Over the last few months I’ve been developing an interactive molecular dynamics platform that supports Virtual Reality (VR). Using the Nano Simbox framework, I can run a research grade GPU-accelerated molecular dynamics simulation (OpenMM) and visualise it in VR.
Molecular simulations are incredibly complex systems as every atom can interact with every other atom in 3D. For example, many drug design problems are akin to a sort of “3D tetris”, where you try to find a drug with the right shape such that it fit snugly into an enzyme’s active site. Virtual reality is a natural environment for exploring these systems, as the inherently 3D nature of VR interaction means we can at last manipulate the system in an intuitive way.
We’ve experimented with a variety of VR solutions, and have found the HTC Vive to be the most robust and enjoyable to use. The fact that you can freely walk around the space and that the controllers are tracked extremely well enables powerful interaction with a simulation. Pulling the triggers on the controllers results in a “force probe” being applied to the selected atoms, meaning you can influence the simulation in a physically meaningful way.
The visualisation and interaction tool we’ve created opens up some exciting prospects. Simply exploring the molecular structure in 3D and observing how the system responds to interaction can be a powerful way of gaining insight into its mechanisms, but I believe we can take this further.
One of the biggest problems in molecular simulations is the so called ‘rare event problem’: interesting events going from one molecular configuration to another (e.g. protein folding, chemical reactions) may occur on the order of milliseconds or, while we typically our simulations are restricted to the order nanoseconds due to the computational intensity of calculating the interactions between all the atoms. In order to compute metrics that can give insight into the system and be compared against experiment, the event has to be sampled many times in order to converge statistics. This has led to a proliferation of methods that attempt to accelerate the occurrence of rare events so that many short simulations can be used to capture the rare event. In previous work, I made some improvements the Boxed Molecular Dynamics (BXD) algorithm, which is an example of one of these methods.
The problem with many of these methods is that they usually require the researcher to set up in advance a set of variables, called collective variables or reaction coordinates, that govern the event of interest. For example, in a simulation of a drug binding to an enzyme, one of the obvious variables governing the binding is the distance of the drug from the active site: it clearly needs to be minimised. However, there may be other more subtle variables as well, such as the angle of the drug as it approaches the protein, or the position of a particle side-chain of a protein. Determining what these collective variables are requires a mixture of chemical intuition and a large amount of trial and error on the part of the researcher, and limits the ability to automate molecular simulations. For simulations of large biomolecules such as proteins, identifying these collective variables can be extremely difficult as the concerted motions between the atoms are incredibly complex. For example, 1% of proteins in the Protein Data Bank are knotted, but it is not clear why or how they end up in this state.
There are methods that attempt to automatically identify the important collective variables for a particular system, but they typically require an initial path between the states of the system. How do you find this initial path if you don’t know what the
important collective variables are? Finding these paths are exactly what interactive molecular dynamics could be useful for.
In the coming months, I’ll be seeing if we can use interactive molecular dynamics with virtual reality to enable researchers to find paths in molecular simulations, which can then be passed on to path refining and collective variable analysis methods. Combining human intuition with automated methods in this way lead to a workflow that provides enhanced insight to chemical problems more quickly.