Computer-aided high-throughput screening for the discovery of GPCR biased ligands
Abstract/Contents
- Abstract
- G protein-coupled receptors (GPCRs) constitute the largest family of membrane receptors in the human genome and play important roles in both physiology and disease. One important property of GPCRs is biased signaling, whereby ligand engagement selectively activates a subset of signaling pathways. There is great interest in the therapeutic development of biased ligands for GPCRs given that they have the potential to selectively target downstream signaling pathways of interest and treat diseases more safe and effective manner. However, the complexity of the structural and signaling properties of GPCRs has presented many challenges in developing biased ligands. Here, we introduce a novel and generalizable high-throughput screening approach for discovering biased ligands for GPCRs. Biased ligands can stabilize distinct receptor conformations, which directly affect receptor interaction with intracellular signaling proteins, resulting in different downstream signaling outcomes. Our method integrates high-throughput screening and statistical modeling to examine large mutant protein libraries and infer the ligand-receptor binding conformation based on mutation statistical patterns. The inferred binding conformations allow for efficient identification of candidate biased ligands based on their different binding interactions with the receptor. We applied our computer-aided high-throughput screening approach to search for biased ligands targeting the Complement 5a Receptor, an important GPCR involved in the immune response and cancer. Importantly, we identified a variety of biased mutants with distinct signaling properties. In the future, we aim to apply our platform to other GPCRs and promote the study and therapeutic application of GPCR biased signaling.
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 | Xu, Yu |
|---|---|
| Degree supervisor | Giaccia, Amato J |
| Thesis advisor | Giaccia, Amato J |
| Thesis advisor | Cochran, Jennifer R |
| Thesis advisor | Graves, Edward (Edward Elliot), 1974- |
| Degree committee member | Cochran, Jennifer R |
| Degree committee member | Graves, Edward (Edward Elliot), 1974- |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Bioengineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Yu Xu. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/dq515jv6759 |
Access conditions
- Copyright
- © 2023 by Yu Xu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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