Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics

TLDR

Modeling macromolecular conformational dynamics over long timescales requires discrete‑state Markov models, which in turn depend on decomposing configuration space into kinetically metastable states; prior approaches rely on prior knowledge or clustering that assumes kinetic distinctness. We introduce an automatic algorithm that discovers kinetically metastable states for solvated macromolecules without prior knowledge. The algorithm iteratively partitions and aggregates conformation space from molecular dynamics trajectories to identify long‑lived, kinetically related regions. Applied to three peptides, the algorithm produced physically meaningful states and faithful kinetic models, demonstrating its general applicability.

Abstract

To meet the challenge of modeling the conformational dynamics of biological macromolecules over long time scales, much recent effort has been devoted to constructing stochastic kinetic models, often in the form of discrete-state Markov models, from short molecular dynamics simulations. To construct useful models that faithfully represent dynamics at the time scales of interest, it is necessary to decompose configuration space into a set of kinetically metastable states. Previous attempts to define these states have relied upon either prior knowledge of the slow degrees of freedom or on the application of conformational clustering techniques which assume that conformationally distinct clusters are also kinetically distinct. Here, we present a first version of an automatic algorithm for the discovery of kinetically metastable states that is generally applicable to solvated macromolecules. Given molecular dynamics trajectories initiated from a well-defined starting distribution, the algorithm discovers long lived, kinetically metastable states through successive iterations of partitioning and aggregating conformation space into kinetically related regions. The authors apply this method to three peptides in explicit solvent—terminally blocked alanine, the 21-residue helical Fs peptide, and the engineered 12-residue β-hairpin trpzip2—to assess its ability to generate physically meaningful states and faithful kinetic models.