Determination of Protein Secondary Structure from Infrared Spectra Using Partial Least-Squares Regression

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 ∼12% for α-helix, ∼7% for β-sheet, 7% for antiparallel β-sheet,
and ∼8% 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, β-sheet, other, and antiparallel β-sheet contents
with good accuracy, comparable to that of circular dichroism, which is widely used for this purpose.