Towards robust dynamical models of biomolecules [electronic resource]
- Matthew P. Harrigan.
- Physical description
- 1 online resource.
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- Biology is the ultimate emergent phenomenon, and we largely lack a full picture of its function at the smallest scales. Molecular dynamics purports to model biomolecules like proteins with all-atom resolution. Among other challenges, merely analyzing the large quantities of data that result from a simulation has become a bottleneck. In this dissertation, I present my work towards building reduced-complexity models that faithfully capture the relevant functional dynamics of biomolecular simulations. In chapter 1, I introduce a mathematical language for dealing with stochastic processes and show the connection to established modeling methods like Markov modeling and tICA. Chapter 2 develops and characterizes a method for including solvent degrees of freedom in Markov state models. In chapter 3, we apply state-of-the-art MSM modeling to understand multi-scale conformational dynamics of a potassium ion channel. Chapter 4 provides an overview of a curated selection of modeling building blocks accessible through our carefully designed software package. Chapter 5 introduces a new non-linear basis which unites the MSM and tICA approaches. Finally, in chapter 6, I introduce parameterized sets of basis functions and use the variational principle directly to optimize the basis set itself. It is my hope that these novel algorithms aided by well-engineered software implementations and validated by characterization on real biomolecular systems will lead the field closer towards truly robust dynamical models of biomolecules.
- Publication date
- Submitted to the Department of Chemistry.
- Thesis (Ph.D.)--Stanford University, 2017.
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