Application of the local regression method interval partial least-squares to the elucidation of protein secondary structure

Abstract

The infrared amide bands are sensitive to the conformation of the polypeptide backbone of proteins. Since the backbone of proteins folds in complex spatial arrangements, the amide bands of these proteins result from the superimposition of vibration modes corresponding to the different types of structural motifs (α helices, β sheets, etc.). Initially, band deconvolution techniques were applied to determine the secondary structure of proteins, i.e., the abundance of each structural motif in the polypeptide chain was directly related to the area of the suitable deconvolved vibration modes under the amide I band (1700–1600 cm −1). Recently, several multivariate regression methods have been used to predict the secondary structure of proteins as an alternative to the previous methods. They are based on establishing a relationship between a matrix of infrared protein spectra and another that includes their secondary structure, expressed as the fractions of the different structural motifs, determined from X-ray analysis. In this study, we investigated the use of the local regression method interval partial least-squares (iPLS) to seek improvements to the full-spectrum PLS and other regression methods. The local character of iPLS avoids the use of spectral regions that can introduce noise or that can be irrelevant for prediction and focuses on finding specific spectral ranges related to each secondary structure motif in all the proteins. This study has been applied to a representative protein data set with infrared spectra covering a large wavenumber range, including amides I–III bands (1700–1200 cm −1). iPLS has revealed new structural mode assignments related to less explored amide bands and has offered a satisfactory predictive ability using a small amount of selected specific spectral information.