Molecular Simulation to Explore Targets in Context
As the field of computer-aided drug discovery matures, it has become evident that viewing a molecular target independent of its biological context is often an oversimplification. To this end, our lab has developed an all-atom model of an influenza virus particle. In this dissertation, I present analysis of molecular dynamics simulations of this virus particle by Markov state modeling. I begin with the analysis of influenza neuraminidase, which includes construction of a Markov state model to characterize the catalytic site dynamics. This Markov state model provides a quantitative framework for comparison to dynamics of isolated influenza neuraminidase simulations to validate the virus particle model. Next, I identify the probable group 1 influenza hemagglutinin binding site for the antiviral Arbidol. Here, a Markov state model is constructed from the virus particle simulations to characterize the dynamics of the proposed binding site. Finally, I conclude with a recent review outlining multiscale simulation approaches to drug-protein binding, for which Markov state modeling is included.