Enhanced sampling methods for kinetics of biomolecules and application to triazine polymers
- Surl-Hee (Shirley) Ahn.
- [Stanford, California] : [Stanford University], 2018.
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- Physical description
- 1 online resource.
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|3781 2018 A||In-library use|
- Molecular dynamics (MD) simulations are becoming essential tools for many different fields, including biology, chemistry, and materials science, that provide us with a molecular picture of what is really happening at the molecular level for many biophysical phenomena. With MD simulations, we can see how the molecule forms and moves and obtain insight into its mechanisms with higher resolution than experiments. Unfortunately, MD simulations are not without limitations. They are restricted in predictive power because the molecules routinely get "stuck" in metastable states and do not change their conformations for an extended period. Hence, there is currently a huge gap between what MD simulations can model and the timescales of biological processes. Consequently, many methods have been developed for MD simulations over the past few decades to overcome this timescale barrier between MD simulations and biological processes. These are referred to as enhanced sampling methods. We need these methods to overcome the timescale barrier so that critical biophysical phenomena can be observed in a computationally tractable period. Current enhanced sampling methods have demonstrated that they can efficiently obtain thermodynamic and/or kinetic properties. However, there is still a need for an enhanced sampling method that requires little a priori knowledge about the system, is less heuristic, can obtain both thermodynamic and kinetic properties, and can be easily parallelized over the available computational resources for computational efficiency. I will go over several classes of enhanced sampling methods before diving into my new enhanced sampling methods that aim to address the issues mentioned above.
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- Submitted to the Department of Chemistry.
- Thesis Ph.D. Stanford University 2018.
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