Quantitative analysis of vibrational circular dichroism spectra of proteins. Problems and perspectives

Abstract

Experimental and computational aspects of the quantitative analysis of vibrational circular dichroism (VCD) of proteins are discussed. Experimentally, the effect of spectral resolution, sample concentration, cell selection and spectral normalization effects are considered. The influence of random intensity variations on the results of quantitative analysis of amide I' VCD are shown to be minor up to a 15% variation in spectral intensity. A computational algorithm, based on factor analysis of the spectra and multiple linear regression calculation of fractions of secondary structures (FC), was designed to analyse quantitatively the details of the VCD spectra-structure relationship. It also enabled the results of VCD measured independently for the amide I' and amide I1 regions to be combined. Our study is based primarily on the optimization of the calculation to predict FC values for proteins not included in the reference data set used for regression. The best prediction is obtained with the function using only part of the observable independent VCD spectral components. Inclusion of all components actually reduces the prediction accuracy of the analysis. Spectroscopic reasons for such behaviour and the consequences of the interdependence of the crystallographic FC values on the spectra-structure analysis are discussed. Finally, the possibility of utilizing VCD spectra to obtain quantitative structural information about the protein beyond the conventional secondary structure composition is explored. A matrix descriptor of supersecondary structure features for proteins is designed, and preliminary results for prediction of this descriptor from amide I' VCD spectra are presented. These latter calculations use a novel design of the back-propagation neural network.

Enhanced Prediction Accuracy of Protein Secondary Structure Using Hydrogen Exchange Fourier Transform Infrared Spectroscopy

“…Thus, it is the relative degree of deuteration, encompassed in the amide II-IIЈ intensity ratio, folded together with the known secondary structure sensitive amide I-IЈ shape that leads to lower helix prediction errors for an improvement in the overall structure prediction. It is this coupling of information, much as we have shown previously for coupling amide IЈ and II VCD (22,23) and for coupling VCD and ECD (23,24) that leads to significant improvements in structure prediction. As noted earlier, the H/D component spectra tend to separate the commonality of bandshape from the H-Ddependent (see Fig.…”

“…Using our RMR approach (22)(23)(24), the prediction abilities of these various regressions were tested based on the "one-left-out" method, where one protein is systematically removed before developing a regression relation between the loadings, L jm , and the fractional components, FC , where corresponds to helix, sheet, etc.…”

Predictions of Secondary Structure using Statistical Analyses of Electronic and Vibrational Circular Dichroism and Fourier Transform Infrared Spectra of Proteins in H2O

“…In recent years there has been a rebirth of various statistical techniques to derive secondary structural parameters from optical spectroscopic data, such as UV electronic circular dichroism (ECD), Fourier transform IR (FTIR), vibrational CD (VCD) in the IR, and Raman spectra (Manavalan & Johnson, 1987;Perczel et al, 1991;van Stokkum et al, 1990;Toumadje et al, 1992;Sreerama & Woody, 1993Lee et al, 1990;Dousseau & Pezolet, 1990;Venyaminov & Kalnin, 1990;Sarver & Krueger, 1991a,b;Pribic et al, 1993;Pancoska et al, , 1994Pancoska et al, , 1995Williams, 1986;Berjot et al, 1987;Bussian & Sander, 1989). These optical techniques are particularly useful for fast, universal, qualitative estimation of average secondary structure and determination of structural integrity during some biochemical process or environmental change in proteins.…”

Novel Matrix Descriptor for Secondary Structure Segments in Proteins: Demonstration of Predictability from Circular Dichroism Spectra

“…Crystal structures of 23 proteins were obtained from the Brookhaven protein databank, and the secondary structure assignment was performed using the DSSP program (14). The DSSP output was processed by a custom-made Turbo Pascal (Borland) secondary structure manipulation (SSM) program (19), which automates construction of the matrix descriptor [s ij ] as previously described (4,5). For the examples discussed in this paper, residues assigned to ␣-helix and 3 10 helix were lumped into a single H category, parallel and antiparallel ␤-strands into a common E category, and all other secondary structure types into a common C category.…”

“…2 represents the basic algorithm for generation of different forms of the matrix descriptor: At any boundary between assigned secondary structure segments from some objective algorithm such as the DSSP program of Kabsch and Sander (14), the contact structures are inserted with consideration for their directionality. 4 The components included in this matrix descriptor can vary beyond helix and sheet since its design is flexible, being based on generalized segments and their interconnections, and can be easily adapted to match the structural sensitivity of any particular experiment. When, for example, it is desirable to categorize sheet into parallel and antiparallel strands or differentiate helix into ␣-and 3 10 helices or the "other" category as bends, turns, and unordered segments, the corresponding matrix descriptors [s ij ] will have a rank n ϫ n (n Ͼ 3) and could be analogously obtained as described above.…”