Understanding the Role of Multiple Pathways in Enzyme Catalysis with a Hierarchical Markov State Model
Open Access
- Author:
- Persichetti, Joseph
- Graduate Program:
- Chemistry
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- May 11, 2022
- Committee Members:
- Lasse Jensen, Major Field Member
Howard Salis, Outside Unit & Field Member
Edward O'Brien, Chair & Dissertation Advisor
Squire Booker, Major Field Member
Philip Bevilacqua, Program Head/Chair - Keywords:
- molecular dynamics
markov state model
QM/MM
enzyme kinetics
alanine dipeptide
dihydrofolate reductase
computational
free-energy simulations
finite-temperature string method - Abstract:
- Markov state models (MSMs) have been used to describe ensembles of pathways connecting metastable states when sufficient overlap of unbiased sampling is attainable. In the cases where multiple ensembles are separated by large free-energy barriers, a chain-of-states method can be used to estimate the rate of transitioning between these sets, though these approaches often consider a single state representing each endpoint. We have developed a Markov state model of Markov state models which we call the Hierarchical-MSM where chain-of-states simulations are used to model the transitions between each local MSM. We have demonstrated that this approach outperforms the conventional string method where only a single pathway is considered for tractable test cases including, a toy model and the alanine dipeptide. In this method, there is no need for sufficient overlap between ensembles, which becomes less likely as the complexity of the system increases. It also provides the additional insights of multiple pathways between ensembles. We have extended this approach to investigate an enzyme, dihydrofolate reductase. We utilize classical simulations to populate kinetically distinct metastable states in the reactants ensemble and utilize 1-dimensional QM/MM umbrella sampling simulations along the reaction coordinate to obtain the free-energy barrier of the chemical reaction associated with each metastable state. With this approach, we determined that there are multiple metastable states which contribute a non-negligible amount to the observed flux, and that the experimentally-determined structures are not representative of the most reactive metastable state. The Hi-MSM is a powerful approach which utilizes the dynamic nature of enzyme catalysis to improve upon the conventional approach which considers a static reactants structure.