Recent developments in methods for identifying reaction coordinates

State of the art realistic simulations of complex atomic processes commonly produce trajectories of large size, making the development of automated analysis tools very important. A popular approach aimed at extracting dynamical information consists of projecting these trajectories into optimally selected reaction coordinates or collective variables. For equilibrium dynamics between any two boundary states, the committor function also known as the folding probability in protein folding studies is often considered as the optimal coordinate. To determine it, one selects a functional form with many parameters and trains it on the trajectories using various criteria. A major problem with such an approach is that a poor initial choice of the functional form may lead to sub-optimal results. Here, we describe an approach which allows one to optimize the reaction coordinate without selecting its functional form and thus avoiding this source of error.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonparametric variational optimization of reaction coordinates

Banushkina

Krivov

2015

“…Due to the numerous degrees of freedom in complex systems, mechanistic interpretation of trajectories from MD simulations is taxing if not impossible. Reaction coordinates, the few coordinates that can determine the progress of a reaction process, are key to reliable mechanistic understanding 2, 3 and provide important clue for efficient methods for enhanced sampling of configuration space. For example, sampling of transition regions can be significantly improved in methods like umbrella sampling, 4 constrained dynamics, 5,6 metadynamics, 7 and orthogonal space sampling, 8,9 if restraints or constraints are applied to the correct reaction coordinates.…”

Section: Introductionmentioning

confidence: 99%

A benchmark for reaction coordinates in the transition path ensemble

2016

Self Cite

The molecular mechanism of a reaction is embedded in its transition path ensemble, the complete collection of reactive trajectories. Utilizing the information in the transition path ensemble alone, we developed a novel metric, which we termed the emergent potential energy, for distinguishing reaction coordinates from the bath modes. The emergent potential energy can be understood as the average energy cost for making a displacement of a coordinate in the transition path ensemble. Where displacing a bath mode invokes essentially no cost, it costs significantly to move the reaction coordinate. Based on some general assumptions of the behaviors of reaction and bath coordinates in the transition path ensemble, we proved theoretically with statistical mechanics that the emergent potential energy could serve as a benchmark of reaction coordinates and demonstrated its effectiveness by applying it to a prototypical system of biomolecular dynamics. Using the emergent potential energy as guidance, we developed a committor-free and intuition-independent method for identifying reaction coordinates in complex systems. We expect this method to be applicable to a wide range of reaction processes in complex biomolecular systems. C 2016 AIP Publishing LLC. [http://dx

“…This can be tested using committor histograms. 55 Interestingly, even for a simple reaction such as the isomerization of alanine dipeptide, the two dihedral angles (Φ, Ψ) cannot describe correctly the transition and at least three are required. 56,57 However, while the two-dimensional description given by our method bears similarities with the two-dihedral description, it takes into account all atomic positions, and thus implicitly all dihedral angles in the molecule.…”

Section: Resultsmentioning

confidence: 99%

Charting molecular free-energy landscapes with an atlas of collective variables

Hashemian

Millán

Arroyo

2016

Collective variables (CVs) are a fundamental tool to understand molecular flexibility, to compute free energy landscapes, and to enhance sampling in molecular dynamics simulations. However, identifying suitable CVs is challenging, and increasingly addressed with systematic data-driven manifold learning techniques. Here, we provide a flexible framework to model molecular systems in terms of a collection of locally valid and partially overlapping CVs: an atlas of CVs. The specific motivation for such a framework is to enhance the applicability and robustness of CVs based on manifold learning methods, which fail in the presence of periodicities in the underlying conformational manifold. More generally, using an atlas of CVs rather than a single chart may help us better describe different regions of conformational space. We develop the statistical mechanics foundation for our multi-chart description and propose an algorithmic implementation. The resulting atlas of data-based CVs are then used to enhance sampling and compute free energy surfaces in two model systems, alanine dipeptide and β-D-Glucopyranose, whose conformational manifolds have toroidal and spherical topology.