RUI: A variational enhanced sampling approach to enzyme kinetics and protein dynamics in condensed phases

With this award, the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry is supporting Dr. James McCarty of Western Washington University to develop molecular simulation algorithms for computing dynamical properties of biomolecules in solution. Computer simulations of biological molecules can spur the design and optimization of engineered proteins and biomimetic nanomaterials.

However, obtaining dynamical information at a reasonable computational cost is limited by the time required to overcome kinetic bottlenecks that characterize rare events in biological systems. Dr. McCarty and his research group will leverage a variational formalism, based on the statistical mechanics of rare events, to compute long-time dynamical observables for proteins in solution and biochemical reactions.

A goal of this work is to provide a computational framework to bridge the gap between computer simulations and experiments that probe long-time dynamics. The methods developed by Dr. McCarty’s group will be made accessible to experimental and computational research groups and will provide new tools for experimentalists to interpret dynamical measurements.

Dr. McCarty’s research will offer increased access and opportunities for undergraduates to engage in computational biochemistry research. An outcome of the planned activities is expected to be the integration of computational advances into the undergraduate biochemistry lab, pairing computer simulation with experimental mutational screening data.

Enhanced sampling methods in molecular simulations use an external bias potential to more efficiently sample state space. Dr. McCarty will make use of a variational approach to enhanced sampling in which an external bias potential accelerates rare-event transitions along a low-dimensional reaction coordinate.

The bias potential is constructed on-the-fly by optimizing a set of variational parameters in an iterative procedure. Through algorithmic advances, benchmarking, and applications of this variational formalism, Dr. McCarty’s work will extend the scope of the variational approach into two key areas important for understanding macromolecular dynamics and enzyme reaction rates. (i) Dr.

McCarty will be developing methods to predict the mean-first-passage times of enzymatic reactions from ab initio molecular dynamics (MD) simulations. (ii) Dr. McCarty will be developing an approach to extract time correlation functions from time-series data obtained from biased classical (MD) trajectories that is expected to efficiently compute NMR relaxation rates.

In both of these areas, dynamic observables are computed by rescaling the biased simulation time according to the statistical mechanics of rare events. The orders-of-magnitude improvement in efficiency that will likely be afforded by Dr. McCarty’s work has the potential to accelerate the capabilities of quantum mechanics/molecular mechanics (QM/MM) hybrid simulations for drug development and protein engineering, and to ultimately provide a new tool for researchers to interpret dynamical NMR measurements.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.