Source 1primary paper2024Nature Communications
Toolkit/TR-FRET assay
TR-FRET assay
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
TR-FRET assay is a time-resolved fluorescence resonance energy transfer binding assay used in the cited study to confirm binding of a small-molecule ligand to CIB1. In this evidence set, it functions as a chemical-input assay for ligand-binding confirmation.
Usefulness & Problems
Why this is useful
The assay is useful for experimentally confirming whether a candidate small molecule binds a target protein after computational or screening-based discovery. Here, it provided binding evidence for a CIB1-directed ligand identified by FRASE-bot.
Source:
We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1)... FRASE-based virtual screening identifies a small-molecule CIB1 ligand
Source:
A neural network model is used to retain fragments with the highest likelihood of being native binders.
Source:
FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE).
Source:
The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments.
Problem solved
It addresses the need to validate that a computationally identified CIB1 ligand shows measurable target engagement in an experimental binding assay. The supplied evidence specifically supports ligand-binding confirmation rather than broader functional or cellular characterization.
Source:
We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1)... FRASE-based virtual screening identifies a small-molecule CIB1 ligand
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete measurement method used to characterize an engineered system.
Techniques
Functional AssayTarget processes
No target processes tagged yet.
Input: Chemical
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: sensor
The available evidence only states that binding was confirmed in a TR-FRET assay. No construct design, labeling strategy, donor-acceptor pair, buffer conditions, instrumentation, or expression system details are provided in the supplied material.
The supplied evidence does not report assay format details, fluorophores, detection wavelengths, affinity values, dynamic range, throughput, or controls. Validation is limited here to a single reported use case involving CIB1 ligand-binding confirmation.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
Applying FRASE-bot to CIB1 identified a small-molecule CIB1 ligand.
We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1)... FRASE-based virtual screening identifies a small-molecule CIB1 ligand
Applying FRASE-bot to CIB1 identified a small-molecule CIB1 ligand.
We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1)... FRASE-based virtual screening identifies a small-molecule CIB1 ligand
Applying FRASE-bot to CIB1 identified a small-molecule CIB1 ligand.
We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1)... FRASE-based virtual screening identifies a small-molecule CIB1 ligand
Applying FRASE-bot to CIB1 identified a small-molecule CIB1 ligand.
We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1)... FRASE-based virtual screening identifies a small-molecule CIB1 ligand
Applying FRASE-bot to CIB1 identified a small-molecule CIB1 ligand.
We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1)... FRASE-based virtual screening identifies a small-molecule CIB1 ligand
Applying FRASE-bot to CIB1 identified a small-molecule CIB1 ligand.
We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1)... FRASE-based virtual screening identifies a small-molecule CIB1 ligand
Applying FRASE-bot to CIB1 identified a small-molecule CIB1 ligand.
We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1)... FRASE-based virtual screening identifies a small-molecule CIB1 ligand
The identified small-molecule CIB1 ligand had binding confirmed in a TR-FRET assay.
a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay)
The identified small-molecule CIB1 ligand had binding confirmed in a TR-FRET assay.
a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay)
The identified small-molecule CIB1 ligand had binding confirmed in a TR-FRET assay.
a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay)
The identified small-molecule CIB1 ligand had binding confirmed in a TR-FRET assay.
a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay)
The identified small-molecule CIB1 ligand had binding confirmed in a TR-FRET assay.
a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay)
The identified small-molecule CIB1 ligand had binding confirmed in a TR-FRET assay.
a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay)
The identified small-molecule CIB1 ligand had binding confirmed in a TR-FRET assay.
a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay)
A neural network model is used to retain seeded fragments with the highest likelihood of being native binders.
A neural network model is used to retain fragments with the highest likelihood of being native binders.
A neural network model is used to retain seeded fragments with the highest likelihood of being native binders.
A neural network model is used to retain fragments with the highest likelihood of being native binders.
A neural network model is used to retain seeded fragments with the highest likelihood of being native binders.
A neural network model is used to retain fragments with the highest likelihood of being native binders.
A neural network model is used to retain seeded fragments with the highest likelihood of being native binders.
A neural network model is used to retain fragments with the highest likelihood of being native binders.
A neural network model is used to retain seeded fragments with the highest likelihood of being native binders.
A neural network model is used to retain fragments with the highest likelihood of being native binders.
A neural network model is used to retain seeded fragments with the highest likelihood of being native binders.
A neural network model is used to retain fragments with the highest likelihood of being native binders.
A neural network model is used to retain seeded fragments with the highest likelihood of being native binders.
A neural network model is used to retain fragments with the highest likelihood of being native binders.
FRASE-bot mines 3D structures of ligand-protein complexes to create a database of fragments in structural environments.
FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE).
FRASE-bot mines 3D structures of ligand-protein complexes to create a database of fragments in structural environments.
FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE).
FRASE-bot mines 3D structures of ligand-protein complexes to create a database of fragments in structural environments.
FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE).
FRASE-bot mines 3D structures of ligand-protein complexes to create a database of fragments in structural environments.
FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE).
FRASE-bot mines 3D structures of ligand-protein complexes to create a database of fragments in structural environments.
FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE).
FRASE-bot mines 3D structures of ligand-protein complexes to create a database of fragments in structural environments.
FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE).
FRASE-bot mines 3D structures of ligand-protein complexes to create a database of fragments in structural environments.
FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE).
The FRASE database can be screened to identify structural environments similar to those in a target protein and seed the target structure with relevant ligand fragments.
The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments.
The FRASE database can be screened to identify structural environments similar to those in a target protein and seed the target structure with relevant ligand fragments.
The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments.
The FRASE database can be screened to identify structural environments similar to those in a target protein and seed the target structure with relevant ligand fragments.
The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments.
The FRASE database can be screened to identify structural environments similar to those in a target protein and seed the target structure with relevant ligand fragments.
The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments.
The FRASE database can be screened to identify structural environments similar to those in a target protein and seed the target structure with relevant ligand fragments.
The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments.
The FRASE database can be screened to identify structural environments similar to those in a target protein and seed the target structure with relevant ligand fragments.
The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments.
The FRASE database can be screened to identify structural environments similar to those in a target protein and seed the target structure with relevant ligand fragments.
The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments.
FRASE-bot is introduced as a hit-finding method intended to expedite drug discovery for unconventional therapeutic targets.
We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets.
FRASE-bot is introduced as a hit-finding method intended to expedite drug discovery for unconventional therapeutic targets.
We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets.
FRASE-bot is introduced as a hit-finding method intended to expedite drug discovery for unconventional therapeutic targets.
We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets.
FRASE-bot is introduced as a hit-finding method intended to expedite drug discovery for unconventional therapeutic targets.
We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets.
FRASE-bot is introduced as a hit-finding method intended to expedite drug discovery for unconventional therapeutic targets.
We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets.
FRASE-bot is introduced as a hit-finding method intended to expedite drug discovery for unconventional therapeutic targets.
We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets.
FRASE-bot is introduced as a hit-finding method intended to expedite drug discovery for unconventional therapeutic targets.
We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets.
Approval Evidence
1 source1 linked approval claimfirst-pass slug tr-fret-assay
binding confirmed in a TR-FRET assay
Source:
binding evidencesupports
The identified small-molecule CIB1 ligand had binding confirmed in a TR-FRET assay.
a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay)
Source:
Comparisons
Source-backed strengths
The cited study reports that binding of the identified small-molecule ligand to CIB1 was confirmed in a TR-FRET assay. The evidence supports TR-FRET as an orthogonal experimental readout following in silico ligand discovery, but does not provide quantitative performance metrics in the supplied text.
Compared with chronoamperometry
TR-FRET assay and chronoamperometry address a similar problem space.
Shared frame: same top-level item type; same primary input modality: chemical
Compared with multicomponent, ligand-functionalized microarrays
TR-FRET assay and multicomponent, ligand-functionalized microarrays address a similar problem space.
Shared frame: same top-level item type; same primary input modality: chemical
Compared with solid phase extraction
TR-FRET assay and solid phase extraction address a similar problem space.
Shared frame: same top-level item type; same primary input modality: chemical
Ranked Citations
- 1.