Protein structure and dynamics from a statistical perspective

Proteins regulate and manage all processes that make up life as we know it. Understanding their structure and how their dynamics relates to their function allows us to unravel intricate and complex biological process. A better understanding of these processes can then be used to regulate them, and fight e.g. disease. Molecular simulations can shed light into how proteins work, but rigorous statistical methods are needed to extract meaningful information from these simulations. To obtain quantitative data that can be compared to experimental data we need a rigorous mathematical framework. Using Markov state models are a way of analysing simulation trajectory data and in combination with Bayesian inference  methods we can build optimal models from our simulations that are both predictive, but also give atomistic details otherwise obscured in experiments. In this talk I will discuss the mathematical background of Markov state models and how to make optimal choices when constructing them from simulation data. I will also highlight how the results of these models can be used to shed light on experimental results of clinically relevant proteins such as Cyclophilin A. 

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