Abstract

Continuous atomistic descriptions of enzyme mechanisms and atomistic dynamics are crucial to the characterization of protein function and dysfunction—a common cause of disease—but unattainable with current techniques, necessitating novel methods which leverage computational-experimental synergy. Computational studies can provide a wealth of information, but carry little weight without experimental validation. Thus, coupling time-resolved crystallographic studies of enzyme mechanisms with computational mechanistic investigation through molecular dynamics (MD) simulation and predictive model generation provides the opportunity to combine approaches. We demonstrate and refine a novel Markov State-informed Multilinear Singular Value Decomposition (MSiMSVD) method with the Pseudomonas mevalonii HMG CoA Reductase (PmHMGR) pathway as a model system. MSiMSVD couples time-resolved crystallographic studies of enzyme mechanisms with computational mechanistic investigation through MD simulation and predictive model generation. Application of the MSiMSVD method to slow dynamical events-such as the PmHMGR 2nd hydride transfer-is limited by the ability of force fields to reproduce atomistic behavior at a transition state. This need can be met via the generation of a Transition State Force Field (TSFF) with a program such as Quantum-guided Molecular Mechanics (Q2MM). However, a frequently encountered problem in the optimization of TSFFs for biomolecules is the high dimensionality of the optimization surface producing local rather than global minima. To address this, we implement a constrained particle swarm algorithm hybridized with a basic genetic algorithm to automate the optimization of TSFFs with Q2MM. The Q2MM and the MSiMSVD methods will become accessible to non-experts for a wide range of systems, such as large biomolecules. Thus far, initial Markov State Models generated from MD simulation of the PmHMGR 2nd hydride transfer indicate that more sampling is necessary to cover the transition state conformation, i.e. the structure of the activated complex.

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