single-molecule FRET

Stages

1. 1.
   Candidate RNA 3D structure generation(library_design)

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

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

2. 2.
   Structural validation and eRMSD filtering(in_silico_filter)

   This stage removes models that fail structural validation before computational FRET prediction.

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

3. 3.
   FRET distribution prediction for retained structures(secondary_characterization)

   This stage converts retained structures into predicted observables that can be compared with experiment.

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

4. 4.
   Comparison and weighting against experimental smFRET data(confirmatory_validation)

   This stage identifies conformational states compatible with the observed FRET states.

   Selection: Agreement of predicted FRET distributions with experimental smFRET data

Steps

1. 1.
   Predict candidate RNA 3D structures with three modeling toolscandidate structure generators

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

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

2. 2.
   Validate candidate structures by Watson-Crick base-pairing patterns and filter by eRMSD threshold

   Retain structurally plausible models for downstream FRET prediction.

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

3. 3.
   Compute Cy3/Cy5 accessible contact volumes and predict FRET distributions with FRETrajFRET back-calculation method

   Translate retained structures into predicted FRET distributions.

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

4. 4.
   Compare and weight predicted FRET distributions against experimental smFRET data

   Identify conformational states compatible with the observed FRET states.

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

Stages

1. 1.
   Mutation panel selection(library_design)

   The study first defines which sequence perturbations will be compared in the binding-folding analysis.

   Selection: PD-linked single-residue mutations A30P, A53T, and E46K were chosen, along with αSyn 1–107 to probe the role of the C-terminal tail.

2. 2.
   Ligand-induced secondary structure characterization(functional_characterization)

   This stage establishes how WT and mutant proteins respond to SDS in ensemble secondary-structure measurements.

   Selection: Use SDS as a lipid mimetic to induce ordered structures and monitor secondary structural changes by CD spectroscopy.

3. 3.
   Thermal unfolding analysis(secondary_characterization)

   This stage distinguishes three-state versus two-state behavior and identifies mutation-specific perturbation of the coupled folding-binding landscape.

   Selection: Monitor ellipticity at 222 nm to identify temperature-induced conformational transitions and infer state behavior.

4. 4.
   Single-molecule conformational state mapping(confirmatory_validation)

   The paper uses smFRET to directly observe conformational distributions and confirm the mutation-dependent loss of the F-state peak in A30P.

   Selection: Use smFRET across a wide SDS concentration range to compare state populations and confirm the altered A30P folding landscape.

Steps

1. 1.
   Select PD-linked mutants and a truncation variant

   Define the comparison set for testing mutation effects on α-synuclein coupled binding and folding.

   The study first specifies which sequence variants will be examined before applying biophysical assays.

2. 2.
   Induce ordered structures with SDS and measure helicity by far-UV CDassay

   Measure how SDS concentration changes secondary structure in WT and mutant proteins.

   This provides an initial ensemble readout of ligand-induced folding before more detailed thermodynamic and single-molecule analysis.

3. 3.
   Monitor ellipticity at 222 nm to identify conformational transitionsassay

   Determine whether each variant shows three-state or two-state thermal unfolding behavior.

   After establishing SDS-induced helicity, the study next resolves thermodynamic state behavior needed to interpret mutation-specific folding landscapes.

4. 4.
   Label A30P α-synuclein and measure smFRET across SDS concentrationsassay

   Directly observe single-molecule conformational distributions and compare A30P with WT.

   The paper uses smFRET after ensemble characterization to confirm that the altered A30P landscape reflects loss of the F-state peak rather than only bulk-average changes.