It has recently become practical to construct Markov state models (MSMs) that reproduce the
long-time statistical conformational dynamics of biomolecules using data from molecular …

Markov state models (MSMs) based on molecular dynamics (MD) simulations are routinely
employed to study protein folding, however, their application to functional conformational …

Markov state models (MSMs) have proven valuable in studying the dynamics of protein
conformational changes via statistical analysis of molecular dynamics simulations. In MSMs …

Principal component analysis of molecular dynamics simulations is a popular method to
account for the essential dynamics of the system on a low-dimensional free energy …

The statistical analysis of molecular dynamics simulations requires dimensionality reduction
techniques, which yield a low-dimensional set of collective variables (CVs){xi}= x that in …

The function of complex biomolecular machines relies heavily on their conformational
changes. Investigating these functional conformational changes is therefore essential for …

We present a systematic approach to reduce the dimensionality of a complex molecular
system. Starting with a data set of molecular coordinates (obtained from experiment or …

To interpret molecular dynamics simulations of complex systems, systematic dimensionality
reduction methods such as principal component analysis (PCA) represent a well …

Extracting insight from the enormous quantity of data generated from molecular simulations
requires the identification of a small number of collective variables whose corresponding low …

Markov state models (MSM) are a popular statistical method for analyzing the
conformational dynamics of proteins including protein folding. With all statistical and …