RESUMEN
Infrared (IR) spectra contain substantial information about protein structure. This has previously most often been exploited by using known band assignments. Here, we convert spectral intensities in bins within Amide I and II regions to vectors and apply machine learning methods to determine protein secondary structure. Partial least squares was performed on spectra of 90 proteins in H2O. After preprocessing and removal of outliers, 84 proteins were used for this work. Standard normal variate and second-derivative preprocessing methods on the combined Amide I and II data generally gave the best performance, with root-mean-square values for prediction of â¼12% for α-helix, â¼7% for ß-sheet, 7% for antiparallel ß-sheet, and â¼8% for other conformations. Analysis of Fourier transform infrared (FTIR) spectra of 16 proteins in D2O showed that secondary structure determination was slightly poorer than in H2O. Interval partial least squares was used to identify the critical regions within spectra for secondary structure prediction and showed that the sides of bands were most valuable, rather than their peak maxima. In conclusion, we have shown that multivariate analysis of protein FTIR spectra can give α-helix, ß-sheet, other, and antiparallel ß-sheet contents with good accuracy, comparable to that of circular dichroism, which is widely used for this purpose.