First-pass extracted concept

FRET-guided RNA 3D structure selection workflow

Candidate: workflow template1 source documents4 linked claims1 workflow observations4 stage observations4 step observations

Jump to workflow stages Jump to workflow logic Jump to workflow steps Jump to evidence Jump to sources Jump to claims

Live refresh every 5sNext refresh in 5s

Aliases

FRET-guided workflow

Workflow Stage Observations

Stage 1library designin silicoSource 1DOI PubMed

Candidate RNA 3D structure generation

Why this stage exists: This stage creates the initial collection of candidate RNA conformations needed for downstream structural validation and FRET-guided selection.

Selection basis: Generate a collection of candidate conformations using three RNA 3D modeling tools.

Advance criteria: Candidate conformations are produced by RNAComposer, FARFAR2, and AlphaFold3.

Enriches for: conformational diversity

Preserves downstream axes: availability of candidate structures for later structural and FRET-based evaluation

Stage 2in silico filterin silicoSource 1DOI PubMed

Structural validation and eRMSD filtering

Why this stage exists: This stage removes models that fail structural validation before computational FRET prediction.

Selection basis: Watson-Crick base-pairing patterns and an eRMSD threshold

Advance criteria: Only retained structures after Watson-Crick validation and eRMSD filtering proceed to FRET distribution prediction.

Higher fidelity: yes

Enriches for: structural plausibility

Guards against: structurally inconsistent models

Preserves downstream axes: credible inputs for FRET prediction

Stage 3secondary characterizationin silicoSource 1DOI PubMed

FRET distribution prediction for retained structures

Why this stage exists: This stage converts retained structures into predicted observables that can be compared with experiment.

Selection basis: Compute accessible contact volume of the Cy3/Cy5 dye pair using FRETraj to predict FRET distributions.

Advance criteria: Predicted FRET distributions are available for comparison to experimental smFRET data.

Higher fidelity: yes

Enriches for: predicted FRET behavior

Preserves downstream axes: comparability to experimental smFRET data

Stage 4confirmatory validationcell freeSource 1DOI PubMed

Comparison and weighting against experimental smFRET data

Why this stage exists: This stage identifies conformational states compatible with the observed FRET states.

Selection basis: Agreement of predicted FRET distributions with experimental smFRET data

Advance criteria: Conformational states are identified as compatible with observed smFRET states after comparison and weighting.

Higher fidelity: yes

Enriches for: compatibility with observed FRET states

Guards against: models inconsistent with experimental transfer efficiencies

Workflow Logic

Workflow evidenceSource 1

Objective: Identify RNA conformational states consistent with experimental smFRET data from a heterogeneous and difficult-to-predict RNA system.

Why it works: The workflow first generates a collection of candidate RNA conformations, removes structurally implausible models, then predicts dye-pair FRET distributions for retained structures and compares them to experimental smFRET data to identify compatible conformational states.

Priority logic: The campaign narrows from broad candidate structure generation to structural validation/filtering and then to FRET-based comparison against experiment, enriching for models that are both structurally plausible and experimentally compatible.

Validation strategy: Retained structures are evaluated by predicted FRET distributions and weighted against experimental smFRET data.

Target properties: compatibility with observed FRET states, structural plausibility, capture of conformational heterogeneity

Target mechanisms: matching predicted FRET distributions to experimental smFRET states, retaining models consistent with Watson-Crick base-pairing patterns

Target techniques: multi-tool RNA 3D structure prediction, structural validation and eRMSD filtering, accessible contact volume calculation, comparison and weighting against experimental smFRET data

Workflow Step Observations

Step 1designSource 1

Predict candidate RNA 3D structures with three modeling tools

Purpose: Generate a collection of candidate conformations for the target ribosomal RNA tertiary contact.

Why now: Candidate structures are required before any structural validation, filtering, or FRET back-calculation can occur.

Targets properties: conformational diversity

Step 2analysisSource 1

Validate candidate structures by Watson-Crick base-pairing patterns and filter by eRMSD threshold

Purpose: Retain structurally plausible models for downstream FRET prediction.

Why now: Structural filtering is performed before FRET prediction so only retained structures are subjected to the more specific comparison against experimental data.

Targets properties: structural plausibility

Step 3analysisSource 1

Compute Cy3/Cy5 accessible contact volumes and predict FRET distributions with FRETraj

Purpose: Translate retained structures into predicted FRET distributions.

Why now: FRET prediction is done after structural filtering because the abstract states it is performed for each retained structure.

Targets properties: predicted FRET behavior

Step 4decisionSource 1

Compare and weight predicted FRET distributions against experimental smFRET data

Purpose: Identify conformational states compatible with the observed FRET states.

Why now: This final comparison uses the predicted observables from the previous step to select experimentally compatible conformational states.

Validation focus: Compatibility of predicted transfer efficiencies with experimental smFRET observations

Decision gate: Agreement with experimental smFRET data is the basis for identifying compatible conformational states.

Targets properties: agreement with experimental transfer efficiencies

Evidence Snippets

We applied a Förster resonance energy transfer (FRET)-guided strategy to identify RNA conformational states consistent with single-molecule FRET (smFRET) experiments... This FRET-guided workflow, combined with structural validation...

Evidence 1Source 1DOI PubMedprovenance

Supporting Sources

Source 1primary paper2026MEDDOI PubMed

1 evidence snippet 1 workflow logic note 4 workflow stages 4 workflow steps 4 linked claims Claim 1 Claim 2 Claim 3 Candidate RNA 3D structure generation Structural validation and eRMSD filtering

Linked Claims

Claim 1filtering strategysupports2026Source 1DOI PubMed

Predicted RNA 3D models were structurally validated by Watson-Crick base-pairing patterns and filtered using an eRMSD threshold before FRET prediction.

Claim 2performance statementsupports2026Source 1DOI PubMed

Experimental transfer efficiencies can be reproduced using in silico predicted RNA 3D structures.

Claim 3scope statementsupports2026Source 1DOI PubMed

The FRET-guided workflow combined with structural validation provides a foundation for capturing diverse conformational states of flexible RNA motifs.

Claim 4workflow applicationsupports2026Source 1DOI PubMed

The study applied a FRET-guided strategy to identify RNA conformational states consistent with smFRET experiments.