Source 1primary paper2018Nature Communications
Toolkit/engineered protein-based Ca2+-sensor
engineered protein-based Ca2+-sensor
Taxonomy: Mechanism Branch / Architecture. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
This tool is an engineered protein-based Ca2+ sensor that switches through mutually exclusive folding of two alternate frames. Computational design and biophysical experiments were used to improve its conformational switching kinetics, producing Ca2+-responsive behavior on the timescale of fast physiological Ca2+ fluctuations.
Usefulness & Problems
Why this is useful
The tool is useful as a protein conformational switch with substantially improved response time, addressing the need for Ca2+ sensors that can track rapid physiological Ca2+ dynamics. The source literature also indicates that the underlying computational design strategy may be applicable to optimizing the kinetics of other protein conformational switches.
Problem solved
It helps solve the problem of slow switching kinetics in engineered protein-based Ca2+ sensors. Specifically, the reported work rationally improved response time so that the sensor operates on the order of fast physiological Ca2+ fluctuations.
Problem links
Need conditional recombination or state switching
DerivedThis tool is an engineered protein-based Ca2+ sensor whose switching process occurs through mutually exclusive folding of two alternate frames. Computational design and biophysical experiments were used to optimize its conformational switching kinetics, yielding substantially faster Ca2+-responsive behavior.
Taxonomy & Function
Primary hierarchy
Mechanism Branch
Architecture: A composed arrangement of multiple parts that instantiates one or more mechanisms.
Mechanisms
ca2+-dependent conformational switchingca2+-dependent conformational switchingmutually exclusive foldingmutually exclusive foldingTechniques
Computational DesignTarget processes
recombinationImplementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationimplementation constraint: multi component delivery burdenoperating role: sensorswitch architecture: multi component
The construct is described as an engineered protein-based Ca2+ sensor whose switching occurs via mutually exclusive folding of two alternate frames. The supplied evidence supports use of computational design and biophysical characterization during optimization, but does not specify cofactors, expression systems, delivery methods, or detailed construct architecture.
The available evidence does not provide quantitative kinetic values, dynamic range, sensitivity, or details of performance across different biological contexts. Independent replication and validation breadth beyond the reported study are not documented in the supplied evidence.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
The computational design strategy may aid in optimizing the kinetics of other protein conformational switches.
Our computational design strategy is general and may aid in optimizing the kinetics of other protein conformational switches.
The computational design strategy may aid in optimizing the kinetics of other protein conformational switches.
Our computational design strategy is general and may aid in optimizing the kinetics of other protein conformational switches.
The computational design strategy may aid in optimizing the kinetics of other protein conformational switches.
Our computational design strategy is general and may aid in optimizing the kinetics of other protein conformational switches.
The computational design strategy may aid in optimizing the kinetics of other protein conformational switches.
Our computational design strategy is general and may aid in optimizing the kinetics of other protein conformational switches.
The computational design strategy may aid in optimizing the kinetics of other protein conformational switches.
Our computational design strategy is general and may aid in optimizing the kinetics of other protein conformational switches.
The computational design strategy may aid in optimizing the kinetics of other protein conformational switches.
Our computational design strategy is general and may aid in optimizing the kinetics of other protein conformational switches.
The computational design strategy may aid in optimizing the kinetics of other protein conformational switches.
Our computational design strategy is general and may aid in optimizing the kinetics of other protein conformational switches.
The computational design strategy may aid in optimizing the kinetics of other protein conformational switches.
Our computational design strategy is general and may aid in optimizing the kinetics of other protein conformational switches.
The computational design strategy may aid in optimizing the kinetics of other protein conformational switches.
Our computational design strategy is general and may aid in optimizing the kinetics of other protein conformational switches.
The computational design strategy may aid in optimizing the kinetics of other protein conformational switches.
Our computational design strategy is general and may aid in optimizing the kinetics of other protein conformational switches.
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
response time on the order of fast physiological Ca2+ fluctuations
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
switching rate increase 32 fold
Approval Evidence
1 source3 linked approval claimsfirst-pass slug engineered-protein-based-ca2-sensor
an engineered protein-based Ca2+-sensor in which the switching process occurs via mutually exclusive folding of two alternate frames
Source:
performance improvementsupports
A computational design strategy combined with biophysical experiments rationally improved the response time of an engineered protein-based Ca2+ sensor.
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
Source:
physiological timescalesupports
The optimized sensor achieved response times on the order of fast physiological Ca2+ fluctuations.
achieving response times on the order of fast physiological Ca2+ fluctuations
Source:
rate increasesupports
The identified mutations increased switching rates by as much as 32-fold.
our strategy identifies mutations that increase switching rates by as much as 32-fold
Source:
Comparisons
Source-backed strengths
A key strength is the reported large enhancement of response times achieved by combining computational design with biophysical experiments. The sensor’s switching mechanism is explicitly defined as mutually exclusive folding between two alternate frames, and the optimized construct was reported to function on physiologically relevant Ca2+ timescales.
Source:
Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor
Compared with ArrayG
engineered protein-based Ca2+-sensor and ArrayG address a similar problem space because they share recombination.
Shared frame: same top-level item type; shared target processes: recombination
Strengths here: looks easier to implement in practice.
Compared with GFP-PHR-caspase8/Flag-CIB1N-caspase8
engineered protein-based Ca2+-sensor and GFP-PHR-caspase8/Flag-CIB1N-caspase8 address a similar problem space because they share recombination.
Shared frame: same top-level item type; shared target processes: recombination
Strengths here: looks easier to implement in practice.
Compared with SIBR-Cas
engineered protein-based Ca2+-sensor and SIBR-Cas address a similar problem space because they share recombination.
Shared frame: same top-level item type; shared target processes: recombination
Ranked Citations
- 1.