Who: Dr Antonia Mey (University of Edinburgh)
When: Wednesday 19/04/2023 at 4:00-5:30PM (UK time).
Title: How well can we model protein dynamics using Markov jump processes?
Proteins regulate and manage all processes that makeup life as we know it. Understanding their structure and how their dynamics relate to their function allows us to unravel intricate and complex biological processes. A better understanding of these processes can then be used to regulate them, and counteract e.g. disease. Molecular simulations can shed light on how proteins work, but rigorous statistical methods are needed to extract quantitative information from these simulations. Can this be automated and optimised such that we can obtain quantitative data that can be compared to experiments? Markov state models are a way of analysing simulation trajectory data using a statistical framework.
In this talk, I will introduce why approximating protein dynamics with a Markov model is a valid approach and then will explore the good, bad and, ugly when it comes to trying to automate the building of Markov Models. For this illustrated journey into the world of Markov state modelling we will look at Cyclophilin A and its mutant's role in proline catalysis, as well as how small peptides/toy proteins such as BBA and chignolin fold.
When: Wednesday 19/04/2023 at 4:00-5:30PM (UK time).
Title: How well can we model protein dynamics using Markov jump processes?
Proteins regulate and manage all processes that makeup life as we know it. Understanding their structure and how their dynamics relate to their function allows us to unravel intricate and complex biological processes. A better understanding of these processes can then be used to regulate them, and counteract e.g. disease. Molecular simulations can shed light on how proteins work, but rigorous statistical methods are needed to extract quantitative information from these simulations. Can this be automated and optimised such that we can obtain quantitative data that can be compared to experiments? Markov state models are a way of analysing simulation trajectory data using a statistical framework.
In this talk, I will introduce why approximating protein dynamics with a Markov model is a valid approach and then will explore the good, bad and, ugly when it comes to trying to automate the building of Markov Models. For this illustrated journey into the world of Markov state modelling we will look at Cyclophilin A and its mutant's role in proline catalysis, as well as how small peptides/toy proteins such as BBA and chignolin fold.
