Markov state models (MSMs) are a powerful framework for analyzing dynamical systems,
such as molecular dynamics (MD) simulations, that have gained widespread use over the …

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 …

There is an increasing demand for computing the relevant structures, equilibria, and long-
timescale kinetics of biomolecular processes, such as protein-drug binding, from high …

Markov (state) models (MSMs) and related models of molecular kinetics have recently
received a surge of interest as they can systematically reconcile simulation data from either …

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

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

A density-based clustering method is proposed that is deterministic, computationally
efficient, and self-consistent in its parameter choice. By calculating a geometric coordinate …

Machine learning (ML) has been an important arsenal in computational biology used to
elucidate protein function for decades. With the recent burgeoning of novel ML methods and …

Calmodulin (CaM) is a ubiquitous Ca2+ sensor and a crucial signalling hub in many
pathways aberrantly activated in disease. However, the mechanistic basis of its ability to …

Identification of the collective coordinates that describe rare events in complex molecular
transitions such as protein folding has been a key challenge in the theoretical molecular …