Toolkit/FRASE-bot

FRASE-bot

Computational Method·Research·Since 2024

Also known as: FRASE-based hit-finding robot

Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.

Summary

FRASE-bot is an in silico fragment-based hit-finding method for drug discovery against unconventional therapeutic targets. It mines thousands of 3D protein-ligand complex structures to build a fragment-in-structural-environment database, matches target protein environments to that database, and uses machine learning to prioritize seeded fragments as candidate binders.

Usefulness & Problems

Why this is useful

FRASE-bot is useful for structure-based hit finding when conventional target classes may be difficult to address, because it distills ligand-binding information from large numbers of known protein-ligand complex structures. In the reported application, it enabled identification of a small-molecule ligand for CIB1, with binding confirmed by TR-FRET.

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-bot addresses the problem of rapidly finding small-molecule starting points for unconventional therapeutic targets from structural information. Specifically, it provides a way to seed a target protein structure with ligand fragments by identifying similar structural environments in a database derived from known protein-ligand complexes.

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.

Target processes

recombinationselection

Input: Chemical

Implementation Constraints

Implementation requires 3D structures of protein-ligand complexes to construct the FRASE database and a target protein structure for structural-environment matching and fragment seeding. The reported validation used a TR-FRET assay to confirm binding of the identified CIB1 ligand, but the evidence does not specify software requirements, input formats, or experimental follow-up workflow details.

The supplied evidence documents one published application to CIB1, so validation breadth is limited. The evidence does not provide quantitative performance metrics, comparative benchmarking, model architecture details, or independent replication across multiple targets.

Validation

Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication

Supporting Sources

Ranked Claims

Claim 1application resultsupports2024Source 1needs review

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
Claim 2application resultsupports2024Source 1needs review

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
Claim 3application resultsupports2024Source 1needs review

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
Claim 4application resultsupports2024Source 1needs review

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
Claim 5application resultsupports2024Source 1needs review

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
Claim 6application resultsupports2024Source 1needs review

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
Claim 7application resultsupports2024Source 1needs review

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
Claim 8binding evidencesupports2024Source 1needs review

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)
Claim 9binding evidencesupports2024Source 1needs review

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)
Claim 10binding evidencesupports2024Source 1needs review

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)
Claim 11binding evidencesupports2024Source 1needs review

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)
Claim 12binding evidencesupports2024Source 1needs review

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)
Claim 13binding evidencesupports2024Source 1needs review

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)
Claim 14binding evidencesupports2024Source 1needs review

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)
Claim 15method capabilitysupports2024Source 1needs review

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.
Claim 16method capabilitysupports2024Source 1needs review

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.
Claim 17method capabilitysupports2024Source 1needs review

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.
Claim 18method capabilitysupports2024Source 1needs review

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.
Claim 19method capabilitysupports2024Source 1needs review

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.
Claim 20method capabilitysupports2024Source 1needs review

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.
Claim 21method capabilitysupports2024Source 1needs review

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.
Claim 22method capabilitysupports2024Source 1needs review

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).
Claim 23method capabilitysupports2024Source 1needs review

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).
Claim 24method capabilitysupports2024Source 1needs review

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).
Claim 25method capabilitysupports2024Source 1needs review

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).
Claim 26method capabilitysupports2024Source 1needs review

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).
Claim 27method capabilitysupports2024Source 1needs review

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).
Claim 28method capabilitysupports2024Source 1needs review

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).
Claim 29method capabilitysupports2024Source 1needs review

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.
Claim 30method capabilitysupports2024Source 1needs review

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.
Claim 31method capabilitysupports2024Source 1needs review

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.
Claim 32method capabilitysupports2024Source 1needs review

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.
Claim 33method capabilitysupports2024Source 1needs review

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.
Claim 34method capabilitysupports2024Source 1needs review

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.
Claim 35method capabilitysupports2024Source 1needs review

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.
Claim 36method introductionsupports2024Source 1needs review

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.
Claim 37method introductionsupports2024Source 1needs review

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.
Claim 38method introductionsupports2024Source 1needs review

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.
Claim 39method introductionsupports2024Source 1needs review

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.
Claim 40method introductionsupports2024Source 1needs review

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.
Claim 41method introductionsupports2024Source 1needs review

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.
Claim 42method introductionsupports2024Source 1needs review

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

2 sources8 linked approval claimsfirst-pass slug frase-bot
We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets.

Source:

FRASE-bot exploits big data and machine learning (ML) to distill 3D information relevant to the target protein from thousands of protein-ligand complexes to seed it with ligand fragments.

Source:

application resultsupports

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

Source:

method capabilitysupports

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.

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:

method introductionsupports

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.

Source:

discovery claimsupports

FRASE-based virtual screening identified a small-molecule ligand for CIB1, described as the first such ligand in the abstract.

FRASE-based virtual screening identified the first small-molecule CIB1 ligand

Source:

method applicationsupports

FRASE-bot was applied to identify ligands for CIB1.

Here, FRASE-bot was applied to identify ligands for Calcium and Integrin Binding protein 1 (CIB1)

Source:

method capabilitysupports

FRASE-bot uses big data and machine learning to distill target-relevant 3D information from thousands of protein-ligand complexes and seed a target protein with ligand fragments.

FRASE-bot exploits big data and machine learning (ML) to distill 3D information relevant to the target protein from thousands of protein-ligand complexes to seed it with ligand fragments.

Source:

Comparisons

Source-backed strengths

The method leverages large-scale 3D structural data from thousands of protein-ligand complexes and applies machine learning to prioritize fragments relevant to a target protein. Its reported utility includes successful application to CIB1, where it identified a small-molecule ligand and obtained binding confirmation in a TR-FRET assay.

Ranked Citations

  1. 1.
    StructuralSource 1Nature Communications2024Claim 1Claim 2Claim 3

    Extracted from this source document.