To understand the mechanisms of disease, we often approach a problem such as viral entry as a biomolecular network. If we can estimate the structure and dynamics of that network, we can understand and manipulate the underlying physiological process.
Molecular simulations provide a powerful tool to understand biomolecular dynamics, but obtaining systematic quantitative insight at biological length and time scales requires enormous computing power as well as advances in our ability to coordinate simulations and analyze their results to build network models. We actively perform research in all of these areas to assist our own research into infectious disease as well as others’ work. Our aim is to help free scientists from being “data jockeys” and allow them to spend less time worrying about where to run simulations and store the data, more time thinking about what models to build and how to analyze them.
As part of this effort, we help develop the Gromacs molecular simulation software and the Copernicus software for parallel adaptive simulations. We have recently developed gmxapi, the Python API for Gromacs.
We were also very excited to work with Google on their Exacycle project. Google donated up to a billion core-hours on their massively parallel architecture to seven scientific projects including our own. Using Exacycle, we were able to predict drug-resistance mutations in bacteria and understand how they function, yielding insight that will help the next generation of antibiotic development.