Data Analysis

The combination of experimental parameters that are investigated (e.g. pH, temperature) is used as a coordinate that defines the numerical values of each axis of a diagram. The set of peak positions (x,y,z…) produced by the derivative analysis of the near-UV spectra defines the value associated with each

parameter coordinate. These experimental data sets acquired at each coordinate represent unit vectors which when combined create a single vector of n dimensions of that coordinate V=

v^K=(x,y,x…). Using mathematical software capable of performing vector analysis, a complete set of unit vectors that constitutes the phase space is defined, leading to the definition of the so called density matrix, with its eigenvalues and eigenvectors. The eigenvalues describe the "weight" of each data point in the density matrix and allows elimination of marginal contributors, further reducing the number of dimensions and permitting simple intuitive visualization. More detailed description of this approach can be found in mathematical literature (6). The phase regions are defined by studying the eigenvectors and demonstrating the coherence over a parameter region. A phase boundary is an abrupt change in the orientation of vectors as the parameters are varied.

Typical results of a second derivative analysis of a therapeutic protein, a bovine granulocyte colony stimulating factor (bGCSF) is shown in Figure 1 (7). Bovine GCSF is a member of the four helix bundle family of protein that has veterinary therapeutic potential (8), whereas its human analog is already an important drug (9). Peaks 1-3 arise from Phe, peak 4 from Tyr, peak 5 from both Tyr and Trp, and peak 6 from Trp. It is apparent that quasi-linear, temperature-dependent increases in wavelength are observed for most peaks at lower temperatures (before transitions). The continuous

nature of these shifts suggests that the shifts are not the result of protein structural alterations but rather reflect an intrinsic protein response of the spectra of the aromatic amino acids side chains to the temperature.

Figure 1. Derivative absorbance studies of bGCSF as a function of temperature at pH 4. The wavelength positions of six negative peaks were followed as a function of temperature: 1(A), Phe; 2(B), Phe; 3(C), Phe; 4(D), Tyr; 5(E), Tyr/Trp; and 6(F), Trp. Protein concentration was 5 micromolar in 10 mM citrate buffer. Spectra were collected at 2.5°C intervals, with a 5-min temperature equilibration period included before data collection. All errors are reported as standard error (n = 3). Reprinted from Kueltzo and Middaugh, 2003.

Figure 2. Temperature-pH phase diagram of bGCSF based on second-derivative absorbance data. Six distinct phases are observed: (1) pH 2-3, T 10°-55°C; (2) pH 2-3, T 55°-90°C; (3) pH 4, T 10°-60°C; (4) pH 4, T 60°-90°C; (5) pH 5-7, T 10°-50°C; and (6) pH 5-7, T 50°-90°C. Blocks of continuous shade represent single phases, conditions under which the raw data-derived vectors behave similarly. The shades were arbitrarily chosen. Reprinted from Kueltzo and Middaugh, 2003.

A bGCSF phase diagram was constructed from derivative absorbance data such as shown in Figure 4 using the matrix approach outlined above (Figure 2). Each solid block indicates conditions where under which the data vectors were parallel, defining a structural phase. From this diagram it appears that cGCSF adopts at least six primary phases. The properties of the phases can be established be reference to other biophysical measurements (10), and are listed in Table 1.

Table 1. bGCSF phase characterization. Reprinted from Kueltzo and Middaugh, 2003.

It should be noted that that previously determined midpoints of CD and DSC transitions generally correspond to the temperature borders between the phases. These results support the suggestion that derivative absorbance analysis does in fact detect the majority of physical perturbations detected by other spectroscopic and calorimetric methods.

One interesting feature observed is the noted temperature dependence of aromatic amino acid peak positions outside the transition regions. The interactions with the microenvironments, that may include dipolar (i.e., dielectric effects), dispersive (i.e., van der Waals forces), and short-range specific interactions (i.e., hydrogen bonding), have all been shown to contribute to spectral interactions (11). The known temperature dependence of such interactions suggests that thermal alterations of solvent physical properties and their

effects on solute electronic spectra could potentially be a source of the peak shifts observed. Association and dissociation of macromolecules often has an effect on their electronic spectra (11). Thus the derivative spectroscopy opens an avenue for the examination of protein dynamics, as seen by the solvent-dependent perturbations of aromatic amino acid microenvironments.

Temperature-dependent alterations of the solvent dielectric constant are expected, therefore, to influence spectral shifts. The decrease of the magnitude of these shifts in comparison to a model compounds shown in Figure 3 reflect the degree of solvation of the buried aromatic amino acids chains (12). The acrylamide fluorescence quenching of azurin, shown in the inset of Figure 6, shows similar extent of protection from the solvent as in the UV derivative analysis, indicating that near-UV spectroscopy may offer similar precision to established spectroscopic methods.

The structural dynamic of proteins can further be studied by the introduction of small salt cations (Li+, Na+, and Cs+) of varying sizes that differentially diffuse into protein interiors and alter the positions of the UV absorbance bands of aromatic amino acids (13). The observed shifts depend on a number of factors, solvent accessibility, degree of protein core penetration, ion size, and local cation-π interactions.

Unlike fluorescence studies of proteins, which rely entirely of Trp residues, all three aromatic amino acids are surveyed by

this technique. Therefore, a more complete picture of the protein structure and behavior can be obtained, in part due to simplicity of the measurements that simultaneously capture peak positions of the three aromatic amino acid chains.

Figure 3. Temperature dependent derivative absorbance peak shifts of the tryptophan residue of model proteins over the temperature range of 10-35 °C (n=3). Inset shows acrylamide quenching of the fluorescence of proteins containing single tryptophan residues. Model compounds used were N-acetyl-L-tryptophan ethyl ester ( ), Melittin ( ), Rnase-T1 ( ), Azurin ( ), and Protein A ( ). Reprinted from Esfandiary, Hunjan, Lushington, Joshi and Middaugh, 2009.

8.8 Concluding remarks

UV spectrophotometers that are available in essentially every research and development laboratory dealing with therapeutic proteins are in daily use, yet their potential is seldom fully realized. While the instruments are typically used to obtain rough estimates, with acceptable errors often in the range of 5%, the intrinsic noise levels are typically less than 0.0001 units of absorbance, equivalent to 0.01% of typical measurement value.

The inherent precision of uv spectroscopy, when combined with the computational power of personal computers that drive the instruments and process the data, gives an opportunity to not only increase the precision of routine analyses but also obtain detailed information about the behavior of studied protein under a wide range of conditions.

With such unrealized potential, the users' attitude toward gaining an expertise in this particular field is critical. We hope that this communication will help those who strive to increase the quality of development process, for the benefit of patients who will ultimately use the product.

8.9 References

1. Ichikawa T, Terada H. 1979. Estimation of state and amount of phenylalanine residues in proteins by second derivative spectrophotometry. Biochim Biophys Acta 580:

120-128.

2. Ragone R, Colonna G, Balestrieri C, Servillo L, Irace G.

1984. Determination of tyrosine exposure in proteins by second-derivative spectroscopy. Biochemistry 23: 1871-1875.

3. Balestrieri C, Colonna G, Giovane A, Irace G, Servillo L.

1978. Second-derivative spectroscopy of proteins. A method for the quantitative determination of aromatic amino acids in proteins. Eur J Biochem 90: 433-440

4. Mach H, Middaugh CR. 1994. Simultaneous monitoring of the environment of tryptophan, tyrosine and phenylalanine residues in proteins by near-ultraviolet

absorption spectroscopy. Anal. Biochemistry 222:323-331.

5. Kueltzo LA, Middaugh CR. 2005. Ultraviolet Absorption Spectroscopy. In Methods for structural Analysis of Proteins, AAPS Press, Arlington, VA, pp1-25.

6. Boyce WE, DiPrima RC. 1997. Elementary differential equations. New York: John Wiley & Sons, Inc., 592 pp.

7. Kueltzo L, Middaugh CR. 2003. Structural characterization of bovine granulocyte colony stimulating factor: effect of temperature and pH. J Pharm Sci 92:1793-1804.

8. Kasraian K, Kuzniar A, Earley D, Kamicker BJ, Wilson G, Manion T, Hong J, Reiber C, Canning P. 2001.

Sustained in vivo activity of recombinant bovine granulocyte colony stimulating factor (bGCSF) using HEPES buffer. Pharm Dev Technol 6: 441-447.

9. Herman AC, Boone TC, Lu HS. 1996. Characterization, formulation, and stability of Neupogen (filgrastim), a recombinant human granulocyte-colony stimulating factor.

In: Pearlman R, Wang YJ, editors. Formulation, characterization, and stability of protein drugs. New York:

Plenum Press, pp 303-326.

10. Kueltzo LA, Ersoy B, Ralston JP, Middaugh CR. 2003.

Derivative Absorbance Spectroscopy and Protein Phase Diagrams as Tools for Comprehensive Protein Characterization: A bGCSF Case Study. J Pharm Sci 92:1805-1820.

11. Donovan JW. 1973. Ultraviolet difference spectroscopy:

New techniques and applications. Methods Enzym 27:497-525.

12. Esfandiary R, Hunjan JS, Lushington GH, Joshi SB, Middaugh CR. 2009. Temperature-dependent 2^nd derivative absorbance spectroscopy of aromatic amino acids as a probe of protein dynamics. Protein Science 18:2603-2614.

13. Lucas LH, Ersoy BA, Kueltzo LA, Joshi SB, Brandau TD, Thyagarajapuram N, Peek LJ, Middaugh CR. 2006.

Probing protein structure and dynamics by second-derivative ultraviolet absorption analysis of cation–π interactions. Protein Sci 15:2228-2243.