Markov state models for protein and RNA folding
Abstract/Contents
- Abstract
- Understanding the molecular bases of human health could greatly augment our ability to prevent and treat diseases. For example, a deeper understanding of protein folding would serve as a reference point for understanding, preventing, and reversing protein misfolding in diseases like Alzheimer's. Unfortunately, the small size and tremendous flexibility of proteins and other biomolecules make it difficult to simultaneously monitor their thermodynamics and kinetics with sufficient chemical detail. Atomistic Molecular Dynamics (MD) simulations can provide a solution to this problem in some cases; however, they are often too short to capture biologically relevant timescales with sufficient statistical accuracy. We have developed a number of methods to address these limitations. In particular, our work on Markov State Models (MSMs) now makes it possible to map out the conformational space of biomolecules by combining many short simulations into a single statistical model. Here we describe our use of MSMs to better understand protein and RNA folding. We chose to focus on these folding problems because of their relevance to misfolding diseases and the fact that any method capable of describing such drastic conformational changes should also be applicable to less dramatic but equally important structural rearrangements like allostery. One of the key insights from our folding simulations is that protein native states are kinetic hubs. That is, the unfolded ensemble is not one rapidly mixing set of conformations. Instead, there are many non-native states that can each interconvert more rapidly with the native state than with one another. In addition to these general observations, we also demonstrate how MSMs can be used to make predictions about the structural and kinetic properties of specific systems. Finally, we explain how MSMs and other enhanced sampling algorithms can be used to drive efficient sampling.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2010 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Bowman, Gregory Ross | |
|---|---|---|
| Associated with | Stanford University, Department of Biophysics. | |
| Primary advisor | Pande, Vijay | |
| Thesis advisor | Pande, Vijay | |
| Thesis advisor | Altman, Russ | |
| Thesis advisor | Herschlag, Daniel | |
| Advisor | Altman, Russ | |
| Advisor | Herschlag, Daniel |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Gregory Ross Bowman. |
|---|---|
| Note | Submitted to the Department of Biophysics. |
| Thesis | Ph.D. Stanford University 2010 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2010 by Gregory Ross Bowman
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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