is injected. This is indicative of large values of heat of dilution even after the peptide would be saturated by metal ion. In electronic absorption spectroscopy, metal ion precipitation manifests itself through increased absorption at lower wavelengths and resembles a broad peak centered in the UV region. In both techniques, the experimenter must ensure that the metal ion is soluble under the conditions of choice (e.g., buffer, pH, temperature, concentration). Another limitation to both methods can show itself through the concept of addition order. In some cases, one metal complex formed may be labile, while adding the same reagents in a different order would make another intermediate species inert. This illustration of kinetic versus thermodynamic control hinders accurate analysis of all competing equilibria.
Both ITC and electronic absorption spectroscopy rely on competitions between the peptide and either buffer or ligand for binding the metal ion. Here, the c-value is introduced, which helps to identify if KITC can be accurately determined by the fitting algorithm. The c-value is defined in Supplemental File, Eq (7)20, where n is stoichiometry, KITC is the apparent binding affinity, and [sample cell] is the concentration of the species in the sample cell. When the c-value is between 1 and 1,000, the determined KITC is accurate. However, c-values below 1 may only provide an upper limit to KITC, whereas c-values above 1,000 may only provide a lower limit20. This is the value of collecting data from complementary techniques: if something is missed in one technique due to its limitations, it may be caught in its complementary technique. In these experiments, if the competition is greatly favored to one species (i.e., the metal ion affinity of the peptide is much greater than the affinity of the buffer), the equilibria will be shifted, making it difficult to interpret. This is also shown in detail by Kocyla et al.23. To work around this challenge, introduction of or replacement with a different competing ligand, or choosing a different buffer is often used to increase the competition between ligand and peptide for accurate quantification. This is because the difference of metal-buffer (or metal-ligand) and metal-peptide affinities would allow for the c-value to fall within the window of 1 to 1,000. It should be noted, however, that the use of a competing ligand or another buffer for quantitative analysis requires that the thermodynamic parameters of the metal-ligand or metal-buffer complex are known or can be determined by other means.
Finally, accurately quantifying the peptide concentration can be challenging. Some common ways to quantify peptide concentrations are to measure specific amino acids and relate the quantity of those amino acids to the sequence of the peptide. For instance, electronic absorption spectroscopy can measure aromatic residues such as tyrosine, Ellman's reagent can quantify the number of free thiols26, and Bradford assays provide an indication of how many residues are present27. Unfortunately, peptides such as the one used in this study do not have aromatic residues or free thiols, and Bradford assays pose challenges when comparing standards from massive proteins with the small peptide of interest. Instead, the peptide concentration was determined by dry mass. Although not ideal and prone to concentrations that are slight overestimations of peptide concentration (as the apparent mass measurements are likely higher than actual peptide masses due to the presence of salts), all aliquots for measurement were treated the same, which allows for more accurate comparisons. In the spectroscopic studies, if the concentration of free peptide is lower than anticipated, the calculated binding affinity represents a lower limit (Supplemental File, Eq (5) and Eq (6)). There are challenges associated with both techniques shown, but with literature as a guide, many can be overcome.
The experimental approach detailed here allows for accurate quantification of metal-peptide binding thermodynamics. Both ITC and electronic absorption spectroscopy are orthogonal and provide insight into the binding affinity, but ITC allows for direct enthalpy quantification. Once these thermodynamics are quantified, it can be inferred if these metal-peptide complexes can form under physiological conditions. As understanding of physiological metal ion flux grows, the prediction whether the binding affinity is strong enough for complex formation in vivo can be made. Furthermore, the researcher can make predictions about competitions between multiple metal ions for the same peptide or multiple peptides for the same metal ion. Looking at these competitions between metal ion and peptides, the researcher can start to understand what contributes to the binding affinity of the metal-peptide complex by parsing out the free energy into enthalpy and entropy contributions. Thermodynamics are an integral part of appreciating the dynamic interplay between metal ions and peptides, and ITC and electronic absorption spectroscopy are providing handles to interrogate these systems. The methods described here can be expanded and used to study other metal ion binding interactions with macromolecules, and may have implications in physiological processes, drug design, and more.
The authors have nothing to disclose.
SC thanks the Whitehead Summer Research Fellowship. MJS thanks the Startup Funds and the Faculty Development Fund at the University of San Francisco. MCH acknowledges funding from the National Institutes of Health (NIH MIRA 5R35GM133684-02) and the National Science Foundation (NSF CAREER 2048265).
1,10-phenanthroline | Sigma Aldrich | 131377-25G | |
bis-Tris buffer | Fisher | BP301-100 | |
Bottle-top 0.45 micron membrane | Nalgene | 296-4545 | Any filtration system that removes the resin without introducing contaminants is acceptable |
Copper(II) chloride | Alfa Aesar | 12458 | |
EDTA | Sigma Aldrich | EDS-500G | |
Electronic absorption spectrophotometer | Varian | Cary 5000 | Another suitable sensitive spectrophotometer is acceptable |
high affinity resin | Sigma Aldrich | C7901-25G | |
Isothermal titration calorimeter (ITC) | TA Instruments | Nano ITC Low Volume | |
ITC analysis software | TA Instruments | NanoAnalyze | SEDPHAT (Methods. 2015, 76: 137–148) may also be used |
ITC software | TA Instruments | ITCRun | |
light-duty delicate wiper | Kimwipe | 34155 | |
loading syringe | Hamilton | Syr 500 uL, 1750 TLL-SAL | |
matched cuvettes | Starna Cells, Inc | 16.100-Q-10/Z20 | Ensure that the window for the small volume cuvette matches the beam height of the spectrophotometer |
MOPS buffer | Alfa Aesar | A12914 | |
spectrophotometer software | Cary | WinUV Scan | |
spreadsheet program | Microsoft | Excel | Any suitable spreadsheet program will work |
titration syringe | TA Instruments | 5346 | |
ultrapure water | Millipore Sigma | Milli-Q | Any water is okay as long as >18 MΩ resistance |