Evaluation of Dimensionality-reduction Methods from Peptide Folding-unfolding Simulations.

ABSTRACT: Dimensionality reduction methods have been widely used to study the free energy landscapes and low-free energy pathways of molecular systems. It was shown that the non-linear dimensionality-reduction methods gave better embedding results than the linear methods, such as principal component analysis, in some simple systems. In this study, we have evaluated several non linear methods, locally linear embedding, Isomap, and diffusion maps, as well as principal component analysis from the equilibrium folding/unfolding trajectory of the second β-hairpin of the B1 domain of streptococcal protein G. The CHARMM parm19 polar hydrogen potential function was used. A series of criteria which reflects different aspects of the embedding qualities were employed in the evaluation. Our results show that pri

SUBMITTER: Duan M 

PROVIDER: S-EPMC3678838 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature