Research Interests
Our research focuses on the use and development of theoretical chemistry and biophysical techniques to further our understanding of biomolecular processes and aid in rational drug design. Specific research areas include:

Simulations of Biomolecular Complexes:
Biological macromolecules have evolved such that their structure, dynamics, and energetics allow them to perform a diverse range of physical and chemical reactions in vivo. Numerous experimental techniques, such as X-Ray crystallography, NMR, FRET, and IR spectroscopy (to name only a few) have been developed to probe the architecture and motions of proteins on the single molecule level, however each of these have distinct limitations. We utilize computational tools, such as molecular dynamics (MD) simulations, which propagate Newtonian dynamics on an atomic resolution model of a system, to bridge the gaps in our understanding of the structure and dynamics of biomolecules. Current computational power allows for simulation in the nanosecond to microsecond timescale, revealing motions difficult to observe in experiments and making them particularly well suited as a theoretical tool in the study of biomolecules.

Rational Drug Design:
To combat modern diseases such as influenza and cancer requires the development of novel small-molecule inhibitors targeting enzymes vital to their proliferation. We are interested in the development and application of computational techniques, such as docking, molecular dynamics simulations, and free energy calculations, to predict the binding conformations and energies of small molecules to target proteins. In collaboration with experimental biologists and medicinal chemists, novel protein inhibitors may be designed which can may be used for in vivo experiments and may potentially lead to new drugs.

Development of Enhanced Free Energy and Enhanced Sampling Methods:
The free energy of molecular processes, such as the conformational change of a protein as it undergoes a transition, the binding of a small molecule into a protein’s active site, and enzyme catalysis, may be calculated via several methods such as umbrella sampling, metadynamics, and alchemical free energy methods. Although these algorithms are theoretically capable of calculating accurate free energies, in practice they suffer from several technical difficulties, such as the requirement for extensive phase space sampling, which lead to decreased reliability and high computational cost. We are working on overcoming some of these challenges in an attempt to improve current free energy methods. For example, the combination of enhanced sampling techniques such as Accelerated Molecular Dynamics with free energy perturbation has been shown to reduce the computational cost of accurate ligand binding calculations, which could result in an increased use of high level free energy calculations in the drug design processes.