Equivariant machine learning for macromolecules
Abstract/Contents
- Abstract
- Predicting the structure of macromolecules and macromolecular complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational methods for structure prediction generally leverage pre-defined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether a method that has full access to the three-dimensional (3D) coordinates of each atom can yield improved results. I present a machine learning method that learns to identify accurate models of protein complexes directly from 3D atomic coordinates, leveraging underlying physical symmetries in lieu of hand-crafted features. The neural network architecture combines multiple ingredients that together enable end-to-end learning from molecular structures containing tens of thousands of atoms: a point-based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. In addition to protein complexes, I present results on RNA structure prediction.
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 | Eismann, Stephan Johannes |
|---|---|
| Degree supervisor | Dror, Ron, 1975- |
| Degree supervisor | Schnitzer, Mark Jacob, 1970- |
| Thesis advisor | Dror, Ron, 1975- |
| Thesis advisor | Schnitzer, Mark Jacob, 1970- |
| Thesis advisor | Brünger, Axel T |
| Degree committee member | Brünger, Axel T |
| Associated with | Stanford University, Department of Applied Physics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Stephan Johannes Eismann. |
|---|---|
| Note | Submitted to the Department of Applied Physics. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/fn736tf4955 |
Access conditions
- Copyright
- © 2021 by Stephan Johannes Eismann
Also listed in
Loading usage metrics...