Disordered RNA chaperones can enhance nucleic acid folding via local charge screening

Abstract

RNA chaperones are proteins that aid in the folding of nucleic acids, but remarkably, many of these proteins are intrinsically disordered. How can these proteins function without a well-defined three-dimensional structure? Here, we address this question by studying the hepatitis C virus core protein, a chaperone that promotes viral genome dimerization. Using single-molecule fluorescence spectroscopy, we find that this positively charged disordered protein facilitates the formation of compact nucleic acid conformations by acting as a flexible macromolecular counterion that locally screens repulsive electrostatic interactions with an efficiency equivalent to molar salt concentrations. The resulting compaction can bias unfolded nucleic acids towards folding, resulting in faster folding kinetics. This potentially widespread mechanism is supported by molecular simulations that rationalize the experimental findings by describing the chaperone as an unstructured polyelectrolyte.

RNA Granules: A View from the RNA Perspective

“…These interactions have been widely studied in viral nucleocapsid proteins, where viral proteins act as RNA chaperones that mask negative charges of nucleic acids. Recent fluorescence resonance energy transfer (FRET) experiments demonstrated that RNA chaperones act as macromolecular counterions that facilitate packing of negatively charged nucleic acids [93]. Therefore, electrostatic interactions may play a similar role in granule assembly.…”

High force catch bond mechanism of bacterial adhesion in the human gut

“…pathways [28][29][30] and receptor-ligand interactions [31][32][33][34] . Singlemolecule FRET (smFRET) is capable of measuring distances at the molecular scale 35 , and has been used to study protein dynamics 36,37 and to characterize structures of receptor-ligand complexes 38,39 . At the computational level, these experimental single-molecule approaches can be elaborated upon by employing molecular dynamics (MD) simulations 40 .…”

DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning

“…Single-molecule Förster resonance energy transfer (smFRET) combined with TIRFm (total internal reflection fluorescence microscopy) is a key powerful method to study the structure of biomolecules and provide a dynamic perspective in structural biology ( Lerner et al, 2018 ). Capturing the real-time readouts of nanometer-scale distances of individual biomolecules by smFRET allows the direct observations of dynamics, interactions, and intermediates of stochastic non-accumulating events, as well as dynamic equilibria between unsynchronized molecules, all of which are obscured in ensemble averaging techniques ( Dimura et al, 2016 ; Hellenkamp et al, 2018 ; Holmstrom et al, 2019 ; Juette et al, 2016 ; Newton et al, 2019 ; Preus et al, 2015 ; Roy et al, 2008 ; Schuler and Eaton, 2008 ; Stella et al, 2018 ). The high fidelity and proficiency of smFRET established it as a key toolbox for the accurate characterization of mechanisms, biomolecular interactions function, and even structures of biomolecules ( Craggs and Kapanidis, 2012 ; Dulin et al, 2018 ; Kalinin et al, 2012 ; Kilic et al, 2018 ; Ratzke et al, 2014 ), under both in vitro ( Schluesche et al, 2007 ; Sharma et al, 2008 ; Stein et al, 2011 ) and in vivo ( Okamoto et al, 2020 ; Sakon and Weninger, 2010 ) conditions.…”