Designing Light Controlled Protein-protein Interactions using Photoactive Yellow Protein and Yeast-two Hybrid Screening
Bibliographic record
Abstract
Molecular tools that control biological systems provide new avenues to explore and answer fundamental biological questions, leading to new biomedical and industrial applications. The pursuit of greater spatial-temporal control has given rise to the field of optogenetics, which uses light to manipulate biological systems. While a broad suite of optogenetic tools has been developed, many of them require further optimization. There is a need to develop high-throughput screening platforms to facilitate this optimization. Furthermore, there remain many valuable biological targets yet to be adapted to optogenetic control. To address these needs, this thesis explores the use of yeast two hybrid (Y2H) technology as a high-throughput screening platform for both improving and developing new optogenetic tools. I also describe, two new optogenetic tools that were developed as photoswitchable affinity reagents.We began by developing a Y2H based selection and screening system to screen for photoswitchable protein-protein interactions (Chapter 2). Using Y2H we found that we were able to improve an existing optogenetic tool that undergoes blue light-mediated dissociation. Furthermore, we found that Y2H results were generally reflective of protein behaviour both in vitro and in mammalian cells. We then used Y2H to develop a genetically encoded photoswitchable affinity reagent by evolving the photoswitchable protein photoactive yellow protein (PYP) to bind the fluorescent protein mVenus (Chapter 3). This may serve as a general method for creating optogenetic tools as PYP could be evolved to bind other target proteins in a light dependent manner. Finally, in addition to making PYP based affinity reagents, I present an alternative strategy to photoswitchable affinity reagent design, utilizing the phenomena of mutually exclusive folding (Chapter 4). To this end PYP was inserted into a 3-helix bundle affinity reagent known as an affibody to create a chimera designated Z-PYP, which was found to make light-controlled protein-protein interactions. This alternative design strategy is potentially more efficient, as existing affinity reagents tools can be made photoswitchable via the insertion of PYP. This work pioneers new engineering methods for optogenetic tool development and creates several novel optogenetic tools to control and manipulate biology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".