Yeast Two-Hybrid Screening of Photoswitchable Protein–Protein Interaction Libraries

Woloschuk, Ryan M; Reed, P Maximilian M; McDonald, Sherin; Uppalapati, Maruti; Woolley, G. Andrew

doi:10.1016/j.jmb.2020.03.011

Cited by 16 publications

(15 citation statements)

References 38 publications

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“…INT+: Positive interaction; INT−, negative interaction; ddFP, dimerization‐dependent fluorescence protein c) Efficient resources for conducting an experiment or collecting data. References of each strategy are listed as follows: machine learning directed evolution, [ 119,120 ] interface directed mutagenesis, [ 122 ] computational prediction, [ 31,123,124 ] native mass spectrometry, [ 128 ] SEC‐HPLC, [ 129 ] yeast screening, [ 122,130 ] signaling biosensor, [ 131 ] optoplate, [ 132 ] LAVA plate, [ 133 ] optobase, [ 1a ] and the optogenetic resource center. [ 1 ] The image of the optoplate is reproduced with permission.…”

Section: Generation and Validation Of Optogenetic Actuatorsmentioning

confidence: 99%

“…For example, by varying the protein interface between circularly permuted photoactive yellow protein (cPYP) and binder of PYP dark state (BoPD), distinct photoswitchable variants with altered affinity, kinetics, and apo‐state binding could be recovered. [ 122 ] Several computational prediction programs were developed to predict the photophysical properties of engineered protein variants. For instance, Rosetta uses energy and distance constraints to predict favorable linker composition and fusion protein orientation in silico.…”

Section: Generation and Validation Of Optogenetic Actuatorsmentioning

confidence: 99%

“…Live‐cell readout, such as yeast cell growth, could be repurposed to validate interactions between photoactivatable proteins, particularly dimerizers. [ 122,130 ] An alternative way is to use biosensors that respond to the activation of photoactivatable proteins to confirm the system's functionality. For instance, the dimer dependent red fluorescence protein (ddFP) was engineered only to emit fluorescence when the synthetic G protein receptor is appropriately activated by light.…”

Section: Generation and Validation Of Optogenetic Actuatorsmentioning

confidence: 99%

See 2 more Smart Citations

Steering Molecular Activity with Optogenetics: Recent Advances and Perspectives

Fan

Skeeters

et al. 2021

Advanced Biology

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In a transparent medium, lensbased optical microscopy could focus a coherent light beam (e.g., laser) into a tiny spot, whose dimension is comparable to the size of the wavelength of the light. The diameter of the smallest beam waist is about half the size of the wavelength, which is referred to as the diffraction limit. Thus, for visible light, the theoretical diffraction limit is between 200 and 400 nm, much smaller than the size of a single cell (Figure 1A). However, in biological tissues that significantly scatter and absorb visible light, the spatial resolution could be compromised. In multicellular organisms, light absorption limits the penetration depth. The scattering of the light by the opaque biological tissues would expand the volume of light stimulation and reduce the spatial resolution.

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Section: Generation and Validation Of Optogenetic Actuatorsmentioning

confidence: 99%

Section: Generation and Validation Of Optogenetic Actuatorsmentioning

confidence: 99%

Section: Generation and Validation Of Optogenetic Actuatorsmentioning

confidence: 99%

See 1 more Smart Citation

Steering Molecular Activity with Optogenetics: Recent Advances and Perspectives

Fan

Skeeters

et al. 2021

Advanced Biology

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“…Blue (Zhao et al, 2019) CRY2/CIB1 Blue (Sweeney, Moreno Morales, Burmeister, Nimunkar, & McClean, 2019) AtLOV2 Blue (Scheffer, Hasenjäger, & Taxis, 2019) AsLOV2 (LINuS plus CRISPR-dCas9) Blue (Geller, Antwi, di Ventura, & McClean, 2019) CRY2/CIB1 (yOTK) Blue (An-Adirekkun et al, 2020) CRY2/CIB1 Blue (Rivera-Tarazona, Bhat, Kim, Campbell, & Ware, 2020) AuLOV Blue (Hepp et al, 2020) EL222 Blue (Perkins, Benzinger, Arcak, & Khammash, 2020) PYP Blue (Woloschuk, Reed, McDonald, Uppalapati, & Woolley, 2020) AsLOV2 (LINX) Blue (Meriesh, Lerner, Chandrasekharan, & Strahl, 2020) CrPHOT Blue (Suzuki et al, 2020) AsLOV2 (CLASP) Blue (Chen et al, 2020) AsLOV2 (LOV2GIVe) Blue (Garcia-Marcos et al, 2020)…”

Section: Blue-light Optogenetic Systemsmentioning

confidence: 99%

The rise and shine of yeast optogenetics

Figueroa

Rojas

Romero

et al. 2020

Yeast

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Optogenetics refers to the control of biological processes with light. The activation of cellular phenomena by defined wavelengths has several advantages compared with traditional chemically inducible systems, such as spatiotemporal resolution, dose-response regulation, low cost, and moderate toxic effects. Optogenetics has been successfully implemented in yeast, a remarkable biological platform that is not only a model organism for cellular and molecular biology studies, but also a microorganism with diverse biotechnological applications. In this review, we summarize the main optogenetic systems implemented in the budding yeast Saccharomyces cerevisiae, which allow orthogonal control (by light) of gene expression, protein subcellular localization, reconstitution of protein activity, and protein sequestration by oligomerization. Furthermore, we review the application of optogenetic systems in the control of metabolic pathways, heterologous protein production and flocculation. We then revise an example of a previously described yeast optogenetic switch, named FUN-LOV, which allows precise and strong activation of the target gene. Finally, we describe optogenetic systems that have not yet been implemented in yeast, which could therefore be used to expand the panel of available tools in this biological chassis. In conclusion, a wide repertoire of optogenetic systems can be used to address fundamental biological questions and broaden the biotechnological toolkit in yeast. Take Away The budding yeast Saccharomyces cerevisiae has become a powerful biological platform for optogenetics, allowing the use of light to control diverse phenotypes. In this review, we summarized the main optogenetic tools implemented in yeast and their applications in metabolic engineering and biotechnology. Finally, we propose different sources of building blocks to expand the current repertoire of optogenetic systems in yeast.

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“…To reveal the mechanisms underlying physiological and pathological processes, high-throughput methods for studying PPIs are urgently needed. To date, some high-throughput experimental methods for detecting PPIs have been reported, such as yeast twohybrid, tandem affinity purification, phage display, and protein chip methods (Gavin et al, 2002;Rao et al, 2014;Sundell and Ivarsson, 2014;Mehla et al, 2015;Huang et al, 2017;Viala and Bouveret, 2017;Woloschuk et al, 2020). However, these methods also have many drawbacks, including complexity, required time, and high cost.…”

Section: Introductionmentioning

confidence: 99%

Delayed Comparison and Apriori Algorithm (DCAA): A Tool for Discovering Protein–Protein Interactions From Time-Series Phosphoproteomic Data

Ding

Xie

Zhang

et al. 2020

Front. Mol. Biosci.

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Analysis of high-throughput omics data is one of the most important approaches for obtaining information regarding interactions between proteins/genes. Time-series omics data are a series of omics data points indexed in time order and normally contain more abundant information about the interactions between biological macromolecules than static omics data. In addition, phosphorylation is a key posttranslational modification (PTM) that is indicative of possible protein function changes in cellular processes. Analysis of time-series phosphoproteomic data should provide more meaningful information about protein interactions. However, although many algorithms, databases, and websites have been developed to analyze omics data, the tools dedicated to discovering molecular interactions from time-series omics data, especially from time-series phosphoproteomic data, are still scarce. Moreover, most reported tools ignore the lag between functional alterations and the corresponding changes in protein synthesis/PTM and are highly dependent on previous knowledge, resulting in high false-positive rates and difficulties in finding newly discovered protein–protein interactions (PPIs). Therefore, in the present study, we developed a new method to discover protein–protein interactions with the delayed comparison and Apriori algorithm (DCAA) to address the aforementioned problems. DCAA is based on the idea that there is a lag between functional alterations and the corresponding changes in protein synthesis/PTM. The Apriori algorithm was used to mine association rules from the relationships between items in a dataset and find PPIs based on time-series phosphoproteomic data. The advantage of DCAA is that it does not rely on previous knowledge and the PPI database. The analysis of actual time-series phosphoproteomic data showed that more than 68% of the protein interactions/regulatory relationships predicted by DCAA were accurate. As an analytical tool for PPIs that does not rely on a priori knowledge, DCAA should be useful to predict PPIs from time-series omics data, and this approach is not limited to phosphoproteomic data.

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Yeast Two-Hybrid Screening of Photoswitchable Protein–Protein Interaction Libraries

Cited by 16 publications

References 38 publications

Steering Molecular Activity with Optogenetics: Recent Advances and Perspectives

Steering Molecular Activity with Optogenetics: Recent Advances and Perspectives

The rise and shine of yeast optogenetics

Delayed Comparison and Apriori Algorithm (DCAA): A Tool for Discovering Protein–Protein Interactions From Time-Series Phosphoproteomic Data

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