Non-Parametric Analysis of Non-Equilibrium Simulations

TL;DR

This paper introduces a non-parametric framework for analyzing non-equilibrium biomolecular simulations, enabling accurate free energy profiling and adaptive sampling without relying on system-specific parameters.

Contribution

It extends reaction coordinate optimization to non-equilibrium ensembles and proposes a new adaptive sampling method called transition state ensemble enrichment.

Findings

Successfully applied to a 50-dimensional model system

Demonstrated on a realistic protein folding trajectory

Framework is immune to curse of dimensionality

Abstract

We extend the non-parametric framework of reaction coordinate optimization to non-equilibrium ensembles of (short) trajectories. For example, we show how, starting from such an ensemble, one can obtain an equilibrium free energy profile along the committor, which can be used to determine important properties of the dynamics exactly. New adaptive sampling approach, the transition state ensemble enrichment, is suggested, which samples the configuration space by "growing" committor segments towards each other starting from the boundary states. This framework is suggested as a general tool, alternative to the Markov state models, for a rigorous and accurate analysis of simulations of large biomolecular systems, as it has the following attractive properties. It is immune to the curse of dimensionality, it does not require system specific information, it can approximate arbitrary reaction coordinates with high accuracy and it has sensitive and rigorous criteria to test optimality and convergence. The approaches are illustrated on a 50-dimensional model system and a realistic protein folding trajectory.