Time-Lagged Independent Component Analysis of Random Walks and Protein Dynamics.

ABSTRACT: Time-lagged independent component analysis (tICA) is a widely used dimension reduction method for the analysis of molecular dynamics (MD) trajectories and has proven particularly useful for the construction of protein dynamics Markov models. It identifies those "slow" collective degrees of freedom onto which the projections of a given trajectory show maximal autocorrelation for a given lag time. Here we ask how much information on the actual protein dynamics and, in particular, the free energy landscape that governs these dynamics the tICA-projections of MD-trajectories contain, as opposed to noise due to the inherently stochastic nature of each trajectory. To answer this question, we have analyzed the tICA-projections of high dimensional random walks using a combination of analytical and

SUBMITTER: Schultze S 

PROVIDER: S-EPMC8444338 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature