bioRxivBiophysics
FRET-guided modeling of nucleic acids
Abstract
The functional diversity of RNA is encoded in their innate conformational heterogeneity. The combination of single-molecule spectroscopy and computational modeling offers new, attractive opportunities to map structural transitions within nucleic acid ensembles. Here, we describe a framework to harmonize single-molecule FRET measurements with molecular dynamics simulations and de novo structure prediction. Using either all-atom or implicit fluorophore modeling we recreate FRET experiments in silico, visualize the underlying structural dynamics and quantify the simulated reaction coordinates. Using multiple accessible-contact volumes (multi-ACV) as a post-hoc scoring method for fragment-assembly in Rosetta, we demonstrate that FRET effectively refines de novo RNA structure prediction without the need of explicit dye labeling. We benchmark our FRET-assisted modeling approach on double-labeled DNA strands and validate it against an intrinsically dynamic manganese(II)-binding riboswitch. We show that a FRET coordinate describing the assembly of a four-way junction allows our pipeline to recapitulate the global fold of the riboswitch with sub-helical accuracy to the crystal structure. We conclude that computational fluorescence spectroscopy facilitates the interpretability of dynamic structural ensembles and improves the mechanistic understanding of nucleic acid interactions. Graphical abstractSchematic workflow of integrative FRET modeling using all-atom fluorophores or an accessible-contact volume dye model. All-atom molecular dynamics track the dye coordinate explicitly as part of the simulation while multi-ACV infer mean dye positions post hoc. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/552238v1_ufig1.gif" ALT="Figure 1"> View larger version (81K): org.highwire.dtl.DTLVardef@122bdf7org.highwire.dtl.DTLVardef@1e5c06forg.highwire.dtl.DTLVardef@bcc3aaorg.highwire.dtl.DTLVardef@d205ab_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: fluorescence / live-cell microscopy
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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