Inferring protein structure and dynamics from simulation and experiment
Abstract/Contents
- Abstract
- An atomic-scale understanding of biological molecules remains a grand challenge for the physical and biological sciences. Here, I describe how molecular dynamics simulations can be used to directly connect to biophysical experiments. I first describe the use of Markov state models to connect simulated and measured protein kinetics, allowing studies of protein folding at the atomic scale. I then introduce the use of NMR measurements, such as chemical shifts and scalar couplings, for the evaluation of molecular dynamics force field quality. Finally, I propose a new statistical technique that can be used to combine both simulation and experiment into accurate models of conformational ensembles. Such models are shown to be free of force field bias and can be used to investigate the structural and equilibrium properties of biomolecules. In sum, the present work demonstrates how statistically-sound methods of inference can forge a direct connection between simulation and experiment.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Beauchamp, Kyle A |
|---|---|
| Associated with | Stanford University, Department of Biophysics. |
| Primary advisor | Das, Rhiju |
| Primary advisor | Pande, Vijay |
| Thesis advisor | Das, Rhiju |
| Thesis advisor | Pande, Vijay |
| Thesis advisor | Harbury, Pehr |
| Thesis advisor | Martinez, Todd J. (Todd Joseph), 1968- |
| Advisor | Harbury, Pehr |
| Advisor | Martinez, Todd J. (Todd Joseph), 1968- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Kyle A. Beauchamp. |
|---|---|
| Note | Submitted to the Department of Biophysics. |
| Thesis | Ph.D. Stanford University 2013 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Kyle Beauchamp
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