Molecular thermal motions play an essential role in many functionally important biochemical processes such as allosteric transitions, ion permeation through channels, or folding of proteins and nucleic acids. Knowledge of functionally important molecular motions has important implications in medicine and nanotechnology, in addition to providing insights into understanding life.
To achieve this goal Kirmizialtin’s lab is using Molecular Dynamics (MD) Simulations. MD is one of the most powerful theoretical approaches to study the structure, molecular interactions and resulted dynamics in a unified framework. However, the timescale of the biological processes are orders of magnitude longer than the timescales accessible to straightforward MD Simulations. To overcome the time scale gap between simulations and experiments, Kirmizialtin’s lab focuses on developing novel computational methods to extend the timescales of MD simulations.
Another direction in Kirmizialtin lab is to integrate computer simulations and experiments to study the complex biomolecular machinery. Using the restraints coming from experiments such as Cryo-EM, SAXS, and NMR Kirmizialtin lab is developing computational tools and methods using path sampling, metadynamics, and machine learning to help to sample conformations more effectively and guide MD simulations toward the direction where it meets with the experimental signal, providing more accurate description of physical processes and allowing atomic level visualization of the experimental data..