Harnessing Molecular Simulations and Statistical Inference to Model Folding and Binding

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Presentation Abstract:

As computers have become more efficient, simulation approaches for understanding the conformational dynamics of biomolecules have increasingly utilized large ensembles of trajectories to statistically infer pathways, rates and mechanisms.  In this talk I will discuss a variety of methods that utilize Markov Model approaches to describe conformational dynamics as a network of discrete state transitions.  Maximum-caliber and Bayesian inference methods can be used to efficiently perturb these models for numerous applications, for example, to reconcile simulations with experimental observables and their uncertainties.  We are applying these methods to better understand the relationship between ligand preorganization (folding) and binding, with the ultimate goal of designing these properties in silico.

Host: 
Banu Ozkan
Affiliation: 
Temple University Department of Chemistry
Speaker: 
Vince Voelz
Date: 
Wednesday, March 10, 2021
Year: 
2021
Semester: 
Spring