In the Laboratory
Investigation of Cu(II) Binding to Bovine Serum Albumin by Potentiometry with an Ion Selective Electrode
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A Biophysical Chemistry Experiment for the Undergraduate Curriculum Jie Liu Department of Chemistry, Nantong Medical College, Nantong, Jiangsu, 226001, China;
[email protected] Copper is an essential trace element to living organisms and performs many fundamental physiological functions. It is also involved in the active sites of metalloproteins and metalloenzymes found in the human body. The study of copper metabolism in the body contributes to an understanding of copper-related diseases. Since serum albumin is the major protein component of blood and assumes a versatile transport function, the complex of Cu(II) with serum albumin has been extensively investigated and considered an intermediate form of copper transport in the blood (1). The amino acid sequences of human serum albumin (HSA) and bovine serum albumin (BSA) have been compared and a striking homology has been found. The differences are mainly of a structurally conservative nature (2). The overall trend of ligand binding is thus considered similar for human and bovine serum albumin (3). Since BSA preparation is more available and cheaper than HSA preparation, an investigation of ligand binding to BSA is examined and the results used to gain insight to the similar binding to HSA. Copper ion binding to BSA has been extensively studied over the past decades. An abundance of biochemical information is available that involves not only the chemical equilibrium data (4), but also the molecular aspects related to binding sites, from an inorganic biochemist’s viewpoint. The latter was often explored by means of physical methods, such as circular dichroism, electron spin resonance, or UV– vis absorption spectroscopy (5–7). The interaction of ligands with biological macromolecules is an important topic in the biophysical chemistry curriculum. To assist undergraduate students in gaining better understanding of this topic, we have developed a laboratory project that investigates Cu(II) binding to BSA in an aqueous solution. Biochemistry students with background in instrumental analysis can use potentiometry with an ion selective electrode to determine the chemical equilibrium data and the binding parameters of Cu(II) binding to BSA, including the average number of Cu(II) binding sites on a BSA molecule and the values of site-related binding constants.
ing Cu(II) binding sites. Each class contains an ni (i = 1, 2) number of individual sites that have the same affinity for the Cu(II) ion, characterized by the corresponding binding constant, Ki (i = 1, 2). There is a significant difference in affinities to Cu(II) between two classes, and the Ki values associated with them may differ more than 100-fold. In other words, one class of higher affinity sites and another class of lower affinity sites exist simultaneously in a BSA molecule for binding Cu(II). The binding constants are often written in terms of equilibrium concentrations, instead of activities, of relevant species provided that the activity coefficients can be reasonably assumed constant by means of an ionic medium in the experiment. An average number of Cu(II) ions bound per molecule of BSA, r, can be represented as, r =
C T,Cu(II) − C F,Cu(II) C T,BSA
(1)
where CT,Cu(II) and CT,BSA are the total concentrations, units of M, for Cu(II) and BSA, respectively, and CF,Cu(II) is the free (unbound) Cu(II) ion concentration (i.e., equilibrium concentration). The term, CT,Cu(II) − CF,Cu(II), represents the bound Cu(II) ion concentration. An application of the chemical equilibrium principle to the binding system yields the binding equation: r =
n1K1C F,Cu(II) n2 K 2 C F,Cu(II) + 1 + K1C F,Cu(II) 1 + K 2 C F,Cu(II)
(2)
The binding parameters, ni (i = 1, 2) and Ki (i = 1, 2), are evaluated by nonlinear fitting of the experimental data to eq 2. A graphic method following transformations of the data according to the Scatchard model (11) can also be used; that is, making a plot of r兾CF,Cu(II) versus r. Consequently, binding studies always focus on determination of the free or bound Cu(II) ion concentrations followed by calculation of the values of r. Ion Selective Electrodes
Theory The binding reaction of Cu(II) ions and BSA is + − pCu2 + qH + + rA y
CupHq A r(2p+q−yr) +
where p, q, r are positive integral numbers and y is the charge of Ay ᎑, the anion of BSA. As previous studies indicate (8– 10), the resultant complexes are usually of formula CupHq A (the charge is omitted). Under certain conditions, BSA exhibits two distinct classes of independent and noninteractwww.JCE.DivCHED.org
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Many analytical techniques have been developed for binding studies. Some traditional techniques such as equilibrium dialysis or gel filtration permit the quantity of free or bound ligands, respectively, to be determined after a physical separation of the two fractions following the binding equilibrium. These techniques are not suitable for the undergraduate laboratory owing to the complexity of the assemblies and the time-consuming procedures. In contrast, this laboratory project employs a simple method that requires no separation procedure.
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In the Laboratory 14 12 10 8
r
Ion selective electrodes (ISEs) are potentiometric chemical sensors that respond directly, selectively, and continuously to the concentration (strictly, activity) of the free ion of interest in aqueous solution. The theory and use of ISEs (and potentiometry) are described in analytical chemistry books (12). Because of their inherent advantages, ISEs have found many applications in a variety of fields, including industrial and biomedical areas (13–14). This experiment is based on the ISE technique because a Cu(II) ion selective electrode is available to directly monitor the free Cu(II) concentration, CF,Cu(II), in the presence of the bound Cu(II) and free macromolecule without a need for separation processes and without any disturbance of the binding equilibrium. The ISE technique has additional advantages, such as low cost, sufficient sensitivity and selectivity, wide analytical range of the analyte concentration, insensitivity to optical interferences, and simplicity in assembly. It is these features that make our design suitable for an undergraduate laboratory.
6 4 2 0 0
15
30
45
60
C F,Cu(II) / (10
ⴚ5
75
mol L
90
105
120
ⴚ1
)
Figure 1. The data for the interaction of Cu(II) ions with BSA fitted by nonlinear regression with the GraphPad Prism: n1 = 1.11; n2 = 14.1; K1 = 8.45 × 105, K2 = 2.56 × 103, R = .9986.
Experiment
Materials and Apparatus All chemicals are analytical reagent grade. Deionized distilled water is used for solution preparation. The buffer solution is composed of boric acid, borate, and mannitol with a pH of 5.02 and an ion intensity of 0.5 M. It serves as the electrolyte in potentiometric experiments and is used for preparation of BSA and Cu(II) standard solutions. A 100-mL jacketed beaker, thermostatted at 25 ⬚C, with a magnetic stirrer is the measurement cell. The Cu(II) ion selective electrode and a saturated calomel reference electrode are immersed in the reaction solution through two holes in the cell’s plastic or rubber cover. Potential readings are obtained from a digital pH–mV meter that is connected to two electrodes. Procedure To obtain a calibration curve, various quantities of the Cu(II) standard solution are added to a specific volume of the buffer solution in the measurement cell. The potentiometric readings are recorded after stabilization following each addition. The measured electromotive force (emf ) values, ∆E, are plotted against the negative logarithm of Cu(II) ion concentration to give a calibration curve of Cu(II) ISE by a linear least-squares fitting. The equilibrium data are obtained by repeating the procedure, but using a buffer solution containing a desired quantity of BSA. The emf values in relation to the free Cu(II) ion concentrations are measured after the binding equilibrium is achieved. A longer period of time is required before a stable emf reading can be obtained. With the aid of the calibration curve of Cu(II) ISE, the free Cu(II) ion concentrations are found from the measured emf values. The total concentrations of Cu(II) and BSA are calculated with respect to each addition of Cu(II) standard solution. Corrections for influences of change in volume of solution must be made. Data Analysis After the data collection, a set of r values that correspond to a set values of CF,Cu(II) are calculated according to eq 1.
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Since the plot of CF,Cu(II) versus r is a rectangular hyperbola, the binding parameters have to be estimated by nonlinear fitting of eq 2 to the data (r, CF,Cu(II)), which may be done by commercially available computer programs. The GraphPad Prism software is recommended (15). The data are also analyzed using the Scatchard model; values of r兾CF,Cu(II) are calculated and a graph of r versus r兾CF,Cu(II) is plotted. The plot is a biphasic graph and has the shape of a hyperbola. The asymptotes of the hyperbola have slopes equal to ᎑K1, ᎑K2 for the two classes of sites and their intercepts on the abscissa give the two values for n1 and n2. Hazards There are no significant hazards related to materials and procedures used in this experiment. Results and Discussion A graph of CF,Cu(II) versus r from a set of typical student data is shown in Figure 1. The data are analyzed by the GraphPad Prism to the eq 2 giving two distinct classes of Cu(II) binding sites on a BSA molecule: n1 = 1.11 and n2 = 14.1, and the corresponding binding constants K1 = 8.45 × 105 and K2 = 2.56 × 103. The same set of data can also be transformed and further analyzed with the aid of the Scatchard method. The resulting Scatchard plot and the relevant parameters are shown in Figure 2. It should be mentioned that the Scatchard method is not the preferred choice for the binding data analysis. Since the data transformation distorts the experimental error and violates the assumptions of linear regression, the binding parameters determined by linear regression of transformed data are thus likely to be farther from their true values than those determined by nonlinear regression. Besides, errors coming from arbitrariness and randomness are also unavoidable in plotting graph. At present the Scatchard method is only used for obtaining initial estimates of binding parameters.
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In the Laboratory
tain organic polyhydroxy compounds such as glycerol, mannitol, or sugars allows boric acid to be titrated directly with NaOH (16). In this way, the buffer made up of boric acid, borate, and mannitol results in a suitable value of pH and contains no species available to complex with the Cu(II) ions.
r CF,Cu(II) / (10ⴚ5 mol Lⴚ1)
10
8
6
Summary 4
slope = K1 n2
slope = K2
2
0 0
n1
2
4
6
8
10
12
14
r
Figure 2. The Scatchard plot for the interaction of Cu(II) ions with BSA based on the same experimental data as in Figure 1.
This simple and useful experiment was developed to serve as an advanced undergraduate laboratory project for biophysical chemistry curriculum. The experiment provides opportunities to study the interaction between ligands and biological macromolecules. Students are introduced to investigation of Cu(II) binding to BSA by potentiometry with the Cu(II) ISE and gain a better understanding of the relevant theory and techniques. To ensure completion of the project in class students should work in small groups and cooperative learning is encouraged. W
Generally speaking, it is difficult for students to check their experimental results with the literature values (4, 8–10) because there are significant differences in experimental conditions and methodology between studies. In addition only a few reports have been found that use ISEs to examine Cu(II) binding to BSA. However, students are encouraged to inquire whether their results are in agreement with each other. The data obtained by the class can be collected and statistical tests performed to evaluate the students’ results. The ISE method used in this experiment can be extended to other binding studies, but it is arduous to develop a number of probes such as ISEs that have response specificity to different ligands. The ISE method negates the need to separate the binding mixture as does other spectroscopic methods used in binding studies, including UV–vis spectrophotometry, fluorescence, circular dichroism, and electron spin resonance. The spectroscopic techniques are perhaps more popular because the determination is based on a change of the absorption, emission, or scattering spectrum of the binding system, and the microscopic structure details of binding can be obtained in addition to the macroscopic binding parameters. While the ISE method has not been as extensively used in binding studies, its pedagogical significance is distinct for the biophysical chemistry laboratory. A unique buffer system is used in this experiment to create an ideal binding medium. If Cu(II) is added as a simple salt to a neutral solution, it will hydrolyze to form metal– hydroxy and metal–oxy polymers. Even if these polymers do not aggregate to form a visible precipitate, such polymeric Cu(II) ions may either bind nonspecifically to BSA or be kinetically inert so that the profile of the Cu(II) binding to BSA will be inaccurate. Another concern is that species in the medium may react with the Cu(II) ions, again causing inaccurate data. To avoid these problems a buffer pair of boric acid–borate with added mannitol to adjust the pH to 5.02 was developed. Boric acid is a very weak monobasic acid (pKa = 9.0), but it forms a stable complex with diol or polyol and its acidity is increased by complexing. The addition of cer-
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Supplemental Material
An expanded version of experimental procedures for the students and notes for the instructors are available on JCE Online. Literature Cited 1. Ettinger, M. J. In Copper Proteins and Copper Enzymes; Lotie, R., Ed.; CRC Press Inc: Boca Raton, FL, 1984; Vol. 3, pp 175–229. 2. Brown, J. R. In Albumin Structure, Function and Uses; Rosenoer, V. M., Oratz, M., Rothschild, M. A., Eds.; Pergamon Press: Oxford, United Kingdom, 1977; pp 27–51. 3. Kragh-Hansen, U. Pharmacol. Rev. 1981, 33, 17–53. 4. Masuoka, J.; Hegenauer, J.; Dyke, B. R. V.; Saltman, P. J. Biol. Chem. 1993, 268, 21533–21537. 5. Bal, W.; Christodoulou, J.; Sadler, P. J.; Tucker, A. J. Inorg. Biochem. 1998, 70, 33–39. 6. Quagraine, E. K.; Reid, R. S. J. Inorg. Biochem. 2001, 85, 53– 60. 7. Zhang, Y.; Wilcox, D. E. J. Bio. Inorg. Chem. 2002, 7, 327– 337. 8. Reynolds, F. H.; Burkhard, R. K.; Mueller, D. D. Biochemistry 1973, 12, 359–364. 9. Naik, D. V.; Jewell, C. F.; Schulman, S. G. J. Pharma. Sci. 1975, 64, 1243. 10. Mohanakrishnan, P.; Chignell, C. J. Pharma. Sci. 1982, 71, 1180–1182. 11. Scatchard, G. Ann. N.Y. Acad. Sci. 1949, 51, 660–672. 12. Skoog, D. A.; Holler, F. J.; Nieman, T. A. Principles of Instrumental Analysis, 5th ed.; Harcourt Brace: Philadelphia, PA, 1998; pp 591–621. 13. Truman, S. L. J. Chem. Educ. 1997, 74, 171–176. 14. Young, C. C. J. Chem. Educ. 1997, 74, 177–182. 15. GraphPad Prism User’s Guide, Version 3; GraphPad Software Inc.: San Diego, CA, 1999. http://www.graphpad.com/prism/ Prism.htm (accessed Nov 2003). 16. Cotton, F. A.; Wilkinson, G. Basic Inorganic Chemistry, 2nd ed.; Wiley: New York, 1987; pp 288–289.
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