Mendieta J, Díaz-Cruz M S, Esteban M, Tauler R
Department de Química Analítica, Universitat de Barcelona, Spain.
Biophys J. 1998 Jun;74(6):2876-88. doi: 10.1016/S0006-3495(98)77994-9.
Different multivariate data analysis techniques based on factor analysis and multivariate curve resolution are shown for the study of biochemical evolutionary processes like conformational changes and protein folding. Several simulated CD spectral data sets describing different hypothetical protein folding pathways are analyzed and discussed in relation to the feasibility of factor analysis techniques to detect and resolve the number of components needed to explain the evolution of the CD spectra corresponding to the process (i.e., to detect the presence of intermediate forms). When more than two components (the native and unordered forms) are needed to explain the evolution of the spectra, an iterative multivariate curve resolution procedure based on an alternating least squares algorithm is proposed to estimate the CD spectrum corresponding to the intermediate form.
展示了基于因子分析和多元曲线分辨的不同多元数据分析技术,用于研究诸如构象变化和蛋白质折叠等生化进化过程。分析并讨论了几个描述不同假设蛋白质折叠途径的模拟圆二色光谱数据集,这些数据集与因子分析技术检测和分辨解释该过程对应圆二色光谱演化所需成分数量(即检测中间形式的存在)的可行性相关。当需要两个以上成分(天然形式和无序形式)来解释光谱演化时,提出了一种基于交替最小二乘算法的迭代多元曲线分辨程序,以估计对应中间形式的圆二色光谱。