We combine computational and experimental methods to understand the structure, interactions and evolution of proteins. ​ A fundamental goal is to encode our understanding of proteins into computational and experimental methods that enables the design self-assembling protein systems. We also want to understand the process of self-assembly by inferring self-assembly pathways from simulated protein structures and time-resolved experimental data.

We develop computational methods to design self-assembling proteins and conformational switches. Methods are based on structure-based approaches guided by energy functions and machine learning approaches.

We develop methods to predict the structure of protein complexes, in particular large symmetrical asssemblies.

We develop experimental methods to characterize protein variants at large scale, characterizing the effect of synonymous and non-synonymous substitutions on stability and expression.

We develop methods to simulate protein evolution using protein structure.

We study the kinetic mechanism behind protein self-assembly with experimental methods and simulations.