Advancing computational prediction of RNA structures and dynamics
Abstract/Contents
- Abstract
- RNA plays critical roles in fundamental biological processes, including transcription, translation, post-transcriptional regulation of genetic expression, and catalysis as enzymes. These critical RNA functions are determined by the structures and dynamics of the RNA molecules. Computational methods might be used to predict the structures and dynamics of RNA. Unfortunately, the prediction accuracies of current computational methods are still inferior compared to experiments. In this dissertation, I discuss recent advances I made in improving and developing computational methods to make accurate predictions on the RNA structures and dynamics. The dissertation contains three individual research projects. In the first part, I present a protocol for Enumerative Real-space Refinement ASsisted by Electron density under Rosetta (ERRASER). ERRASER combined RNA structure prediction algorithm with experimental constraints from crystallography, to correct the pervasive ambiguities in RNA crystal structures. On 24 RNA crystallographic datasets, ERRASER corrects the majority of steric clashes and anomalous backbone geometries, improves the average Rfree by 0.014, resolves functionally important structural discrepancies, and refines low-resolution structures to better match higher resolution structures. In the second part, I present HelixMC, a package for simulating kilobase-length double-stranded DNA and RNA (dsDNA and dsRNA) under external forces and torques, which is typical in single-molecule tweezers experiments. It recovered the experimental bending persistence length of dsRNA within the error of the simulations and accurately predicted that dsRNA's "spring-like" conformation would give a two-fold decrease of stretch modulus relative to dsDNA. In the third part, I developed a framework of Reweighting of Energy-function Collection with Conformational Ensemble Sampling (RECCES), to predict the folding free energies of RNA duplexes. With efficient sampling and reweighting, RECCES allows comprehensive exploration of the prediction power of Rosetta energy function, and provides a powerful platform for testing future improvement of the energy function. In all the projects above, I leveraged rich datasets from previous experiments to develop novel algorithms that gave predictions with unprecedented accuracies, which were validated by independent blind tests. These computational methods I developed could also serve as a solid foundation for future efforts of improving prediction accuracies of RNA computational algorithms.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2015 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Chou, Fang-Chieh |
|---|---|
| Associated with | Stanford University, Department of Biochemistry. |
| Primary advisor | Das, Rhiju |
| Thesis advisor | Das, Rhiju |
| Thesis advisor | Martinez, Todd J. (Todd Joseph), 1968- |
| Thesis advisor | Pande, Vijay |
| Advisor | Martinez, Todd J. (Todd Joseph), 1968- |
| Advisor | Pande, Vijay |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Fang-Chieh Chou. |
|---|---|
| Note | Submitted to the Department of Biochemistry. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2015. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2015 by Fang-Chieh Chou
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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