Estimation of protein secondary structure and error analysis from circular dichroism spectra

IHM van Stokkum, HJW Spoelder, M Bloemendal… - Analytical …, 1990 - Elsevier
IHM van Stokkum, HJW Spoelder, M Bloemendal, R Van Grondelle, FCA Groen
Analytical biochemistry, 1990Elsevier
The estimation of protein secondary structure from circular dichroism spectra is described by
a multivariate linear model with noise (Gauss-Markoff model). With this formalism the
adequacy of the linear model is investigated, paying special attention to the estimation of the
error in the secondary structure estimates. It is shown that the linear model is only adequate
for the α-helix class. Since the failure of the linear model is most likely due to nonlinear
effects, a locally linearized model is introduced. This model is combined with the selection of …
The estimation of protein secondary structure from circular dichroism spectra is described by a multivariate linear model with noise (Gauss-Markoff model). With this formalism the adequacy of the linear model is investigated, paying special attention to the estimation of the error in the secondary structure estimates. It is shown that the linear model is only adequate for the α-helix class. Since the failure of the linear model is most likely due to nonlinear effects, a locally linearized model is introduced. This model is combined with the selection of the estimate whose fractions of secondary structure summate to approximately one. Comparing the estimation from the CD spectra with the X-ray data (by using the data set of W. C. Johnson Jr., 1988, Annu. Rev. Biophys. Chem. 17, 145–166) the root mean square residuals are 0.09 (α-helix), 0.12 (anti-parallel β-sheet), 0.08 (parallel β-sheet), 0.07 (β-turn), and 0.09 (other). These residuals are somewhat larger than the errors estimated from the locally linearized model. In addition to α-helix, in this model the β-turn and “other” class are estimated adequately. But the estimation of the antiparallel and parallel β-sheet class remains unsatisfactory. We compared the linear model and the locally linearized model with two other methods (S. W. Provencher and J. Glöckner, 1981, Biochemistry 20, 1085–1094; P. Manavalan and W. C. Johnson Jr., 1988, Anal. Biochem. 167, 76–85). The locally linearized model and the Provencher and Glöckner method provided the smallest residuals. However, an advantage of the locally linearized model is the estimation of the error in the secondary structure estimates.
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