Human learning for molecular simulations: the Collective Variables Dashboard in VMD

TL;DR

The paper introduces the Collective Variables Dashboard in VMD, a software tool that enables real-time exploration and analysis of molecular structures in collective variable space, aiding in enhanced sampling and simulation analysis.

Contribution

It presents a new integrated graphical interface for designing and analyzing collective variables within VMD, streamlining workflows for molecular simulation studies.

Findings

Facilitates intuitive exploration of collective variable space.

Supports design and validation of collective variables for simulations.

Demonstrated on ligand unbinding and hydration bias applications.

Abstract

The Collective Variables Dashboard is a software tool for real-time, seamless exploration of molecular structures and trajectories in a customizable space of collective variables. The Dashboard arises from the integration of the Collective Variables Module with the visualization software VMD, augmented with a fully discoverable graphical interface offering interactive workflows for the design and analysis of collective variables. Typical use cases include a priori design of collective variables for enhanced sampling and free energy simulations as well as post-mortem analysis of any type of simulation or collection of structures in a collective variable space. A combination of those cases commonly occurs when preliminary simulations, biased or unbiased, reveal that an optimized set of collective variables is necessary to improve sampling in further simulations. Then the Dashboard provides an efficient way to intuitively explore the space of likely collective variables, validate them on existing data, and use the resulting collective variable definitions directly in further biased simulations using the Collective Variables Module. We illustrate the use of the Dashboard on two applications: discovering coordinates to describe ligand unbinding from a protein binding site, and designing volume-based variables to bias the hydration of a transmembrane pore.