Much of my research revolves around atomistic simulations of biomolecules, which include proteins, drugs, solvent (e.g. water), and other small molecules that are of biological interest. The simulations model the forces between atoms to predict configurations and dynamical processes. These in turn can be used to estimate quantities that can be verified (or falsified) experimentally. The simulations are atomistic in resolution, giving us an unprecedented level of detail on the systems in question. We can use them to generate hypotheses or to address questions like ‘how does this particular drug bind?’ and ‘how does this particular protein function?’.
Despite being simplified models of the atomistic world, the simulations are highly complex and time-intensive. For a typically-sized protein immersed in water, the simulations have to evaluate the forces between tens of thousands of atoms over hundreds of thousands of iterations. Depending on the system and the questions one is trying to answer, simulations can take days to months. Despite the increasing power of computer processors and smarter use of hardware, sampling the important states of the biomolecules still remains a challenge. Broadly speaking, the two main problems my field grapples with are 1) improving the accuracy of the modelled atomistic forces, and 2) improving the thoroughness of the sampling. Much of my research has been on the latter, with particular focus on enhancing the sampling of water in buried protein cavities (for example, see this paper).
I am also interested in reducing the barriers to using atomistic simulations. They require expertise to set up, run, and analyse. One large barrier is the complexity of the software we use, a problem that is compounded in academic research due to the frequent augmentations and modifications that are made to the software as the science progresses. This in turn raises issues around the sustainability of the software itself. I strive to make all my software easy to use, free, open-source, and written in an interpretable way. I am currently a developer for ProtoMS and work on tools for OpenMM. Another barrier to using atomistic simulations is knowing how make reliable inferences from the largeand complex data that is produced. It is important to ask the right questions from the data, and have the best tools to address them. To this end, I'm very interested in statistics (with a Bayesian flavour) and machine learning.