title: Modeling molecular kinetics with tICA and the kernel trick.
abstract: The allure of a molecular dynamics simulation is that, given a sufficiently accurate force field, it can provide an atomic-level view of many interesting phenomena in biology. However, the result of a simulation is a large, high-dimensional time series that is difficult to interpret. Recent work has introduced the time-structure based Independent Components Analysis (tICA) method for analyzing MD, which attempts to find the slowest decorrelating linear functions of the molecular coordinates. This method has been used in conjunction with Markov State Models (MSMs) to provide estimates of the characteristic eigenprocesses contained in a simulation (e.g., protein folding, ligand binding). Here, we extend the tICA method using the kernel trick to arrive at nonlinear solutions. This is a substantial improvement as it allows for kernel-tICA (ktICA) to provide estimates of the characteristic eigenprocesses directly without building an MSM.
authors.affiliation: Department of Chemistry, Stanford University, Stanford, California 94305, United States
authors.forename: Christian R
authors.initials: CR
authors.lastname: Schwantes
authors.forename: Vijay S
authors.initials: VS
authors.lastname: Pande
mesh_terms: D004151:Dipeptides / Q000737:chemistry; D007700:Kinetics; D008958:Models, Molecular; D015233:Models, Statistical; D056004:Molecular Dynamics Simulation; D013997:Time Factors
chemical_list: D004151:Dipeptides