title: Detecting coupled collective motions in protein by independent subspace analysis.
abstract: Protein dynamics evolves in a high-dimensional space, comprising aharmonic, strongly correlated motional modes. Such correlation often plays an important role in analyzing protein function. In order to identify significantly correlated collective motions, here we employ independent subspace analysis based on the subspace joint approximate diagonalization of eigenmatrices algorithm for the analysis of molecular dynamics (MD) simulation trajectories. From the 100 ns MD simulation of T4 lysozyme, we extract several independent subspaces in each of which collective modes are significantly correlated, and identify the other modes as independent. This method successfully detects the modes along which long-tailed non-Gaussian probability distributions are obtained. Based on the time cross-correlation analysis, we identified a series of events among domain motions and more localized motions in the protein, indicating the connection between the functionally relevant phenomena which have been independently revealed by experiments.
authors.affiliation: Graduate School of Frontier Sciences, The University of Tokyo, Tokyo 113-0032, Japan.
authors.forename: Shun
authors.initials: S
authors.lastname: Sakuraba
authors.forename: Yasumasa
authors.initials: Y
authors.lastname: Joti
authors.forename: Akio
authors.initials: A
authors.lastname: Kitao
mesh_terms: D000465:Algorithms; D017122:Bacteriophage T4 / Q000201:enzymology*; D056004:Molecular Dynamics Simulation; D009068:Movement; D009113:Muramidase / Q000737:chemistry* / Q000378:metabolism
chemical_list: D009113:Muramidase