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Unité de Repliement et Modélisation des Protéines, Department of Structural Biology and Chemistry, Institut Pasteur, Paris, France
Reprint requests to: Michel E. Goldberg, Institut Pasteur, 28 rue du Dr. Roux, 75724 Paris Cedex 15, France; e-mail: goldberg{at}pasteur.fr; fax: +33-1-40-61-30-43.
(RECEIVED June 30, 2005; FINAL REVISION August 22, 2005; ACCEPTED August 22, 2005)
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Keywords: ATR; FTIR; proteins; secondary structure; adsorption
Abbreviations: ATR, attenuated total reflectance • FKPA, a peptidylprolyl cis–trans isomerase from Escherichia coli • FTIR, Fourier transform infrared • MBP, the maltose binding protein from Escherichia coli • PDB, the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank • TRX, the oxidized thioredoxin from Escherichia coli
Article and publication are at http://www.proteinscience.org/cgi/doi/10.1110/ps.051678205.
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Introduction |
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-helices but fails to precisely predict the 
-strand content, while infrared spectroscopy predicts -strands much better than -helices. Yet, infrared spectroscopy is still scarcely used for protein structural studies, in spite of the considerable improvements to the speed of acquisition, precision, and quality of the spectra brought about by the advent of Fourier transform infrared spectroscopy (FTIR) (Jackson and Mantsch 1995; Backmann et al. 1996; Baello et al. 2000). The main reason for this trailing of FTIR seems to be the practical difficulties encountered in applying it to proteins in aqueous solutions. Indeed, the information needed to predict the secondary structure contents from infrared spectra is contained in the amide I and amide II absorption bands of the peptide bonds (centered at ~1650 and 1550 cm–1, respectively), which overlap the very strong absorption band of water in the 1600 cm–1 region. This has two consequences. One is that the optical path used for FTIR measurements must be extremely short (a few microns only) to let enough light emerge from the sample cell. The second is that, in view of the very small concentration of peptide bonds compared to the water concentration (~ 10–2 M peptide bonds vs. 111 M water O—H bonds in a 1 mg/mL protein solution), the protein signal is extremely small compared to the water signal, which requires the thickness and positioning of the sample cell to be rigorously the same for successive measurements. The conjunction of these two requirements results in severe technical difficulties. Indeed, only demountable cells can be practically used so that the cell windows can be efficiently cleaned after each protein sample. Since the exact path length of such cells depends on the strength put in tightening them upon reassembly, it is extremely difficult to keep the optical path strictly constant. This prompted the design of demountable (for cleaning purposes) cells that can be filled and emptied without disassembling them (for constant optical path purposes) for recording the sample and buffer spectra. But filling, emptying, cleaning, rinsing, and drying very thin cells are delicate and time-consuming operations since residual traces of liquid as well as air bubbles are not easy to avoid. To circumvent these difficulties, an "attenuated total reflectance" accessory (ATR) can be used instead of the usual transmission sample cell. With an ATR, rather than measuring the absorption of the incident light, one measures the absorption of the evanescent wave that penetrates the solution when the incident light is reflected at the crystal/liquid interface. Loading the ATR with a solution, emptying it, cleaning the sample compartment, rinsing, and drying it are extremely rapid and simple. Furthermore, because the ATR geometry is factory defined and remains perfectly constant, the "optical path" depends only on the crystal and solution refractive indices, thus rendering it perfectly constant for a given solution. Measurements are therefore highly reproducible. Moreover, for several commercially available ATRs with a circular well (as opposed to long crystals) only minute volumes of samples are needed since 10 µL of solution amply suffice to cover the active surface of the ATR. These major advantages prompted attempts to use ATR/FTIR spectroscopy for structural studies of proteins (Raussens et al. 1998; Smith et al. 2002).
While starting FTIR studies on proteins, we indeed found it very easy and rapid to acquire spectra with an ATR. However, we also found out that their interpretation is not straightforward and may be badly misleading. In this report, we shall show that the ATR/FTIR spectra as recorded are both biased and distorted, we shall offer a physical interpretation for the bias and distortions we observed, we shall propose a simple method to correct the observed spectra so as to render them reliable, and we shall show that the corrected spectra thus obtained allow for reliable secondary structure predictions.
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). Visual examination of the spectra revealed that the height of the amide I and amide II peaks were not proportional to the protein concentration. This was confirmed by plotting the observed absorbance at the amide I and amide II peak frequencies as a function of the protein concentration. The straight line thus obtained (Fig. 2) indicates the presence of two components to the absorbance. One is concentration independent and corresponds to the absorbance obtained by extrapolation to zero protein concentration (0.0072 and 0.005 arbitrary units, respectively, for the amide I and amide II bands). The second is proportional to the protein concentration. For the amide I band, the absorbance of the concentration independent component corresponds to that of 3.5 mg/mL of the concentration-dependent component, as estimated from the slope of the amide I straight line. Similar experiments were conducted with bovine Ribonuclease A and horse muscle myoglobin and provided similar results, with the absorbance of the concentration-independent components corresponding to that of 3 and 5 mg/mL, respectively, of the concentration-dependent components. That the slopes of the straight lines obtained for the amide I and amide II bands are very similar, while the extrapolation to zero concentrations of the two lines do not coincide, suggests that the ratios of the amplitudes of the amide I and amide II bands are not the same for the concentration-dependent and concentration independent components.
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). Although the signal-to-noise ratio was rather poor (because of a small signal and limited number of accumulations), the spectra thus obtained clearly showed the characteristics of a protein spectrum, with the two major bands at the amide I and amide II positions. This confirmed that some protein got strongly adsorbed to the crystal during the 10-min incubation of the lysozyme in the ATR. Moreover, as expected from our hypothesis, the amplitudes of the amide I and amide II bands were the same (within the noise) for the four concentrations investigated, and these amplitudes represented over 70% of the concentration-independent component of the absorption discussed in the previous section. This suggested that at least a major part of the adsorbed protein was strongly attached to the ATR crystal. To test the stability of the adsorption, the buffer that had been added after removal of the 8 mg/mL lysozyme solution was removed by aspiration and replaced by 20 µL of fresh buffer, rinsing (i.e., aspiration followed by addition of 20 µL of buffer) was repeated twice, and the spectrum recorded. Finally, rinsing was applied three more times and the spectrum recorded again. While the peak amplitude after the first three rinses was slightly reduced compared to the initial spectrum of the adsorbed material, the spectrum after the sixth rinse was unchanged compared to that after three rinses. Thus, about half of the adsorbed protein appeared very tightly bound to the crystal, while the other half could be removed by repeated rinsing. Both the loosely and tightly adsorbed protein molecules could be efficiently removed from the crystal surface by washing the ATR sample compartment twice with a soft detergent solution (2% Hellmanex) followed by thorough rinsing with distilled water, a procedure that lasts ~2 min (see Materials and Methods). This was demonstrated by incubating a 10-mg/mL lysozyme solution in the ATR, applying the wash-and-rinse procedure, adding 20 µL of buffer to the ATR and recording the spectrum, which showed no detectable absorption in the amide I–amide II region.
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Absorption spectrum of the protein in solution
As shown above, the contribution of the crystal-adsorbed protein to the absorption spectrum is quite important. The experimental spectrum therefore does not correspond to the real spectrum of the protein in solution. This can be easily corrected by recording two spectra of the same protein, one at high (5–10 mg/mL), the second at low (~0.5–1 mg/mL) protein concentration. Subtracting the absorption spectrum at low concentration from the absorption spectrum at high concentration eliminates the contribution of the adsorbed protein (since it is the same in the two samples) and yields the spectrum of the soluble protein at a concentration equal to the concentration difference between the two solutions. Figure 4A shows the absorption spectrum of lysozyme at 9.1 mg/mL obtained by subtracting the spectrum recorded at 0.9 mg/mL from that recorded at 10 mg/mL. When this was done for lysozyme solutions at various concentrations, the resulting spectra were superimposable when normalized with respect to the concentration. Furthermore, the peak absorbances obtained for the amide I and amide II bands were now strictly proportional to the "apparent" concentration (i.e., the difference between the high and the low concentrations used for recording the spectra) as shown in Figure 4B.
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), differed from the corrected spectrum of the protein in solution in terms of the amide I and amide II band shapes, as well as in relative intensities of these two bands. This indicates that the adsorbed protein differs from the native protein in solution in terms of secondary structure contents, suggesting partial denaturation of the protein upon adsorption. Such post-adsorption changes in conformation were already reported for several proteins (Kondo and Mijara 1996; Zoungrana et al. 1997; Van Tassel et al. 1998; Santos et al. 2003). This emphasizes the need to clear the experimental spectra from the contribution of the nonnative adsorbed protein. The double concentration differential method we propose meets this requirement and renders the absorption measurements quantitative.
Baseline adjustment
Most of the absorption spectra obtained by this differential method (see, e.g., the continuous line in Fig. 4A) showed a baseline distortion: While the absorbance should be zero throughout the 1700–1850 cm–1 region, it systematically showed a decrease with decreasing frequencies until the region of the amide I band was reached. This unexpected behavior appeared the more pronounced for spectra acquired at the higher protein concentrations. From this, we inferred that this spurious absorption might be due to a very slight reduction in the depth of penetration of the evanescent wave with increasing protein concentration, presumably due to an increase in the refractive index of the solution. This would result in a slight reduction of the water absorption in the experiment at high protein concentration compared to that at low protein concentration. Using the Spectral Subtract function of the PROTA software (Subtract.AB in GRAMS/32), a variable weight factor was assigned to the buffer versus air absorption spectrum (recorded in the ATR) and the weight factor was optimized so as to minimize the difference between the protein spectrum and the weighted buffer spectrum in the 1720–1850 cm–1 region. Applying this procedure resulted in a corrected lysozyme spectrum (dotted line in Fig. 4A), which shows a flat and constant baseline. The usefulness of such spectra, corrected as indicated above for both the adsorbed protein and the baseline distortion, will now be considered.
Secondary structure analysis
Although corrected as described above, the ATR/FTIR spectra obtained still do not coincide with FTIR spectra obtained with a conventional transmission cell, since the ATR introduces some systematic spectral distortions. Some of them, like that caused by the variation of the penetration depth with the light frequency, can be corrected (Griffiths and de Haseth 1986). Others are not readily amenable to mathematical correction. However, these distortions are constant for a given ATR accessory and reproducible from spectrum to spectrum. Thus, differences in the ATR/FTIR spectra (once corrected as discussed above for protein adsorption and baseline distortion) of different proteins should quantitatively reflect differences in their conformations and serve for secondary structure prediction. Based on this reasoning, we constructed a library of ATR/FTIR corrected spectra for proteins of known three-dimensional structures and used it to predict the secondary structure contents of proteins not represented in the library. The transmission spectra of 29 reference proteins, each at low and high concentration, were acquired using the ATR with 1000 accumulations and at 4 cm–1 resolution. For each protein, the absorption spectrum of the protein in solution was constructed, using the transmission spectrum of the diluted sample as a reference for the concentrated sample. The baseline distortion correction was applied as indicated above. Finally, the corrected spectrum of each protein was normalized and introduced in the data library as described in the Materials and Methods section. In order to test the validity of the database thus constructed, three proteins investigated in our laboratory (the maltose binding protein MBP, the peptidylprolyl cis–trans-isomerase FKPA and thioredoxin TRX from Escherichia coli) were subjected to secondary structure predictions based on their FTIR spectra. Their spectra were acquired and treated as those of the database. The structural parameters, i.e., contents in -helices H, extended -strands E, bends S, and hydrogen bonded turns T as defined by Kabsch and Sander (1983), were obtained from the PDB files indicated in Table 1. Secondary structure predictions were then achieved using an adapted (see Materials and Methods) version of the variable selection method (Manavalan and Johnson 1987). The reconstructed spectrum corresponding to the best fit is shown, for each protein, as a solid line in Figure 5. While the fits seem reasonably good for the maltose binding protein and the FKPA isomerase, it significantly deviates from the experimental data in the region of the amide I band peak for thioredoxin. This rather strong deviation of the thioredoxin fit is confirmed by the high value of its root mean square deviation (0.0222) as indicated in Table 1. Table 1 also shows the secondary structure predictions corresponding to the best fit for each test protein, together with their secondary structure features assessed from their known 3D structures. The comparison between the predicted versus the assessed secondary structure contents indicates that the secondary structure contents of the three proteins were predicted with fairly good accuracy from the ATR/FTIR corrected spectra, in particular for the extended -strands of the two first proteins, while their -helix, bend and turn contents predictions were less accurate as often reported with transmission cell FTIR.
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shows the predicted secondary structure contents of these 20 proteins using the ATR library as well as their contents assessed from their 3D structure in the PDB. Figure 6 shows, for each database, the plot of the -strand contents of the 20 proteins assessed versus their predicted contents. For the set of data points obtained with each database, a linear regression was applied. The ordinates at zero abscissa were –0.015 and 0.026 for the ATR and transmission cell database, respectively, the slopes were 1.075 and 0.937, respectively, and the correlation coefficients were 0.896 and 0.642, respectively. Thus, in terms of ordinate at zero abscissa and of slopes, the two fits compare equally well with the expected values (0 and 1, respectively), but the ATR database provides a much better correlation coefficient which reflects a significantly more robust prediction of the -strand contents with the ATR measurements compared to the conventional transmission cell FTIR measurements. The same method was used to analyze the spectra of the two databases, and the two databases contain the spectra of the same set of proteins. Thus, the better correlation obtained with the ATR—compared to the transmission—database likely reflects larger experimental errors introduced in recording the spectra with transmission cells (used in PROTA) rather than with the ATR.
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FTIR spectra obtained by use of an ATR are also distorted by a variety of factors influencing the behavior of the evanescent wave. One of them is the variation of the penetration depth with the protein concentration, which results in a difference in the light absorption by water for the concentrated and diluted solutions. This difference is particularly important in the amide I–amide II region, which coincides with a very strong absorption band of water. As shown above, this can be easily corrected by adding to the protein spectrum a water spectrum weighted so as to minimize the absorbance of the protein solution in the 1720–1850 cm–1 region, where the protein absorbance should be zero. It should be noted that, though the physical cause of the spectrum distortion is different when transmission cells are used (irreproducibility of the cell thickness rather than influence of the protein refractive index on the evanescent wave penetration depth), the same correction procedure is routinely applied in conventional IR spectroscopy.
A second factor influencing the absorption of the evanescent wave is related to the change in the penetration depth of the evanescent wave with the light frequency. This affects all spectra recorded by means of an ATR, regardless of the solute and its concentration. Because the penetration depth is proportional to the wavelength (assuming the refractive indices of the ATR crystal and of the solution to be frequency independent, which is not rigorously correct), the penetration depth can be calculated (see formula in Griffiths and de Haseth 1986) to be ~8%–9% longer at the wavelength of the amide II band than at that of the amide I band, thus increasing artificially the apparent intensity of the amide II, compared to amide I, band.
Another important source of spectral distortion is the so-called "anomalous dispersion." This effect was shown (Grdadolnik 2002) to account for 10%–15% of the amide II signal observed for bovine serum albumin in an ATR. Taken together, the penetration depth dependence on the wavelength and the anomalous dispersion thus account for a 20%–25% overestimate of the amide II band intensity compared to the amide I. These two effects account for the major part, if not the totality, of the lower ratio of the amide I to amide II amplitudes observed in the ATR (~0.9–1.1) compared to a classical transmission cell (~1.3–1.4).
Though a mathematical formula (Griffiths and de Haseth 1986) can easily correct the experimental ATR spectra for the wavelength dependence of the penetration depth, and a procedure was developed to correct for the anomalous dispersion (Grdadolnik 2002), these corrections were not applied in the present study because the same solvent was used throughout our experiments, resulting in the same spectral distortions for all the samples analyzed, and because systematic spectral distortions, i.e., that are the same for the proteins under investigation and for the proteins in the database, do not affect the spectrum analysis procedure we used. Thus, FTIR spectra recorded using an ATR accessory, once corrected for the crystal-adsorbed protein and for the distortion caused by the protein refractive index increment, can be readily used for secondary structure predictions, as documented by the results in Table 2. Indeed, the robustness of secondary structure predictions from the spectra recorded with the ATR appears better than that of predictions made from conventional spectra recorded with transmission cells, as indicated by a much better correlation coefficient for the ATR data compared to the transmission cell data shown in Figure 6.
The fit between predicted and assessed secondary structure contents (see Tables 1, 2) is far from being perfect. For some proteins, the discrepancy between the predicted and the assessed values is rather high. This may be due in part to the fact that the IR absorption bands of several amino acid side chains overlap the amide I and II bands, and thus contribute to the part of the IR absorption spectra used in the spectral analysis. Careful spectral analysis should therefore subtract the side chain contributions from the spectra of the unknown proteins as well as from those of the database. This could, in principle, be done by recording the spectra of the side chains alone, computing a "side-chain spectrum" from the known amino acid composition of each protein, and subtracting it from the experimental spectrum. It is likely that such side-chain corrections, included in the PROTA software for the correction of conventional transmission cell spectra, would improve the robustness of the predictions. Another way to improve the secondary structure prediction from the FTIR spectra might be to include more secondary structure types in the analysis. Indeed, the analysis reported here was based on only the following structural features (as defined by Kabsch and Sander 1983): -helices, extended -strands, hydrogen bonded turns and bends, all residues not belonging to one or the other of these four types being qualified as "others." Including 3–10 helices, 
-helices and/or isolated -bridges in the database might improve the overall secondary structure predictions. The limited number of secondary structure types we used might account for the rather poor prediction of the -strand content of E. coli thioredoxin, for which the 3D structure reported in the PDB indicates the presence of six residues (i.e., 5.5%) belonging to 3–10 helices in one of the two chains per unit cell. It is plausible that this relatively high 3–10 helix content is responsible for the impossibility to obtain a better fit of the theoretical spectrum of thioredoxin to the experimental data (Fig. 5C), and hence, better secondary structure predictions. Considering the 3–10 helices as a distinct type of secondary structure (while in our analysis these helices were included in the "other" type), or grouping all the helices (, , and 3–10) in the same secondary structure type, could be easily done through minor modifications of the VARSELEC program, and might improve the robustness of the secondary structure prediction based on FTIR spectra.
There exists a variety of procedures, other than VAR-SELEC, that have been developed for determining the secondary structure of proteins from FTIR spectra (for reviews, see Surewicz and Mantsch 1988; Arrondo et al. 1993). Some are based on band decomposition and curve fitting including frequency and/or bandshape analysis (Cameron et al. 1982; Byler and Susi 1986; Goormaghtigh et al. 1990), others are based on the use of calibration sets (Dousseau and Pézolet 1990; Lee et al. 1990; Sarver and Krueger 1991a). A systematic comparison of their accuracy in predicting the secondary structure from FTIR spectra on the basis of data reported in the literature is not an easy task. Indeed, the studies reported rely on different procedures, but also on different databases, which makes it difficult to assess which of the procedures or the databases is responsible for the quality of the results. However, based on our experience with CD, the most critical point for obtaining good predictions from CD spectra comparison is the quality of the experimental data (both that of the sample to analyze and of the proteins in the database) as well as the proper choice of the proteins in the database to be taken into account, the latter being optimized by the Variable Selection procedure (Manavalan and Johnson 1987). We therefore chose, in our study, the VARSELEC program, which is based on the "singular value decomposition" method initially proposed for CD by Hennessy and Johnson (1981), and includes the Variable Selection procedure (Manavalan and Johnson 1987). In spite of the fact that running it is much less tedious and time consuming than using VAR-SELEC, the program contained in the PROTA package, which is based on the "principal component factor analysis" method (Pancoska et al. 1991), was not retained because it does not include the variable selection procedure. Indeed, when tested on individual spectra extracted from the PROTA database using either the PROTA or the VARSELEC algorithm, VARSELEC provided a better secondary structure prediction, justifying further that the VARSELEC procedure was preferred.
It has been reported that improved predictions can be obtained, using a principal component regression-based computer program that includes an "inside model space" bootstrap to take into account the variability of the amide I and amide II bands introduced by the various levels of hydration of the amide bonds (Smith et al. 2002). This is of particular interest in view of the significant differences in the absorption of exposed and buried amide groups that are in -helices (Walsh et al. 2003). There also exists a variety of experimental approaches that were reported to improve the reliability and precision of secondary structure predictions from FTIR spectroscopy. Thus, coupling FTIR with circular dichroism (Sarver and Krueger 1991b), or including hydrogen exchange in the FTIR measurements (Baello et al. 2000) were shown to improve the secondary structure predictions. None of these approaches was attempted in the present study since our goal was limited to finding out whether or not ATR/FTIR can be substituted for transmittance FTIR for the sake of simplifying and eventually improving data collection. The results we obtained clearly indicate that, provided appropriate caution is exerted in collecting and analyzing the data, the use of an ATR accessory leads to secondary structure predictions that are at least as good as those obtained by conventional transmission cell spectroscopy, with the considerable advantage that sample handling is dramatically easier, more reproducible, and much faster. It is likely that, based on this conclusion, the use of ATR/FTIR spectroscopy will become of more widespread use in investigations on protein structure.
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-chymotrypsinogen, concanavalin A, cytochrome c from horse liver, enolase from baker yeast, glyceraldehyde-3-phosphate dehydrogenase from rabbit muscle, human hemoglobin, hexokinase from baker yeast, soybean trypsin inhibitor, bovine pancreatic trypsin inhibitor, human -lactalbumin, lactate dehydrogenase from rabbit muscle, bovine -lactoglobulin, hen egg white lysozyme, myoglobin from horse muscle, hen ovalbumin, pepsin from porcine stomach, porcine pepsinogen, horse radish peroxidase, phosphoglycerate kinase from yeast, bovine ribonucleases A and S, human serum albumin, superoxide dismutase from bovine erythrocyte, thaumatin from Thaumatococcus daniellii, and triose phosphate isomerase from baker yeast. They were all obtained from Sigma-Aldrich in the highest purity grade available and used without further purification. The test proteins were the E. coli maltose binding protein MBP and prolyl cis–trans-isomerase FKPA, both purified in our unit by Dr. J.-M. Betton, and thioredoxin purified and kindly provided by the laboratory of J. Beckwith (Harvard); 0.5 to 1 mL of a solution of each protein, prepared at a concentration expected to be ~10 mg/mL, was dialyzed for at least 15 h at 4°C against 500 mL of 0.01 M sodium phosphate pH 7.0 (except for pepsin, where the pH was 5.5). The protein concentration in the dialysate was determined by spectrophotometry, using either published specific extinction coefficients, or extinction coefficients calculated from the amino acid composition according to Pace et al. (1995). A 10-fold dilution of the dialysate in the dialysis buffer was prepared and used for the spectrophotometric concentration determination as well as to serve as the 100% transmission reference solution for the FTIR measurements (see below).
Acquisition of ATR/FTIR spectra
The FTIR spectra were recorded on a PROTA FTIR Protein Analyzer from ABB-Bomem, which corresponds to an ABB-Bomem MB104 infrared spectrometer supplemented with software specifically dedicated to the analysis of infrared spectra of proteins. The PROTA software is based on the GRAMS program from Galactic Industries Corporation and includes, among other features, a database containing the FTIR absorption spectra (obtained by using conventional transmission cells) of 33 proteins of known 3D structures, a routine allowing one to subtract the contribution of the protein amino acid side chains from its absorption spectrum, and a program to predict the secondary structure contents of a protein from its IR spectrum by comparison to the database. The spectrophotometer was equipped with a DuraSamplIR II, 9 reflection diamond ATR accessory from SensIR Technologies, and with a conventional, low-sensitivity, DTGS Deuterated Tri-Glycine Sulfate (DTGS) detector. Higher sensitivity, improved signal-to-noise ratios and shorter accumulation times would have been obtained using a narrow range Mercury Cadmium Telluride (MCT) detector. The spectrophotometer was set up in a room were the temperature was maintained at 20° ± 2°C. The volume of the sample deposited in the ATR well was 20 µL. The ATR well was covered with a slightly convex glass cover (supplied with the ATR) to prevent solvent evaporation. All spectra were acquired in the single beam mode with a 4-cm–1 resolution. For all preliminary experiments (unless otherwise stated) the data from 300 scans were accumulated and a buffer transmission spectrum was systematically recorded (300 scans) before and after each series of measurements. For the construction of the database as well as for the analysis of the three test proteins, accumulation was over 1000 scans and took ~50 min for each spectrum. The spectra of the dialysate and its 10-fold dilution were recorded one after the other without delay so as to minimize a possible drift in the output of the light source. The absorption spectrum of each solution was constructed from its single beam spectrum using the appropriate reference spectrum and the Absorbance function of the PROTA software. Uncorrected absorption spectra were constructed using the buffer single beam spectrum as a reference. Absorption spectra corrected for protein adsorption on the ATR crystal were constructed from the spectrum of the dialysate using the spectrum of the 10-fold diluted dialysate as the reference. The baseline distortion resulting from the refractive index increment due to the protein was corrected by means of the Spectral Subtract function of the PROTA software, using as subtrahend the absorption spectrum of the buffer (obtained from the single beam spectra of buffer and air, i.e., with the empty ATR) and the 1850–1720 cm–1 frequency range for the minimization. Unless otherwise stated, after each spectrum recording, the ATR well was emptied by means of an Eppendorf pipette tip connected to a vacuum pump. Next, the ATR window was washed as follows: 50 µL of a 2% Hellmanex (Hellma Gmbh) detergent solution were introduced in the ATR well. After 1 min, the ATR window was thoroughly rubbed with the extremity of a cotton tip previously soaked in the detergent solution. The excess detergent was removed from the compartment by aspiration and the whole washing operation was repeated a second time. The ATR well was then thoroughly rinsed by flushing it with distilled water with simultaneous aspiration of the excess water. The window and well were then carefully dried by aspiration of all traces of residual water.
Estimation of secondary structure
The secondary structure contents of the proteins were estimated using the single value decomposition with the variable selection (VARSELEC) procedure proposed by Manavalan and Johnson (1987). Two databases were used, one consisting in the 33 reference spectra included in the PROTA software, the second consisting in the 29 protein spectra acquired with the ATR accessory. In all cases, the spectra were normalized to 1 at the peak of the amide I band and only the 165 values corresponding to the spectral range between 1797 and 1481 cm1 were taken into account for the analysis. Four structural types were considered: -helix, extended -strands, bends, and H-bonded turns. The fraction of residues belonging to each structural type was assessed from the DSSP analysis (Kabsch and Sander 1983) of the PDB entry corresponding to each protein. For each secondary structure prediction, the best fit was selected from the combination(s) of spectra corresponding to the smallest RMS value and the sum of the structural fractions (between 0.90 and 1.10) closest to 1.0. In order to test the reliability of the secondary structure determination with either the PROTA database or the database we constructed, each of the 20 spectra of the reference proteins common to both databases were submitted to the VARSELEC procedure after its removal from the corresponding database.
The PDB files used for assessing the secondary structure contents in the ATR database were as follows: alcohol dehydrogenase, 1YE3
[PDB]
; carbonic anhydrase, 1CA2; catalase, 7CAT; citrate synthase, 1CTS; -chymotrypsinogen, 2CGA; concanavalin A, 1NLS; cytochrome c, 1AKK; enolase, 1EBH; glyceraldehyde-3-phosphate dehydrogenase, 1J0X; hemoglobin, 1BBB; hexokinase, 2YHX; soybean trypsin inhibitor, 1AVU; bovine pancreatic trypsin inhibitor, 4PTI; -lactalbumin, 1HFZ; lactate dehydrogenase, 6LDH; -lactoglobulin, 1BEB; lysozyme, 6LYZ; myoglobin, 1WLA; ovalbumin, 1OVA; pepsin, 4PEP; pepsinogen, 3PSG; peroxidase, 1HCH; phosphoglycerate kinase, 3PGK; ribonuclease A, 3RN3; ribonuclease S, 2RNS; serum albumin, 1AO6; superoxide dismutase, 2SOD; thaumatin, 1RQW; and triose phosphate isomerase, 1YPI.
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Acknowledgments |
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References |
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