Complexity of free energy landscapes of peptides revealed by nonlinear principal component analysis

@article{citeulike:967049, abstract = {Employing the recently developed hierarchical nonlinear principal component analysis (NLPCA) method of Saegusa et al. (Neurocomputing 2004;61:57-70 and IEICE Trans Inf Syst 2005;E88-D:2242-2248), the complexities of the free energy landscapes of several peptides, including triglycine, hexaalanine, and the C-terminal ?-hairpin of protein G, were studied. First, the performance of this NLPCA method was compared with the standard linear principal component analysis (PCA). In particular, we compared two methods according to (1) the ability of the dimensionality reduction and (2) the efficient representation of peptide conformations in low-dimensional spaces spanned by the first few principal components. The study revealed that NLPCA reduces the dimensionality of the considered systems much better, than did PCA. For example, in order to get the similar error, which is due to representation of the original data of ?-hairpin in low dimensional space, one needs 4 and 21 principal components of NLPCA and PCA, respectively. Second, by representing the free energy landscapes of the considered systems as a function of the first two principal components obtained from PCA, we obtained the relatively well-structured free energy landscapes. In contrast, the free energy landscapes of NLPCA are much more complicated, exhibiting many states which are hidden in the PCA maps, especially in the unfolded regions. Furthermore, the study also showed that many states in the PCA maps are mixed up by several peptide conformations, while those of the NLPCA maps are more pure. This finding suggests that the NLPCA should be used to capture the essential features of the systems. Proteins 2006. {\copyright} 2006 Wiley-Liss, Inc.}, address = {Institute of Physical and Theoretical Chemistry, J.W. Goethe University, Frankfurt, Germany}, author = {Nguyen, Phuong H. }, citeulike-article-id = {967049}, doi = {10.1002/prot.21185}, journal = {Proteins: Structure, Function, and Bioinformatics}, keywords = {free-energy, hexapeptide, pca}, number = {4}, pages = {898--913}, posted-at = {2008-07-04 18:29:56}, priority = {5}, title = {Complexity of free energy landscapes of peptides revealed by nonlinear principal component analysis}, url = {http://dx.doi.org/10.1002/prot.21185}, volume = {65}, year = {2006} }

TY - JOUR ID - citeulike:967049 L3 - citeulike-article-id:967049 TI - Complexity of free energy landscapes of peptides revealed by nonlinear principal component analysis JF - Proteins: Structure, Function, and Bioinformatics VL - 65 IS - 4 SP - 898 EP - 913 AD - Institute of Physical and Theoretical Chemistry, J.W. Goethe University, Frankfurt, Germany N2 - Employing the recently developed hierarchical nonlinear principal component analysis (NLPCA) method of Saegusa et al. (Neurocomputing 2004;61:57-70 and IEICE Trans Inf Syst 2005;E88-D:2242-2248), the complexities of the free energy landscapes of several peptides, including triglycine, hexaalanine, and the C-terminal ?-hairpin of protein G, were studied. First, the performance of this NLPCA method was compared with the standard linear principal component analysis (PCA). In particular, we compared two methods according to (1) the ability of the dimensionality reduction and (2) the efficient representation of peptide conformations in low-dimensional spaces spanned by the first few principal components. The study revealed that NLPCA reduces the dimensionality of the considered systems much better, than did PCA. For example, in order to get the similar error, which is due to representation of the original data of ?-hairpin in low dimensional space, one needs 4 and 21 principal components of NLPCA and PCA, respectively. Second, by representing the free energy landscapes of the considered systems as a function of the first two principal components obtained from PCA, we obtained the relatively well-structured free energy landscapes. In contrast, the free energy landscapes of NLPCA are much more complicated, exhibiting many states which are hidden in the PCA maps, especially in the unfolded regions. Furthermore, the study also showed that many states in the PCA maps are mixed up by several peptide conformations, while those of the NLPCA maps are more pure. This finding suggests that the NLPCA should be used to capture the essential features of the systems. Proteins 2006. © 2006 Wiley-Liss, Inc. KW - free-energy KW - hexapeptide KW - pca AU - Nguyen, Phuong PY - 2006/// UR - http://dx.doi.org/10.1002/prot.21185 ER -