High-resolution (13)C solid-state NMR stands out as one of the most promising techniques to solve the structure of insoluble proteins featuring biological and technological importance. The simplest nuclear magnetic resonance (NMR) spectroscopy method to quantify the secondary structure of proteins uses the areas of carbonyl and alpha carbon peaks. The quantification obtained by fitting procedures depends on the assignment of the peaks to the structure, type of line shape, number of peaks to be used, and other parameters that are set by the operator. In this paper, we demonstrate that the analysis of (13)C NMR spectra by a pattern recognition method-based on the singular value decomposition (SVD) regression, which does not depend on the operator-shows higher correlation coefficients for α-helix and β-sheet (0.96 and 0.91, respectively) than Fourier transform infrared spectroscopy (FTIR) method. Therefore, the use of (13)C solid-state NMR spectra and SVD is a simple and reliable method for quantifying the secondary structures of insoluble proteins in solid-state.
Keywords: 13C solid-state NMR; Insoluble protein; Protein secondary structure; Singular value decomposition.