ESTIMATION OF PROTEIN SECONDARY STRUCTURE AND ERROR ANALYSIS FROM CIRCULAR-DICHROISM SPECTRA
ESTIMATION OF PROTEIN SECONDARY STRUCTURE AND ERROR ANALYSIS FROM CIRCULAR-DICHROISM SPECTRA
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DOI：
10.1016/0003-2697(90)90396-q
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发表时间：
1990-11-15
影响因子：
2.9
通讯作者：
GROEN, FCA
中科院分区：
文献类型：
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作者：
VANSTOKKUM, IHM;SPOELDER, HJW;GROEN, FCA
用含噪声的多元线性模型（Gauss-Markoff模型）描述了由圆二色光谱估计蛋白质二级结构的方法。有了这种形式主义的线性模型的充分性进行了研究，特别注意二级结构估计的误差估计。结果表明，线性模型仅适用于α-螺旋类。由于线性模型的失效很可能是由于非线性效应，因此引入了局部线性化模型。该模型与二级结构分数总和约为1的估计值的选择相结合。将CD谱估算值与X射线数据（利用W。C.小约翰逊，1988年，Annu.生物物理学17，145-166），均方根残差为0.09（α-螺旋）、0.12（反平行β-折叠）、0.08（平行β-折叠）、0.07（β-转角）和0.09（其他）。这些残差略大于局部线性化模型估计的误差。除了α-螺旋，在这个模型中，β-转角和“其他”类被充分估计。但对反平行和平行β折叠类的估计仍不令人满意。我们比较了线性模型和局部线性化模型与其他两种方法（S。W. Provencher和J. Glöckner，1981，Biochemistry 20，1085-1094; P. Manavalan和W. C.小约翰逊，1988年，阿纳尔。167，76-85）。局部线性化模型和Provencher和Glöckner方法提供了最小的残差。然而，局部线性化模型的优点是二级结构估计中的误差估计。
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.