Inferring RNA structure and stability via high-throughput experiment
Abstract/Contents
- Abstract
- The computer-aided study and design of RNA molecules is increasingly prevalent across a range of disciplines, yet advancing RNA design will require quantitative improvements in predicting RNA structure. The first part of this dissertation describes contributions to advancing RNA thermodynamics predictions, a highlight of which is the development of a novel multitask-learning-based model that links the training of an RNA thermodynamic model to the statistical mechanics of several prediction tasks. We trained this framework using large datasets of diverse synthetic constructs obtained from the crowdsourced RNA design project, Eterna. The resulting algorithm, EternaFold, demonstrated improved performance on diverse independent datasets, including complete viral genomes probed in virion, human mRNAs probed in vivo, and synthetic designs modeling mRNA vaccines. This work establishes an extensive benchmark for evaluating RNA secondary structure ensembles through several types of experiment, and a general statistical mechanical framework for inferring energetic parameters from equilibrium experimental observables. The second part of this dissertation describes advances in a pressing application of RNA design: creating more thermostable RNA vaccines. Vaccines based on messenger RNA (mRNA) emerged as forerunners in the current COVID-19 pandemic and show promise as a novel therapeutic platform, yet their inherent chemical instability sets a fundamental limit on the stability of mRNA vaccines. Predictions from our developed biophysical models indicated that the half-life of any mRNA could be immediately increased at least two-fold through sequence design, predictions validated in vitro and in vivo. We anticipate this work will guide future therapeutic and vaccine development in potency and stability.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Wayment-Steele, Hannah Katherine |
|---|---|
| Degree supervisor | Das, Rhiju |
| Thesis advisor | Das, Rhiju |
| Thesis advisor | Herschlag, Daniel |
| Thesis advisor | Markland, Thomas E |
| Degree committee member | Herschlag, Daniel |
| Degree committee member | Markland, Thomas E |
| Associated with | Stanford University, Department of Chemistry |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Hannah Katherine Wayment-Steele. |
|---|---|
| Note | Submitted to the Department of Chemistry. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/kz670mm5198 |
Access conditions
- Copyright
- © 2021 by Hannah Katherine Wayment-Steele
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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