Learning to design protein structure and sequence
Abstract/Contents
- Abstract
- Over the last ten years the field of de novo protein design has been revolutionized by new computational methods. Here, I discuss work which illustrates applications of the "traditional" methods of de novo protein design, sampling with MCMC using semi-physical energy functions and heavy manual intervention to design a novel TIM barrel and a stabler and more functional IL-2 cytokine. I will also discuss my work on developing "new" methods based on deep learning which are much more efficient and accurate than traditional methods, and abstract away the need for expert curation. This includes some of the first works to generate NTF2 structures conditioned on secondary and tertiary structure labels, and to co-design protein structures and sequences for all-atom protein modeling. Finally, I briefly conclude and suggest possible future directions for the field to continue improving how we are able to design novel proteins.
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 | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Chu, Alexander E |
|---|---|
| Degree supervisor | Huang, Possu |
| Thesis advisor | Huang, Possu |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Dror, Ron |
| Thesis advisor | Fordyce, Polly |
| Degree committee member | Altman, Russ |
| Degree committee member | Dror, Ron |
| Degree committee member | Fordyce, Polly |
| Associated with | Stanford University, School of Humanities and Sciences |
| Associated with | Stanford University, Biophysics Program |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Alexander E. Chu. |
|---|---|
| Note | Submitted to the Biophysics Program. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/md544qc5840 |
Access conditions
- Copyright
- © 2023 by Alexander E Chu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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