Protein secondary structure, methods

P Stolorz, A Lapedes, Y Xia. Predicting protein secondary structure using neural net and statistical methods. J Mol Biol 225 363-377, 1992. 

Our band shape methods have made use of the principal component method of factor analysis (Pancoska etal., 1979 Malinowski, 1991) to characterize the protein spectra in terms of a relatively small number of coefficients (loadings) (Pancoska et al., 1994 1995 Baumruk et al., 1996). This approach is similar, in its initial stages, to various methods (Selcon, Variselect, etc.) that have been used for determining protein secondary structure from ECD data (Hennessey and Johnson, 1981 Provencher and Glockner, 1981 Johnson, 1988 Pancoska and Keiderling, 1991 Sreerama and Woody, 1993, 1994 Venyaminov and Yang, 1996). At this point, one can say these traditional quantitative methods have had little impact upon structural studies of denatured proteins. 

The effect of formalin-treatment on the structural properties of RNase A was examined using circular dichroism (CD) spectropolarimetry. A brief introduction to CD spectropolarimetry is provided in Section 15.15.2 for those readers unfamiliar with this biophysical method. The secondary structure of RNase A consists of one long four-stranded anti-parallel p-sheet and three short a-helixes,44 which places RNase A in the a + p structural class of proteins. The effect of a 9-day incubation of RNase A (6.5mg/mL) in 10% formalin on the protein secondary structure was examined with CD spectropolarimetry in the far-UV region (170-240nm) as shown in Figure 15.6a. The resulting... 

Pelton, J. T., and McLean, L. R. (2000). Spectroscopic methods for analysis of protein secondary structure. Anal. Biochem. 277, 167-176. 

D. S. Wishart, B. D. Sykes and F. M. Richards, The chemical shift index A fast and simple method for the assignment of protein secondary structure through NMR spectroscopy, Biochemistry, 1992, 31, 1647-1651. 

In the literature Raman spectroscopy has been used to characterize protein secondary structure using reference intensity profile method (Alix et al. 1985). A set of 17 proteins was studied with this method and results of characterization of secondary structures were compared to the results obtained by x-ray crystallography methods. Deconvolution of the Raman Amide I band, 1630-1700 cm-1, was made to quantitatively analyze structures of proteins. This method was used on a reference set of 17 proteins, and the results show fairly good correlations between the two methods (Alix et al. 1985). 

There is substantial history regarding the application of conventional vibrational spectroscopy methods to study the intact surface of skin, the extracted stratum corneum and the ceramide-cholesterol-fatty acid mixtures that constitute the primary lipid components of the barrier. The complexity of the barrier and the multiple phases formed by the interactions of the barrier components have begun to reveal the role of each of these substances in barrier structure and stability. The use of bulk phase IR to monitor lipid phase behavior and protein secondary structures in the epidermis, as well as in stratum corneum models, is also well established 24-28 In addition, in vivo and ex vivo attenuated total reflectance (ATR) techniques have examined the outer layers of skin to probe hydration levels, drug delivery and percutaneous absorption at a macroscopic level.29-32 Both mid-IR and near-IR spectroscopy have been used to differentiate pathological skin samples.33,34 The above studies, and many others too numerous to mention, lend confidence to the fact that the extension to IR imaging will produce useful results. 

A number of servers offer various methods to predict secondary structures of proteins. Secondary structure prediction of ExPASy Proteomic tools (http //... 

Sreerama, N. and Woody, R.W. 2000. Estimation of protein secondary structure from circular dichroism spectra Comparison of CONTIN, SELCON, and CDSSTR methods with an expanded reference set. Anal Biochem 287(2) 252-260. 

Source Data from Liljas A, Rossmann MG. X-ray studies of protein interactions. Annu Rev Biochem 43 475-505, 1974 Argos P, Schwarz JS, Schwarz J. An assessment of protein secondary structure prediction methods based on amino acid sequence. Biochim Biophys Acta 439 261-273, 1976. 

A widely used approach to extract information on protein secondary structure from infrared spectra is linked to computational techniques of Fourier deconvolution. These methods decrease the widths of infrared bands, allowing for increased separation and thus better identification of overlapping component bands present under the composite wide contour in the measured spectra [705]. Increased separation can also be achieved by calculating the nth derivative of the absorption spectrum, either in the frequency domain or though mathematical manipulations in the Fourier domain [114], An example is the method of Susi [775] which uses second derivative FT-IR spectra recorded in D20 for comparison with similar spectra derived from proteins with known structure. These methods have not yielded quantitative results that are more accurate than those obtained with methods that do not use deconvolution. 

Gamier, J Gibrat, J. F. Robson, B. (1996). GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol 266,540-53. 

Neural network method is often quoted as a data-driven method. The weights are adjusted on the basis of data. In other words, neural networks learn from training examples and can generalize beyond the training data. Therefore, neural networks are often applied to domains where one has little or incomplete understanding of the problem to be solved, but where training data is readily available. Protein secondary structure prediction is one such example. Numerous rules and statistics have been accumulated for protein secondary structure prediction over the last two decades. Nevertheless, these... 

Manavalan P., Johnson W.C. Jr. (1987) Variable Selection Method Improves the Prediction of Protein Secondary Structure from Circular Dichroism Spectra, Anal. Biochem. 167, 76-85. 

Yi TM, Lander ES. Protein secondary structure prediction using nearest neighbor methods. J. Mol. Biol. 1993 232 1117-1129. de Dios AC, Pearson JG, Oldfield E. Secondary and tertiary structural effects on protein NMR chemical shifts an ab initio approach. Science 1993 260 1491-1496. 

The partial least squares methods (PLSl, and PLS2) used for analysis of secondary structure have been discussed in detail by Haaland and Thomas (18). The software package PLSPlus version 2.1G for GRAMS/386 was purchased from Galactic Industries. The PLS2 algorithm, because of its speed, was used to determine optimal parameters, after which PLSl was used for prediction of protein secondary structure. 

Figure 3. Multidimensional scaling analysis of the dissimilarities between accuracies of different protein secondary structure prediction methods. The method codes can be found in Table I.

The present analysis might give rise to a somewhat pessimistic view of the effectiveness of protein secondary structure prediction algorithms. In fact, with the increasing number of proteins with known three-dimensional structure, constant re-evaluation of performance must take place in order to ascertain the validity of the methods. We note that the methods do not have the predictive power claimed by its authors when analyzed consistently using the 148 proteins selected in this study. Moreover, the situation is even worse for the Mathews correlation coefficient, which indicates that the predictions are poorly correlated with the actual structure. 

Cuff, J. A. and G. J. Barton, Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins, 1999. 34(4) p. 508-19. 

Hua, S. Sun, Z. (2001). A novel method of protein secondary structure prediction with high segment overlap measure Support Vector Machine approach. J Mol Biol 308(2), 397-407. 

A rapid FTIR method for the direct determination of the casein/whey ratio in milk has also been developed [26]. This method is unique because it does not require any physical separation of the casein and whey fractions, but rather makes use of the information contained in the whole spectrum to differentiate between these proteins. Proteins exhibit three characteristic absorption bands in the mid-infrared spectrum, designated as the amide I (1695-1600 cm-i), amide II (1560-1520 cm-i) and amide III (1300-1230 cm >) bands, and the positions of these bands are sensitive to protein secondary structure. From a structural viewpoint, caseins and whey proteins differ substantially, as the whey proteins are globular proteins whereas the caseins have little secondary structure. These structural differences make it possible to differentiate these proteins by FTIR spectroscopy. In addition to their different conformations, other differences between caseins and whey proteins, such as their differences in amino acid compositions and the presence of phosphate ester linkages in caseins but not whey proteins, are also reflected in their FTIR spectra. These spectroscopic differences are illustrated in Figure 15, which shows the so-called fingerprint region in the FTIR spectra of sodium caseinate and whey protein concentrate. Thus, FTIR spectroscopy can provide a means for quantitative determination of casein and whey proteins in the presence of each other. 

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