Determination of the secondary structure of proteins from the amide I band of the laser Raman spectrum

The amide I band in the laser Raman spectrum of proteins has been resolved into six components, each representing residues in a different type of secondary structure. These structure types are ordered or bihydrogen-bonded helix (believed to be located in the center of helical segments), disordered or monohydrogen-bonded helix (believed to be located at the ends of helical segments), antiparallel beta sheet, parallel beta sheet, reverse turn, and undefined. The Raman spectrum representing 100% of each type of residue conformation has been computed from the solvent-subtracted Raman spectra of ten proteins with known secondary structure, plus poly-l-lysine using a least-squares solution of the overdetermined system of equations. Linear combinations of these reference spectra were then fitted to the experimental amide I spectra of these and other proteins to estimate the fractions of residues in these conformations. Statistical tests suggest that the discrimination between bihydrogen-bonded helix and monohydrogen-bonded helix is significant as is the discrimination between parallel and antiparallel β-sheet. However, the discrimination between random structure and turns has not yet been accomplished by these studies. The absolute difference between X-ray and Raman estimates of structure for 17 protein samples is generally less than 6%. We conclude that detailed and reasonably accurate estimates of secondary structure can be derived from the amide I spectra of proteins.     

A holistic approach to protein secondary structure characterization using amide I band Raman spectroscopy

We have developed a holistic protein structure estimation technique using amide I band Raman spectroscopy. This technique combines the superposition of reference spectra for pure secondary structure elements with simultaneous aromatic, fluorescence, and solvent background subtraction, and is applicable to solution, suspension, and solid protein samples. A key component of this technique was the calculation of the reference spectra for ordered helix, unordered helix, and sheet, turns, and unordered structures from a series of well-characterized reference proteins. We accurately account for the overlap between the amide I and non-amide I regions and allow for different scattering efficiencies for different secondary structures. For hydrated samples, we allowed for the possibility that bound water spectra differ from the bulk water spectra. Our computed reference spectra compare well with previous experimental and theoretical results in the literature. We have demonstrated the use of these reference spectra for the estimation of secondary structures of proteins in solution, suspension, and dry solid forms. The agreement between our structure estimates and the corresponding determinations from X-ray crystallography is good.     

Automatic amide I frequency selection for rapid quantification of protein secondary structure from Fourier transform infrared spectra of proteins

Here we report the development of a new neural network based approach for rapid quantification of protein secondary structure from Fourier transform infrared (FTIR) spectra of proteins. A technique for efficiently reducing the amount of spectral data by almost 90% is suggested to facilitate faster neural network analysis. Additionally, an automatic procedure is introduced for selecting only those regions within the amide I band of protein FTIR spectra, which can be best related to secondary structure contents by subsequent neural network analysis. Based on a given reference set of FTIR spectra from proteins with known secondary structure, a subset of merely 29 out of 101 amide I absorbance values could be identified, which lead to an improved prediction accuracy. The average prediction accuracy achieved for helix, sheet, turn, bend, and other is 4.96% which is better than that achieved by alternative methods that have been previously reported indicating the significant potential of this approach. Our suggested automatic amide I frequency selection procedure may be easily extended to identify promising regions from spectral data recorded by other spectroscopic techniques, like for example circular dichroism spectroscopy.     

Estimation of poly A secondary structure from Raman scattering in acid aqueous solution.

In the frequency region 600–1600 cm^?1 the Raman spectra of acidic aqueous solutions of poly (rA) consist of several well-resolved lines. Four of these lines at 725, 1303, 1336 and 1508 cm^?1 demonstrate the Raman hypochromic effect of poly (rA) at pH-values of 5.73 and 5.35 as a function of the temperature.The results suggest that Raman intensity measurements are sensitive to order-disorder transitions of aqueous polynucleotides.     

Estimation of protein secondary structure and error analysis from circular dichroism spectra

The estimation of protein secondary structure from circular dichroism spectra is described by a multivariate linear model with noise (Gauss-Markoff model). With this formalism the adequacy of the linear model is investigated, paying special attention to the estimation of the error in the secondary structure estimates. It is shown that the linear model is only adequate for the alpha-helix class. Since the failure of the linear model is most likely due to nonlinear effects, a locally linearized model is introduced. This model is combined with the selection of the estimate whose fractions of secondary structure summate to approximately one. Comparing the estimation from the CD spectra with the X-ray data (by using the data set of W.C. Johnson Jr., 1988, Annu. Rev. Biophys. Chem. 17, 145-166) the root mean square residuals are 0.09 (alpha-helix), 0.12 (anti-parallel beta-sheet), 0.08 (parallel beta-sheet), 0.07 (beta-turn), and 0.09 (other). These residuals are somewhat larger than the errors estimated from the locally linearized model. In addition to alpha-helix, in this model the beta-turn and "other" class are estimated adequately. But the estimation of the antiparallel and parallel beta-sheet class remains unsatisfactory. We compared the linear model and the locally linearized model with two other methods (S. W. Provencher and J. Gl?ckner, 1981, Biochemistry 20, 1085-1094; P. Manavalan and W. C. Johnson Jr., 1988, Anal. Biochem. 167, 76-85). The locally linearized model and the Provencher and Gl?ckner method provided the smallest residuals. However, an advantage of the locally linearized model is the estimation of the error in the secondary structure estimates.     

Investigation of the cAMP receptor protein secondary structure by Raman spectroscopy

Raman spectroscopy was employed to examine the secondary structure of the cAMP receptor protein (CRP). Spectra were obtained over the range 400-1900 cm-1 from solutions of CRP and from CRP-cAMP cocrystals. The spectra of CRP dissolved in 30 mM sodium phosphate and 0.15 M NaCl buffered at either pH 6 or pH 8 or dissolved in 0.15-0.2 M NaCl at protein concentrations of 5, 15, and 30 mg/mL were examined. Estimates of the secondary structure distribution were made by analyzing the amide I region of the spectra (1630-1700 cm-1). CRP secondary structure distributions were essentially the same in either pH and at all protein concentrations examined. The amide I analyses indicated a structural distribution of 44% alpha-helix, 28% beta-strand, 18% turn, and 10% undefined for CRP in solution. Raman spectra of CRP-cAMP cocrystals differed from the spectra of CRP in solution. Some differences were assigned to interfering background bands, whereas other spectral differences were attributed to changes in CRP structure. Differences in the amide III region and in the intensity at 935 cm-1 were consistent with alterations in secondary structure. Analysis of the amide I region of the CRP-cAMP cocrystal spectrum indicated a secondary structure distribution of 37% alpha-helix, 33% beta-strand, 17% turn, and 12% undefined. This result is in agreement with a published secondary structure distribution derived from X-ray analysis of CRP-cAMP cocrystals (37% alpha-helix and 36% beta-strand).(ABSTRACT TRUNCATED AT 250 WORDS)     

Protein secondary structures in water from second-derivative amide I infrared spectra

Infrared spectra have been obtained for 12 globular proteins in aqueous solution at 20 degrees C. The proteins studied, which vary widely in the relative amounts of different secondary structures present, include myoglobin, hemoglobin, immunoglobulin G, concanavalin A, lysozyme, cytochrome c, alpha-chymotrypsin, trypsin, ribonuclease A, alcohol dehydrogenase, beta 2-microglobulin, and human class I major histocompatibility complex antigen A2. Criteria for evaluating how successfully the spectra due to liquid and gaseous water are subtracted from the observed spectrum in the amide I region were developed. Comparisons of second-derivative amide I spectra with available crystal structure data provide both qualitative and quantitative support for assignments of infrared bands to secondary structures. Band frequency assignments assigned to alpha-helix, beta-sheet, unordered, and turn structures are highly consistent among all proteins and agree closely with predictions from theory. alpha-Helix and unordered structures can each be assigned to only one band whereas multiple bands are associated with beta-sheets and turns. These findings demonstrate a method of analysis of second-derivative amide I spectra whereby the frequencies of bands due to different secondary structures can be obtained. Furthermore, the band intensities obtained provide a useful method for estimating the relative amounts of different structures.     

K2D2: Estimation of protein secondary structure from circular dichroism spectra

Background  

Circular dichroism spectroscopy is a widely used technique to analyze the secondary structure of proteins in solution. Predictive methods use the circular dichroism spectra from proteins of known tertiary structure to assess the secondary structure contents of a protein with unknown structure given its circular dichroism spectrum.     

Circular dichroism and laser Raman spectroscopic analysis of the secondary structure ofCerebratulus lacteus toxin B-IV

The secondary structure ofCerebratulus lacteus toxin B-IV, a neurotoxic polypeptide containing 55 amino acid residues and four disulfide bonds, was experimentally estimated by computer analyses of toxin circular dichroism (CD) and laser Raman spectra. The CD spectrum of the toxin displayed typical α-helical peaks at 191, 208, and 222 nm. At neutralpH, the α-helix estimates from CD varied between 49 and 55%, when nonrepresentative spectrum analytical methods were used. Analysis of the laser Raman spectrum obtained at a much higher toxin concentration yielded a 78% α-helix estimate. Both CD and Raman spectroscopic methods failed to detect any β-sheet structure. The spectroscopic analyses revealed significantly more α-helix and less β-sheet for toxin B-IV than was predicted from its sequence. To account for the difference between the 49–55% helix estimate from CD spectra and the 78% helix estimate from the Raman spectrum, we postulate that some terminal residues are unfolded at the low toxin concentrations used for CD measurements but form helix at the high toxin concentration used for Raman measurements. Our CD observations showing thatCerebatulus toxin B-IV helix content increases about 15% in trifluoroethanol or at highpH are consistent with this interpretation.     

Determination of the secondary structure content of proteins in aqueous solutions from their amide I and amide II infrared bands. Comparison between classical and partial least-squares methods

A method for estimating protein secondary structure from infrared spectra has been developed. The infrared spectra of H2O solutions of 13 proteins of known crystal structure have been recorded and corrected for the spectral contribution of water in the amide I and II region by using the algorithm of Dousseau et al. [Dousseau, F., Therrien, M., & Pézolet, M. (1989) Appl. Spectrosc. 43, 538-542]. This calibration set of proteins has been analyzed by using either a classical least-squares (CLS) method or the partial least-squares (PLS) method. The pure-structure spectra calculated by the classical least-squares method are in good agreement with spectra of poly(L-lysine) in the alpha-helix, beta-sheet, and undefined conformations. The results show that the best agreement between the secondary structure determined by X-ray crystallography and that predicted by infrared spectroscopy is obtained when both the amide I and II bands are used to generate the calibration set, when the PLS method is used, and when it is assumed that the secondary structure of proteins is composed of only four types of structure: ordered and disordered alpha-helices, beta-sheet, and undefined conformation. Attempts to include turns in the secondary structure estimation have led to a loss of accuracy. The standard deviation of the difference between X-ray and infrared secondary structure estimates with this method is 4.8% for the alpha-helix, 3.7% for the beta-sheet, and 5.1% for the undefined structure, whereas the regression coefficients are 0.95, 0.96, and 0.56, respectively. The spectra of the calibration proteins were also recorded in 2H2O solution.(ABSTRACT TRUNCATED AT 250 WORDS)     

Ultraviolet Raman examination of the environmental dependence of bombolitin I and bombolitin III secondary structure.

Bombolitin I and III (BI and BIII) are small amphiphilic peptides isolated from bumblebee venom. Although they exist in predominately nonhelical conformations in dilute aqueous solutions, we demonstrate, using UV Raman spectroscopy, that they become predominately alpha-helical in solution at pH > 10, in high ionic strength solutions, and in the presence of trifluoroethanol (TFE) and dodecylphosphocholine (DPC) micelles. In this paper, we examine the effects of electrostatic and hydrophobic interactions that control folding of BI and BIII by systematically monitoring their secondary structures as a function of solution conditions. We determine the BI and BIII secondary structure contents by using the quantitative UV Raman methodology of Chi et al. (1998. Biochemistry. 37:2854-2864). Our findings suggest that the alpha-helix turn in BIII at neutral pH is stabilized by a salt bridge between residues Asp2 and Lys5. This initial alpha-helical turn results in different BI and BIII alpha-helical folding mechanisms observed in high pH and high salt concentrations: BIII folds from its single alpha-helix turn close to its N-terminal, whereas the BI alpha-helix probably nucleates within the C-terminal half. We also used quasielastic light scattering to demonstrate that the BI and BIII alpha-helix formation in 0.2 M Ca(ClO4)2 is accompanied by formation of trimers and hexamers, respectively.     

Circular dichroism and laser Raman spectroscopic analysis of the secondary structure ofCerebratulus lacteus toxin B-IV

The secondary structure ofCerebratulus lacteus toxin B-IV, a neurotoxic polypeptide containing 55 amino acid residues and four disulfide bonds, was experimentally estimated by computer analyses of toxin circular dichroism (CD) and laser Raman spectra. The CD spectrum of the toxin displayed typical -helical peaks at 191, 208, and 222 nm. At neutralpH, the -helix estimates from CD varied between 49 and 55%, when nonrepresentative spectrum analytical methods were used. Analysis of the laser Raman spectrum obtained at a much higher toxin concentration yielded a 78% -helix estimate. Both CD and Raman spectroscopic methods failed to detect any -sheet structure. The spectroscopic analyses revealed significantly more -helix and less -sheet for toxin B-IV than was predicted from its sequence. To account for the difference between the 49–55% helix estimate from CD spectra and the 78% helix estimate from the Raman spectrum, we postulate that some terminal residues are unfolded at the low toxin concentrations used for CD measurements but form helix at the high toxin concentration used for Raman measurements. Our CD observations showing thatCerebatulus toxin B-IV helix content increases about 15% in trifluoroethanol or at highpH are consistent with this interpretation.     

Assigning secondary structure from protein coordinate data.

We have developed a program to convert the three dimensional coordinates describing protein structure in the Brookhaven Data Bank into an assignment of secondary structure. The program assigns secondary structure in the same way a person assigns structure visually. It uses two angles and three distances to assign alpha-helix, 3(10)-helix, beta-strand, hydrogen-bonded beta-turn, non-hydrogen-bonded beta-turn, and poly (L-proline) II type 3(1)-helix. The program is concerned with amide-amide interactions and should be particularly useful to spectroscopists.     

Significance of aromatic-backbone amide interactions in protein structure

Weakly polar interactions between aromatic rings of amino acids and hydrogens of backbone amides (Ar-HN) have been shown to support local structures in proteins. Their role in secondary structures, however, has not been elucidated. To investigate the relationship between Ar-HN interaction and the stability of local and secondary structures of polypeptides and to improve the prediction of this interaction based on amino acid sequence, the structures of 560 nonhomologous proteins, from the Protein Data Bank, were searched for Ar-HN interactions between the aromatic ring of each Phe, Tyr, and Trp residue at position i and the backbone amide group of any residue, except Pro, at the positions i, i - 1, i - 2, i - 3, i + 1, i + 2, and i + 3. Ar-HN interactions were identified by calculating the chemical shift of the amide hydrogen caused by the proximal aromatic ring. Ar(i)-HN(i + 1, i + 2 and i + 3) interactions were more common (7.10%, 2.08%, and 0.54%, respectively) than were Ar(i)-HN(i - 1, i - 2, and i - 3) interactions (0.66%, <0.1%, and 0.18%, respectively). The value of the chi(1) torsion angle of the aromatic residue in position i depended on the direction of the Ar-HN interaction. The position of the aromatic ring in Ar(i)-HN(i + 1, i + 2, and i + 3) interactions was mostly trans, in Ar(i)-HN(i - 1, i - 2, and i - 3) interactions mainly gauche(-), and in Ar(i)-HN(i) interactions mostly gauche(+). The analyses of the secondary structures of the protein fragments containing Ar-HN interactions showed that Ar-HN interactions were in all types of secondary structures. Search results suggest that Ar-HN interactions have a stabilizing effect on all types of secondary structures.     

Prediction of protein secondary structure from amino acid sequence

The conformational parametersP _k for each amino acid species (j=1–20) of sequential peptides in proteins are presented as the product ofP _i,k , wherei is the number of the sequential residues in thekth conformational state (k=-helix,-sheet,-turn, or unordered structure). Since the average parameter for ann-residue segment is related to the average probability of finding the segment in the kth state, it becomes a geometric mean of (P _k )_av=(P _i,k ) ^1/n with amino acid residuei increasing from 1 ton. We then used ln(P_k)_av to convert a multiplicative process to a summation, i.e., ln(P _k ) _av =(1/n)P _i,k (i=1 ton) for ease of operation. However, this is unlike the popular Chou-Fasman algorithm, which has the flaw of using the arithmetic mean for relative probabilities. The Chou-Fasman algorithm happens to be close to our calculations in many cases mainly because the difference between theirP _k and our InP _k is nearly constant for about one-half of the 20 amino acids. When stronger conformation formers and breakers exist, the difference become larger and the prediction at the N- and C-terminal-helix or-sheet could differ. If the average conformational parameters of the overlapping segments of any two states are too close for a unique solution, our calculations could lead to a different prediction.     

Estimation of protein secondary structure from circular dichroism spectra: inclusion of denatured proteins with native proteins in the analysis

We have expanded our reference set of proteins used in the estimation of protein secondary structure by CD spectroscopy from 29 to 37 proteins by including 3 additional globular proteins with known X-ray structure and 5 denatured proteins. We have also modified the self-consistent method for analyzing protein CD spectra, SELCON3, by including a new selection criterion developed by W. C. Johnson, Jr. (Proteins Struct. Funct. Genet. 35, 307-312, 1999). The secondary structure corresponding to the denatured proteins was approximated to be 90% unordered, owing to the spectral similarity of the denatured proteins and unordered structures. We examined the thermal denaturation of ribonuclease T1 by CD using both the original and expanded sets of reference proteins and obtained more consistent results with the expanded set. The expanded set of reference proteins will be helpful for the determination of protein secondary structure from protein CD spectra with higher reliability, especially of proteins with significant unordered structure content and/or in the course of denaturation.     

Implications of protein structure instability: from physiological to pathological secondary structure

Proteins are folded during their synthesis; this process may be spontaneous or assisted. Both phenomena are carefully regulated by the "housekeeping" mechanism and molecular chaperones to avoid the appearance of misfolded proteins. Unfolding process generally occurs during physiological degradation of protein, but in some specific cases it results from genetic or environmental changes and does not correspond to metabolic needs. The main outcome of these phenomena is the appearance of nonfunctional pathologically unfolded proteins with a strong tendency to aggregation. Moreover, for some of these unfolded proteins, the agglomeration that follows initial proteins association may give rise to highly structured soluble aggregates. These aggregates have been identified as the main cause of the so-called amyloidosis or amyloid diseases, such as Alzheimer's, Parkinson's, and Creutzfeldt-Jakob diseases, and type II diabetes mellitus. Although some common mechanisms of amyloid protein aggregation have been identified, the roles of the environmental conditions inducing amyloidosis remain to be clarified. In this review, we will summarize recent studies identifying the origin of amyloid nucleation and will try to predict the therapeutic prospects that may be opened by elucidation of the amyloidosis mechanisms.