Source 1primary paper2024Nature Communications
Toolkit/FRASE
FRASE
Also known as: FRAgments in Structural Environments
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
FRASE, also described as FRASE-bot, is a computational fragment-based ligand discovery method that mines 3D ligand–protein complex structures to build a database of fragments in structural environments. It screens this database against a target protein, seeds the target structure with relevant ligand fragments, and uses a neural network to prioritize fragments with the highest likelihood of being native binders.
Usefulness & Problems
Why this is useful
FRASE is useful for structure-based small-molecule discovery when a target protein can be compared against known ligand-binding structural environments. In the cited 2024 study, application to CIB1 identified a small-molecule CIB1 ligand, indicating utility for discovering target-directed ligands from structural information.
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
FRASE addresses the problem of finding candidate ligand fragments for a protein target by leveraging previously observed ligand–protein structural environments rather than relying only on de novo enumeration. It specifically helps prioritize seeded fragments that are more likely to represent native binders using a neural network model.
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 computational method used to design, rank, or analyze an engineered system.
Mechanisms
neural-network-based binder prioritizationstructural-environment similarity matchingstructure-based fragment seedingTarget processes
recombinationselectionInput: Chemical
Implementation Constraints
Implementation requires available 3D structures of ligand–protein complexes to construct the FRASE database and a target protein structure for structural-environment screening and fragment seeding. The workflow also includes a neural network model for ranking seeded fragments, and the cited validation used a TR-FRET binding assay for the CIB1 hit.
The supplied evidence documents one published application, to CIB1, so validation breadth is limited from the provided record. No details are provided here on model generalization, hit rate across multiple targets, input structure requirements, or comparative performance against other virtual screening methods.
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 source2 linked approval claimsfirst-pass slug frase
FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE).
Source:
method capabilitysupports
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).
Source:
method capabilitysupports
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.
Source:
Comparisons
Source-backed strengths
The method integrates three supported capabilities: mining 3D ligand–protein complexes, matching structural environments in a target protein, and neural-network-based prioritization of seeded fragments. Its application to CIB1 produced a small-molecule ligand with binding confirmed by TR-FRET assay, providing experimental support for the computational workflow.
Ranked Citations
- 1.