Erik Thiede

Assistant Professor


Proteins are fundamental to the molecular machinery of life: they are nature's freight trains, construction cranes, and chemical factories.  Understanding how they move to accomplish their roles would revolutionize our technology on an atomic level and lead to new medical breakthroughs.  But because proteins are so small, understanding precisely how they accomplish all these amazing things is very difficult.

In our research, we develop and apply new computational tools for understanding protein motion and function by combining machine learning with molecular simulation.  Molecular simulations allow us to visualize protein motion on an atom-by-atom level.  Machine learning techniques can then improve and accelerate these simulations by integrating them with experimental data.  Particular areas of interest are the development of new techniques for combining simulation with experimental techniques, such as cryogenic-sample electron microscopy, and the development of new algorithms for finding allosteric drug candidates.