Source 1primary paper2023
Toolkit/LOV-Turbo
LOV-Turbo
Taxonomy: Mechanism Branch / Architecture. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
LOV-Turbo is a light-regulated proximity-labeling system generated by installing a light-sensitive LOV domain into the biotin ligase TurboID. It provides rapid and reversible control of biotin-labeling activity with low-power blue light and can also be activated by luciferase-derived BRET for interaction-dependent proximity labeling in living cells.
Usefulness & Problems
Why this is useful
LOV-Turbo enables spatiotemporally precise control of proximity labeling while reducing background labeling, including in biotin-rich environments such as neurons. It is useful for pulse-chase and interaction-dependent labeling experiments in multiple cellular contexts.
Source:
We used LOV-Turbo for pulse-chase labeling to discover proteins that traffick between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
Problem solved
This tool addresses the difficulty of controlling TurboID proximity-labeling activity with high temporal precision while limiting constitutive background biotinylation. It also addresses the need to trigger labeling without external illumination by using luciferase-derived BRET.
Source:
We used LOV-Turbo for pulse-chase labeling to discover proteins that traffick between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
Taxonomy & Function
Primary hierarchy
Mechanism Branch
Architecture: A composed arrangement of multiple parts that instantiates one or more mechanisms.
Mechanisms
bret-mediated activationlight-gated allosteric switchingproximity-dependent biotin labelingTarget processes
recombinationselectionInput: Light
Implementation Constraints
LOV-Turbo is implemented by domain fusion, specifically insertion of a light-sensitive LOV domain into TurboID. Activation can be achieved either with low-power blue light or through luciferase-derived BRET, but the supplied evidence does not specify construct architecture, luciferase identity, cofactors, or delivery format.
The provided evidence does not report quantitative performance metrics, kinetic constants, dynamic range, or direct comparisons across many model systems. Independent replication is not indicated in the supplied evidence.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
LOV-Turbo can be activated by luciferase-derived BRET instead of external light, enabling interaction-dependent proximity labeling.
We also showed that instead of external light, LOV-Turbo can be activated by BRET from luciferase, enabling interaction-dependent PL.
LOV-Turbo can be activated by luciferase-derived BRET instead of external light, enabling interaction-dependent proximity labeling.
We also showed that instead of external light, LOV-Turbo can be activated by BRET from luciferase, enabling interaction-dependent PL.
LOV-Turbo can be activated by luciferase-derived BRET instead of external light, enabling interaction-dependent proximity labeling.
We also showed that instead of external light, LOV-Turbo can be activated by BRET from luciferase, enabling interaction-dependent PL.
LOV-Turbo can be activated by luciferase-derived BRET instead of external light, enabling interaction-dependent proximity labeling.
We also showed that instead of external light, LOV-Turbo can be activated by BRET from luciferase, enabling interaction-dependent PL.
LOV-Turbo can be activated by luciferase-derived BRET instead of external light, enabling interaction-dependent proximity labeling.
We also showed that instead of external light, LOV-Turbo can be activated by BRET from luciferase, enabling interaction-dependent PL.
LOV-Turbo can be activated by luciferase-derived BRET instead of external light, enabling interaction-dependent proximity labeling.
We also showed that instead of external light, LOV-Turbo can be activated by BRET from luciferase, enabling interaction-dependent PL.
LOV-Turbo can be activated by luciferase-derived BRET instead of external light, enabling interaction-dependent proximity labeling.
We also showed that instead of external light, LOV-Turbo can be activated by BRET from luciferase, enabling interaction-dependent PL.
LOV-Turbo was used for pulse-chase proximity labeling to discover proteins that traffic between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
We used LOV-Turbo for pulse-chase labeling to discover proteins that traffick between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
LOV-Turbo was used for pulse-chase proximity labeling to discover proteins that traffic between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
We used LOV-Turbo for pulse-chase labeling to discover proteins that traffick between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
LOV-Turbo was used for pulse-chase proximity labeling to discover proteins that traffic between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
We used LOV-Turbo for pulse-chase labeling to discover proteins that traffick between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
LOV-Turbo was used for pulse-chase proximity labeling to discover proteins that traffic between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
We used LOV-Turbo for pulse-chase labeling to discover proteins that traffick between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
LOV-Turbo was used for pulse-chase proximity labeling to discover proteins that traffic between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
We used LOV-Turbo for pulse-chase labeling to discover proteins that traffick between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
LOV-Turbo was used for pulse-chase proximity labeling to discover proteins that traffic between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
We used LOV-Turbo for pulse-chase labeling to discover proteins that traffick between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
LOV-Turbo was used for pulse-chase proximity labeling to discover proteins that traffic between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
We used LOV-Turbo for pulse-chase labeling to discover proteins that traffick between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
The authors installed a light-sensitive LOV domain into TurboID to create LOV-Turbo, enabling rapid and reversible light control of proximity-labeling activity with low-power blue light.
Through structure-guided screening and directed evolution, we installed the light-sensitive LOV domain into the PL enzyme TurboID to rapidly and reversibly control its labeling activity with low-power blue light.
The authors installed a light-sensitive LOV domain into TurboID to create LOV-Turbo, enabling rapid and reversible light control of proximity-labeling activity with low-power blue light.
Through structure-guided screening and directed evolution, we installed the light-sensitive LOV domain into the PL enzyme TurboID to rapidly and reversibly control its labeling activity with low-power blue light.
The authors installed a light-sensitive LOV domain into TurboID to create LOV-Turbo, enabling rapid and reversible light control of proximity-labeling activity with low-power blue light.
Through structure-guided screening and directed evolution, we installed the light-sensitive LOV domain into the PL enzyme TurboID to rapidly and reversibly control its labeling activity with low-power blue light.
The authors installed a light-sensitive LOV domain into TurboID to create LOV-Turbo, enabling rapid and reversible light control of proximity-labeling activity with low-power blue light.
Through structure-guided screening and directed evolution, we installed the light-sensitive LOV domain into the PL enzyme TurboID to rapidly and reversibly control its labeling activity with low-power blue light.
The authors installed a light-sensitive LOV domain into TurboID to create LOV-Turbo, enabling rapid and reversible light control of proximity-labeling activity with low-power blue light.
Through structure-guided screening and directed evolution, we installed the light-sensitive LOV domain into the PL enzyme TurboID to rapidly and reversibly control its labeling activity with low-power blue light.
The authors installed a light-sensitive LOV domain into TurboID to create LOV-Turbo, enabling rapid and reversible light control of proximity-labeling activity with low-power blue light.
Through structure-guided screening and directed evolution, we installed the light-sensitive LOV domain into the PL enzyme TurboID to rapidly and reversibly control its labeling activity with low-power blue light.
The authors installed a light-sensitive LOV domain into TurboID to create LOV-Turbo, enabling rapid and reversible light control of proximity-labeling activity with low-power blue light.
Through structure-guided screening and directed evolution, we installed the light-sensitive LOV domain into the PL enzyme TurboID to rapidly and reversibly control its labeling activity with low-power blue light.
LOV-Turbo increases the spatial and temporal precision of proximity labeling.
Overall, LOV-Turbo increases the spatial and temporal precision of PL, expanding the scope of experimental questions that can be addressed with PL.
LOV-Turbo increases the spatial and temporal precision of proximity labeling.
Overall, LOV-Turbo increases the spatial and temporal precision of PL, expanding the scope of experimental questions that can be addressed with PL.
LOV-Turbo increases the spatial and temporal precision of proximity labeling.
Overall, LOV-Turbo increases the spatial and temporal precision of PL, expanding the scope of experimental questions that can be addressed with PL.
LOV-Turbo increases the spatial and temporal precision of proximity labeling.
Overall, LOV-Turbo increases the spatial and temporal precision of PL, expanding the scope of experimental questions that can be addressed with PL.
LOV-Turbo increases the spatial and temporal precision of proximity labeling.
Overall, LOV-Turbo increases the spatial and temporal precision of PL, expanding the scope of experimental questions that can be addressed with PL.
LOV-Turbo increases the spatial and temporal precision of proximity labeling.
Overall, LOV-Turbo increases the spatial and temporal precision of PL, expanding the scope of experimental questions that can be addressed with PL.
LOV-Turbo increases the spatial and temporal precision of proximity labeling.
Overall, LOV-Turbo increases the spatial and temporal precision of PL, expanding the scope of experimental questions that can be addressed with PL.
LOV-Turbo works in multiple contexts and reduces background in biotin-rich environments such as neurons.
“LOV-Turbo” works in multiple contexts and dramatically reduces background in biotin-rich environments such as neurons.
LOV-Turbo works in multiple contexts and reduces background in biotin-rich environments such as neurons.
“LOV-Turbo” works in multiple contexts and dramatically reduces background in biotin-rich environments such as neurons.
LOV-Turbo works in multiple contexts and reduces background in biotin-rich environments such as neurons.
“LOV-Turbo” works in multiple contexts and dramatically reduces background in biotin-rich environments such as neurons.
LOV-Turbo works in multiple contexts and reduces background in biotin-rich environments such as neurons.
“LOV-Turbo” works in multiple contexts and dramatically reduces background in biotin-rich environments such as neurons.
LOV-Turbo works in multiple contexts and reduces background in biotin-rich environments such as neurons.
“LOV-Turbo” works in multiple contexts and dramatically reduces background in biotin-rich environments such as neurons.
LOV-Turbo works in multiple contexts and reduces background in biotin-rich environments such as neurons.
“LOV-Turbo” works in multiple contexts and dramatically reduces background in biotin-rich environments such as neurons.
LOV-Turbo works in multiple contexts and reduces background in biotin-rich environments such as neurons.
“LOV-Turbo” works in multiple contexts and dramatically reduces background in biotin-rich environments such as neurons.
Approval Evidence
1 source5 linked approval claimsfirst-pass slug lov-turbo
“LOV-Turbo” works in multiple contexts and dramatically reduces background in biotin-rich environments such as neurons.
Source:
activation mode claimsupports
LOV-Turbo can be activated by luciferase-derived BRET instead of external light, enabling interaction-dependent proximity labeling.
We also showed that instead of external light, LOV-Turbo can be activated by BRET from luciferase, enabling interaction-dependent PL.
Source:
application claimsupports
LOV-Turbo was used for pulse-chase proximity labeling to discover proteins that traffic between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
We used LOV-Turbo for pulse-chase labeling to discover proteins that traffick between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
Source:
engineering resultsupports
The authors installed a light-sensitive LOV domain into TurboID to create LOV-Turbo, enabling rapid and reversible light control of proximity-labeling activity with low-power blue light.
Through structure-guided screening and directed evolution, we installed the light-sensitive LOV domain into the PL enzyme TurboID to rapidly and reversibly control its labeling activity with low-power blue light.
Source:
overall utility claimsupports
LOV-Turbo increases the spatial and temporal precision of proximity labeling.
Overall, LOV-Turbo increases the spatial and temporal precision of PL, expanding the scope of experimental questions that can be addressed with PL.
Source:
performance claimsupports
LOV-Turbo works in multiple contexts and reduces background in biotin-rich environments such as neurons.
“LOV-Turbo” works in multiple contexts and dramatically reduces background in biotin-rich environments such as neurons.
Source:
Comparisons
Source-backed strengths
Reported strengths include rapid and reversible activation, operation with low-power blue light, and reduced background in biotin-rich environments such as neurons. The source literature also states that LOV-Turbo functions in multiple contexts and was applied to pulse-chase proximity labeling to identify proteins trafficking between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress.
Source:
Through structure-guided screening and directed evolution, we installed the light-sensitive LOV domain into the PL enzyme TurboID to rapidly and reversibly control its labeling activity with low-power blue light.
Source:
“LOV-Turbo” works in multiple contexts and dramatically reduces background in biotin-rich environments such as neurons.
Ranked Citations
- 1.