Quantifying the Binding Interactions Between Cu(II) and Peptide Residues in the Presence and Absence of Chromophores

Sohee Choi; Jessica A. San Juan; Marie C. Heffern; Michael J. Stevenson

doi:10.3791/63668

JoVE Journal > Biochemistry

Bioquímica

Quantifying the Binding Interactions Between Cu(II) and Peptide Residues in the Presence and Absence of Chromophores

Published: April 05, 2022

doi:

10.3791/63668

Sohee Choi¹, Jessica A. San Juan², Marie C. Heffern², Michael J. Stevenson¹

¹Department of Chemistry,University of San Francisco, ²Department of Chemistry,University of California, Davis

Summary

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.

Abstract

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.

Introduction

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 (ΔH_ITC). In addition, the stoichiometry of the binding event (n) and the apparent binding affinity (K_ITC) are quantified. From these parameters, the free energy (ΔG_ITC) and entropy (ΔS_ITC) 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.

Protocol

1. Electronic absorption spectroscopy: direct titration with buffer competition

Sample preparation
1. Prepare a buffered solution of 50 mM 2-[bis(2-hydroxyethyl)amino]-2-(hydroxymethyl)propane-1,3-diol (bisTris) at pH 7.4 using ultrapure water (>18 MΩ resistance). Remove trace metal ions by incubating with a high-affinity resin for at least 2 h with subsequent filtration.
2. Dissolve or dilute a known quantity of the peptide into the metal-free buffer.
  NOTE: When monitoring d-d bands with small extinction coefficients²¹, higher concentrations of peptide must be used. Here, the final concentration of C-peptide in buffered solution was 300 µM (volume depends on the size of the cuvette). C-peptide was synthesized by solid-phase peptide synthesis and is detailed elsewhere in literature¹⁴.
3. Dissolve a known mass of CuCl₂ in ultrapure water to make a solution at 10-15 mM.
  NOTE: It is important to initially dissolve the metal salt in nonbuffered water to prevent precipitation. Other Cu(II) salts may be used, but care must be taken to ensure that the anion is weakly coordinating.
Running the experiment
1. Turn on the electronic absorption spectrophotometer and let it warm up for ~15-20 min before use. Launch the spectrophotometer software and configure the parameters such as scanning range (200-900 nm), scan rate (200 nm/s), and double beam baseline-corrected (more parameters are listed in the Supplemental File).
2. Collect a baseline with no cuvettes or samples in the beam paths.
3. Using two matched cuvettes in the double-beam spectrophotometer, load one cuvette with 115 µL of ultrapure water and the other cuvette with 115 µL of the peptide sample. Ensure that there are no air bubbles in the cuvettes as these will interfere with the signal.
4. Place the cuvette with ultrapure water in the reference beam and the cuvette with peptide in the sample beam.
5. Collect the absorption spectrum of the metal-free (apo) peptide.
6. Add in a sub-stoichiometric amount (0.5 equivalents, 150 µM) of the Cu(II) solution into the cuvette with the peptide sample. Ensure that the volume of Cu(II) added is less than 3 µL and record the volume for analysis later.
7. Gently pipette up and down to mix the solution while avoiding the generation of air bubbles. Let the solution react and equilibrate for 5 min and record the absorption spectrum.
8. Repeat the addition of Cu(II) aliquots as in step 1.2.6 into the peptide solution for the following equivalents: 1.0, 1.5, 2.0, 3.0, and 5.0 (or a total of 300, 450, 600, 900, and 1,500 µM). Make sure to record the total volume of Cu(II) added and the total volume of the cuvette.
  NOTE: If more resolution is desired, reduce the spacing between equivalents.
9. Remove the sample cuvette and thoroughly clean as per the manufacturer's directions.
10. Add the buffered solution without peptide and record the absorption spectrum. Repeat the addition of Cu(II) aliquots as in steps 1.2.5-1.2.8, recording the absorption spectrum for each Cu(II) equivalent.
11. Export all spectra as csv files for processing. Thoroughly clean the cuvettes according to the manufacturer's instructions and power down the spectrophotometer.
Processing the data
1. Load all spectra on a spreadsheet program.
2. Subtract the buffer-only (0 µM Cu(II)) spectrum from every other spectrum to remove any absorbance features from the buffer itself.
3. Normalize each spectrum to account for the dilution resulting from the addition of the Cu(II) solution (step 1.2.6). See the Supplemental File, Eq (1)¹⁴ for an example of the normalization where v_initial is the volume (115 µL) of peptide added to the cuvette, v_Cu(II) is the volume of Cu(II) solution added in step 1.2.6, and Abs_{buffer subtracted spectrum} is the data obtained in step 1.2.7.
4. Graph all spectra together to identify regions of change.
  NOTE: Typical d-d bands from Cu(II) complexes range from 500 to 750 nm. This spectrophotometric titration can be challenging due to the small extinction coefficient from d-d bands, which are Laporte-forbidden transitions in octahedral geometry²¹. If the absorbance is too weak, an alternative approach is to utilize chromophoric ligands that result in charge transfer bands upon binding to Cu(II) (see section 2).

2. Electronic absorption spectroscopy: peptide competition with chromophoric ligand

Sample preparation
1. Dissolve 1,10-phenanthroline (phen) in ultrapure water to obtain a final concentration of ~1 mM.
2. In addition to the sample preparation described in section 1.1 for the peptide (step 1.1.2) and Cu(II) (step 1.1.3), prepare a 10 µM Cu(II) and 40 µM phen solution in buffer ([Cu(phen)₃]²⁺). Ensure that the volume fills the cuvette.
Running the experiment
1. Start the electronic absorption spectrophotometer as in steps 1.2.1 and 1.2.2 but set the scanning range to 200-400 nm.
2. In two matched cuvettes, load one cuvette with 115 µL of ultrapure water and the other cuvette with 115 µL of the [Cu(phen)₃]²⁺ solution. Place the cuvette with water in the reference beam and the cuvette with [Cu(phen)₃]²⁺ solution in the sample beam.
3. Collect the absorption spectrum of the metal-ligand complex.
4. Add a stoichiometric amount (≈1 equivalents, ≈10 µM) of peptide to the [Cu(phen)₃]²⁺ solution. Gently pipette up and down to thoroughly mix but be careful not to introduce air bubbles. Record the volume of peptide added for future analysis.
  NOTE: Using cuvettes that hold 115 µL, adding 3.83 µL of 300 µM peptide yields a final peptide concentration of 9.7 µM.
5. Incubate the solution for 5 min to reach equilibrium. Record the absorption spectrum.
  NOTE: If the binding affinity of the metal-peptide and metal-ligand are similar, the concentration of peptide added will need to be in large excess. Be sure to account for the total volume of peptide added for normalization.
6. Repeat the addition of peptide aliquots into the [Cu(phen)₃]²⁺ solution for the following approximate equivalents: 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 22, and 26. Record the volume of peptide added so that the diluted concentration can be determined.
7. Remove the sample and clean the cuvette thoroughly according to the manufacturer's instructions. Collect a spectrum of the buffer. Collect a spectrum of 50 µM peptide in the buffer.
8. Export all data as csv files for processing and clean the cuvettes according to the manufacturer's instructions.
Processing the data
1. Load the spectra on a spreadsheet program and subtract the buffer spectrum from the other spectra.
2. Normalize the spectra following Supplemental File, Eq (2)¹⁴.
3. Using the extinction coefficient for [Cu(phen)₃]²⁺ at 265 nm (ε_max = 90,000 M^-1cm^-1)²², determine the concentration of [Cu(phen)₃]²⁺ with each addition of the peptide.
4. For each addition of the peptide, determine the concentration of the Cu(II)-peptide complex by subtracting the remaining concentration of [Cu(phen)₃]²⁺, as determined in step 2.3.2, from the initial concentration of [Cu(phen)₃]²⁺ (at 0 µM peptide).
5. Calculate the concentration of free phen ligand by the equation in Supplemental File, Eq (3)¹⁴.
6. Calculate the concentration of free peptide using Supplemental File, Eq (4)¹⁴, where [peptide]_stock represents the undiluted peptide titrated into the cuvette, V₁ represents the volume of stock peptide added, V₂ is the total volume of the cuvette, and [Cu²⁺-peptide] is determined in step 2.3.4.
7. Calculate the experimental binding affinity (K_ex) using Eq (5) in Supplemental File²³.
8. Relate the dissociation constant of Cu(II)-peptide to K_ex by Eq (6)²³ de Supplemental File, where K_{d,Cu(II)-phen} = 1.0 × 10^-9 (see ²²). Determine the average and standard deviation from all the determined dissociation constants.
  NOTE: Under a 4:1 ratio of phen:Cu(II), [Cu(phen)₃]²⁺, [Cu(phen)₂]²⁺, and [Cu(phen)]²⁺ exist in the solution, and the peptide will chelate Cu(II) away from the species ([Cu(phen)]²⁺) with the weakest binding²².

3. Isothermal titration calorimetry

Sample preparation
1. Prepare a buffered solution of 15 mM 3-morpholinopropane-1-sulfonic acid (MOPS) at pH 7.4 using ultrapure water (>18 MΩ resistance). Remove trace metal ions by incubating with a high-affinity resin for at least 2 h with subsequent vacuum filtration through a bottle-top 0.45 µm membrane.
2. Dissolve a known mass of CuCl₂ in ultrapure water to prepare a solution of ≈50-100 mM. Dilute this Cu(II) solution into buffer to obtain a 1.0 mL solution with final concentration of 1.4 mM Cu(II). Record the exact volume of the CuCl₂ solution used.
3. Dissolve or dilute the peptide solution in buffer to make 450 µL of a 154 µM peptide solution. Ensure that the same proportion of additional ultrapure water from step 3.1.2 is added to the peptide solution, which will reduce heat of dilution and increase signal-to-noise.
4. After preparation of the samples, ensure that the solutions are at the same pH and, if needed, adjust accordingly.
5. Optional step: Degas the solutions to minimize microbubbles loaded into the ITC.
Running the experiment
1. Turn on the ITC. Launch the ITC software to run the instrument. Wait for the first initialization, which will ask to rehome the buret; then, follow the instructions on the screen.
2. Remove the cover from the reference cell. Remove any water from the reference cell and rinse three times with 450 µL of degassed ultrapure water.
3. Slowly draw up ultrapure water to the 450 µL mark of a loading syringe, taking care not to introduce air bubbles into the syringe. Insert the loading syringe into the reference cell until it is ≈1 mm from the bottom, and slowly inject part of the solution until 150 µL remains in the loading syringe. Move the loading syringe plunger quickly up and down by ≈25 µL several times to dislodge any bubbles on the cell surface. Slowly inject until the plunger reaches the 100 µL mark on the loading syringe, thereby dispensing a total of 350 µL of ultrapure water into the reference cell, and replace the reference cell cover.
4. Remove any residual solution from the sample cell and load with 450 µL of 10 mM ethylenediaminetetraacetic acid (EDTA) using a loading syringe. Soak for 10 min to ensure that trace metal ions are removed because the EDTA will bind trace metals.
5. Remove the EDTA solution (with bound trace metal ions) and thoroughly rinse the loading syringe with copious amounts of ultrapure water.
6. Clean the ITC in accordance with the manufacturer's directions, rinsing the sample cell with ultrapure water.
7. Condition the sample cell by rinsing with 450 µL of buffer at least three times.
8. Remove the buffer that is conditioning the sample cell. Load the peptide solution into the sample cell using the loading syringe (follow step 3.2.3).
9. Rinse the titration syringe with 200 µL of buffered solution. To do this, remove the plunger and use a micropipette to pipette the buffer through the hole at the top of the glass titration syringe, through the syringe, and out the needle below.
10. Fully insert the plunger into the titration syringe.
11. Dip the tip of the titration syringe needle into the metal solution and slowly pull the plunger up, causing the metal solution to fill the syringe and result in a void volume at the top of the glass part of the titration syringe. Remove most of the void volume by rotating the titration syringe parallel to the floor, remove the plunger, and slightly tilt the glass part toward the floor. Give the titration syringe a gentle shake so that the solution moves to the end of the glass part of the titration syringe and fills most of the void volume, but ensure that 2-3 µL of void volume remains. While keeping the syringe parallel to the floor, reinsert the plunger.
12. Hold the titration syringe upright, dip the tip of the needle back into the metal solution, and push the plunger down until air ceases to come out of the needle. Load the titration syringe by slowly pulling up the plunger to just above the 50 µL mark while keeping the tip of the needle in the solution.
13. Carefully insert the glass part of the titration syringe into the buret and screw until finger-tight. When a small amount of solution comes out of the titration syringe due to compression of the plunger, use a light-duty delicate wiper to carefully absorb the solution without touching the tip of the needle.
14. Insert the buret with titration syringe into the sample cell and fasten it securely.
15. Set up the parameters on the ITC software. Starting on Instrument Control, set the stirring rate (typical stirring rates range from 150 to 350 RPM) and the temperature at which the experiment will be conducted (typically 25 °C). Enter the syringe and cell concentrations de millimolar units under Experiment Details.
16. In the Experiment Method section, select Incremental Titration. Click on Setup and specify 20 injections of 2.5 µL. If more resolution is required to observe the binding event, increase the number of injections and decrease the volume per injection. Input the time spacing between each injection so that it is long enough for the signal to equilibrate and return to baseline, typically 300 s.
17. Click the run button to start the experiment and specify where the data are saved.
18. Upon completion of the experiment, clean the sample cell and titration syringe.
19. Run all experiments in at least triplicate to ensure precise data collection.
20. Run a control experiment where the metal solution is titrated into the buffered solution (in the absence of peptide) to ensure the heat of dilution from the metal ion is small and that there are no unaccounted-for equilibria. If the heat of dilution is large, consider a different buffer system, if possible.
Processing the data
1. Launch the ITC analysis software and load the data file for analysis.
2. Navigate to the Baseline tab and inspect the thermogram. Note any exogenous heat evolved or absorbed from air bubbles or other artifacts in the thermogram. Look for spikes that are not due to injection of the metal solution.
3. Ensure that the baseline generated by the analysis software follows the portion of the data after injection and equilibration. If it deviates, use Baseline Pivot Points to adjust the baseline. Ensure that the Integration Regions include the peak generated from injection of the metal, but occlude any air bubbles or artifacts in the thermogram found in step 2. Subtract the baseline from the thermogram.
  NOTE: This type of manipulation is recommended only after multiple thermograms are collected, so the experimenter knows what is real data and what is an artifact, as adjusting the baseline and Integration Regions may drastically affect the data.
4. Navigate to the Modeling window to begin fitting the data where the analysis software will show the integrated and concentration-normalized data for each injection.
5. Left-click on the datum of the first injection to remove it from the fitting algorithm.
  NOTE: This is common since there will be minor mixing between the titrant and sample cell solution leading to inexact molar dispensing of the first injection.
6. In the Models section, select Blank (constant) in the Style dropdown menu, which is based on the final injection enthalpy and will be subtracted from every data point, accounting for the heat of dilution. In addition, select Independent (or the best model for the system) in the second Style dropdown menu to fit the data.
  NOTE: For standard 1:1 binding interactions, the most common model is Independent.
7. Fit the data using the two models by pressing the green play button with a Σ.
  NOTE: Another software to process ITC data is SEDPHAT²⁴.
8. Account for all competing equilibria in post hoc analysis previously reported by Grossoehme et al.²⁰.

Representative Results

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 K_Cu(II)-MOPS < K_{Cu(II)-bis-Tris}¹⁶^,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 ΔH_ITC. 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 K_ITC. 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, K_{Cu(II)/C-peptide} = 1 (± 1) x 10⁸ and ΔH_{Cu(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 ΔS_{Cu(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; K_d,ITC = 6 (± 3) x 10^-7; ΔH_ITC = −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]	K_ex (x10⁸)	K_d^{Cu(II)/C-peptide}	log K_d^{Cu(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
			ΔH_ITC= Χ + 3.40
			Χ = ΔH_ITC − 3.40
			Χ = ΔH_{Cu(II)/C-peptide} = −6.8 kJ mol^-1

Table 2: Post hoc analysis to determine ΔH_{Cu(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, ΔH_{Cu(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 (ΔH_ITC + ΔH_{Cu(II)-Buffer}) vs ΔH_Buffer-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.

Discussion

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 K_ITC can be accurately determined by the fitting algorithm. The c-value is defined in Supplemental File, Eq (7)²⁰, where n is stoichiometry, K_ITC 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 K_ITC is accurate. However, c-values below 1 may only provide an upper limit to K_ITC, 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.

Declarações

The authors have nothing to disclose.

Acknowledgements

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).

Materials

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

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Choi, S., San Juan, J. A., Heffern, M. C., Stevenson, M. J. Quantifying the Binding Interactions Between Cu(II) and Peptide Residues in the Presence and Absence of Chromophores. J. Vis. Exp. (182), e63668, doi:10.3791/63668 (2022).