Computational tools for structure-guided drug discovery
Abstract/Contents
- Abstract
- Most drugs function by binding to proteins and modulating their function. Computational methods that model the atomic interactions between drug molecules and proteins promise to greatly accelerate drug discovery. This work discusses the application and development of several computational methods, involving molecular simulations and machine learning, for understanding and designing novel therapeutics. First, we apply atomic-level simulations to investigate mechanisms that contribute to drug selectivity and avoid undesired side-effects. We reveal the structural basis for the selectivity of a long-studied Schizophrenia drug between structurally similar muscarinic acetylcholine receptors. We validate this mechanism experimentally through mutagenesis and use it to design molecules with altered selectivity profiles. Second, we develop a series of methods that use machine learning to assist in structure-guided drug design. Using the three-dimensional structure of a target protein, these methods aim to generate novel and diverse drug-like molecules that bind to the target. We compare several approaches, including a fragment-based approach specifically designed to incorporate chemical intuition. Third, we investigate new molecular mechanisms by which drug molecules can modulate the signaling of G-protein coupled receptors. Using atomic-level simulations, we found that certain ligands can trigger activation of free fatty acid receptor 1 by directly rearranging an intracellular loop that interacts with G-proteins. We further supported this non-canonical mechanism through targeted mutagenesis; specific mutations which disrupt interactions with the intracellular loop convert these agonists into inverse agonists in in vitro experiments. This work highlights how computational tools, combined with complementary experimental methods, can accelerating drug discovery by elucidating molecular mechanisms as well as directly facilitating structure-guided drug design.
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 | 2024; ©2024 |
| Publication date | 2024; 2024 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Powers, Alexander (Alexander S.) |
|---|---|
| Degree supervisor | Dror, Ron, 1975- |
| Thesis advisor | Dror, Ron, 1975- |
| Thesis advisor | Boxer, Steven G. (Steven George), 1947- |
| Thesis advisor | Cui, Bianxiao |
| Degree committee member | Boxer, Steven G. (Steven George), 1947- |
| Degree committee member | Cui, Bianxiao |
| Associated with | Stanford University, School of Humanities and Sciences |
| Associated with | Stanford University, Department of Chemistry |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Alexander Powers. |
|---|---|
| Note | Submitted to the Department of Chemistry. |
| Thesis | Thesis Ph.D. Stanford University 2024. |
| Location | https://purl.stanford.edu/bn615xh0212 |
Access conditions
- Copyright
- © 2024 by Alex Powers
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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