T-Distributed Stochastic Neighbor Embedding Method with the Least Information Loss for Macromolecular Simulations.

ABSTRACT: Dimensionality reduction methods are usually applied on molecular dynamics simulations of macromolecules for analysis and visualization purposes. It is normally desired that suitable dimensionality reduction methods could clearly distinguish functionally important states with different conformations for the systems of interest. However, common dimensionality reduction methods for macromolecules simulations, including predefined order parameters and collective variables (CVs), principal component analysis (PCA), and time-structure based independent component analysis (t-ICA), only have limited success due to significant key structural information loss. Here, we introduced the t-distributed stochastic neighbor embedding (t-SNE) method as a dimensionality reduction method with minimum structu

SUBMITTER: Zhou H 

PROVIDER: S-EPMC6679899 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature