title: 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 principal component analysis is not worse than the non-linear ones on this complex system. There is no clear winner in all aspects of the evaluation. Each dimensionality-reduction method has its limitations in a certain aspect. We emphasize that a fair, informative assessment of an embedding result requires a combination of multiple evaluation criteria rather than any single one. Caution should be used when dimensionality-reduction methods are employed, especially when only a few of top embedding dimensions are used to describe the free energy landscape.
authors.affiliation: Gustaf H. Carlson School of Chemistry and Biochemistry, Clark University, Worcester, MA 01610 USA.
authors.forename: Mojie
authors.initials: M
authors.lastname: Duan
authors.forename: Jue
authors.initials: J
authors.lastname: Fan
authors.forename: Minghai
authors.initials: M
authors.lastname: Li
authors.forename: Li
authors.initials: L
authors.lastname: Han
authors.forename: Shuanghong
authors.initials: S
authors.lastname: Huo
keywords: Isomap; diffusion maps; dimensionality reduction; free energy profile; locally linear embedding; molecular dynamics simulations; principal component analysis