Predictions of protein secondary structures using factor analysis on Fourier transform infrared spectra: effect of Fourier self-deconvolution of the amide I and amide II bands.
Fourier self-deconvolution (FSD) was performed on protein amide I and II Fourier transform infrared (FTIR) spectra to test if the resultant increased band shape variation would lead to improvements in protein secondary structure prediction with our factor analysis based restricted multiple regression (RMR) methods. FTIR spectra of 23 proteins dissolved in H2O were measured and normalized to a constant amide I peak absorbance. The deconvolved spectra were renormalized by area so that the deconvolved spectra sets had the same area as before. Principal component analysis of the deconvolved spectra sets was carried out, which was followed by a selective multiple linear regression (RMR) analysis of the principal component loadings with regard to the fractional components (FC) of secondary structure. As compared to analyses based on the original spectra set, helix and sheet predictions were not noticeably improved by FSD; but, if a very large number of component spectra (16) were retained in the pool to select which loadings to be used in the RMR optimization, better predictions of turn and "other" resulted. The prediction quality varied depending on the deconvolution parameters used.