title: Going beyond clustering in MD trajectory analysis: an application to villin headpiece folding.
abstract: Recent advances in computing technology have enabled microsecond long all-atom molecular dynamics (MD) simulations of biological systems. Methods that can distill the salient features of such large trajectories are now urgently needed. Conventional clustering methods used to analyze MD trajectories suffer from various setbacks, namely (i) they are not data driven, (ii) they are unstable to noise and changes in cut-off parameters such as cluster radius and cluster number, and (iii) they do not reduce the dimensionality of the trajectories, and hence are unsuitable for finding collective coordinates. We advocate the application of principal component analysis (PCA) and a non-metric multidimensional scaling (nMDS) method to reduce MD trajectories and overcome the drawbacks of clustering. To illustrate the superiority of nMDS over other methods in reducing data and reproducing salient features, we analyze three complete villin headpiece folding trajectories. Our analysis suggests that the folding process of the villin headpiece is structurally heterogeneous.
authors.affiliation: Department of Physics, University of Illinois, Urbana-Champaign, Illinois, United States of America.
authors.forename: Aruna
authors.initials: A
authors.lastname: Rajan
authors.forename: Lydia
authors.initials: L
authors.lastname: Freddolino
authors.forename: Klaus
authors.initials: K
authors.lastname: Schulten
mesh_terms: D016000:Cluster Analysis; D008840:Microfilament Proteins / Q000737:chemistry*; D056004:Molecular Dynamics Simulation; D025341:Principal Component Analysis; D013223:Statistics as Topic / Q000379:methods* / Q000592:standards
chemical_list: D008840:Microfilament Proteins; C026615:villin