Acidic C-terminal domains autoregulate the RNA chaperone Hfq

Abstract

The RNA chaperone Hfq is an Sm protein that facilitates base pairing between bacterial small RNAs (sRNAs) and mRNAs involved in stress response and pathogenesis. Hfq possesses an intrinsically disordered C-terminal domain (CTD) that may tune the function of the Sm domain in different organisms. In Escherichia coli, the Hfq CTD increases kinetic competition between sRNAs and recycles Hfq from the sRNA-mRNA duplex. Here, de novo Rosetta modeling and competitive binding experiments show that the acidic tip of the E. coli Hfq CTD transiently binds the basic Sm core residues necessary for RNA annealing. The CTD tip competes against non-specific RNA binding, facilitates dsRNA release, and prevents indiscriminate DNA aggregation, suggesting that this acidic peptide mimics nucleic acid to auto-regulate RNA binding to the Sm ring. The mechanism of CTD auto-inhibition predicts the chaperone function of Hfq in bacterial genera and illuminates how Sm proteins may evolve new functions.

Proteins That Chaperone RNA Regulation

“…Recent experiments on Hfq illustrate the importance of these acidic peptides for RNA chaperone activity (176, 177). The last 30 residues of E. coli Hfq are disordered in solution and protrude from the edge of the hexameric ring formed by the stable Sm domain (178, 179).…”

“…The last 30 residues of E. coli Hfq are disordered in solution and protrude from the edge of the hexameric ring formed by the stable Sm domain (178, 179). Acidic residues at the tip of the CTD, which are moderately conserved in bacterial Hfq sequences, interact with the arginine patches on the rim of the hexamer (176), displacing double-stranded RNA and inhibiting the binding of non-specific RNA and DNA (177). In an analogous fashion, acidic residues at the C-terminus of NCp7 were also found to increase the rate of dissociation from nucleic acid substrates (180).…”

A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins

“…This platform has demonstrated extensive success in predicting structures for ordered systems through the use of fragment sampling in combination with a simulated annealing MC approach with limited gradient-based minimization 25 . Additionally, Rosetta has been utilized to identify IDRs and to model IDRs and IDPs with the application of experimental constraints 4,11,24,[26][27][28] .…”

“…Here, we focus on adapting two algorithms: AbInitio, which can predict folded protein structure from sequence, and FloppyTail, which can produce structural ensembles of IDRs 24,26,[29][30][31] . We tested these methods' ability to produce an accurate structural ensemble of an IDP, α-synuclein (αS), for which there exists a battery of experimental data.…”

Architectural principles for Hfq/Crc-mediated regulation of gene expression

“…Hfq is a member of the Lsm/Sm family and shares with that group an ancient structural core that oligomerizes to form toroidal architectures exposing several RNA-binding surfaces. Crystallographic and biophysical data show that RNA recognition is mediated by distinct interactions with distal, proximal and rim faces (Schumacher et al, 2002; Link et al, 2009; Sauer et al, 2012; Panja et al, 2013), as well as revealing the role of the natively unstructured C-terminal tail in autoregulating RNA binding activities (Santiago-Frangos et al, 2016; 2017).…”