Proteins are the molecular machines that make cells work. They perform a wide variety of functions through interactions with each other and many additional molecules. Traditionally, proteins are described in a single static state (a picture). It is now increasingly recognised that many proteins can adopt multiple states and move between these conformational states dynamically (a movie).
We investigate how the dynamics, conformational states and available experimental data of proteins relates to their amino acid sequence. Underlying physical and chemical principles are computationally unravelled through data integration, analysis and machine learning, so connecting them to biological events and improving our understanding of the way proteins work.
