Cocrystal structure of a class-I preQ1 riboswitch reveals a pseudoknot recognizing an essential hypermodified nucleobase

Riboswitches are mRNA domains that bind metabolites and modulate gene expression in cis. We report cocrystal structures of a remarkably compact riboswitch (34 nucleotides suffice for ligand recognition) from Bacillus subtilis selective for the essential nucleobase preQ1 (7-aminomethyl-7-deazaguanine). These reveal a previously unrecognized pseudoknot fold, and suggest a conserved gene-regulatory mechanism whereby ligand binding promotes sequestration of an RNA segment that otherwise assembles into a transcriptional anti-terminator

Inferring Parameters for an Elementary Step Model of DNA Structure Kinetics with Locally Context-Dependent Arrhenius Rates

Models of nucleic acid thermal stability are calibrated to a wide range of experimental observations, and typically predict equilibrium probabilities of nucleic acid secondary structures with reasonable accuracy. By comparison, a similar calibration and evaluation of nucleic acid kinetic models to a broad range of measurements has not been attempted so far. We introduce an Arrhenius model of interacting nucleic acid kinetics that relates the activation energy of a state transition with the immediate local environment of the affected base pair. Our model can be used in stochastic simulations to estimate kinetic properties and is consistent with existing thermodynamic models. We infer parameters for our model using an ensemble Markov chain Monte Carlo (MCMC) approach on a training dataset with 320 kinetic measurements of hairpin closing and opening, helix association and dissociation, bubble closing and toehold-mediated strand exchange. Our new model surpasses the performance of the previously established Metropolis model both on the training set and on a testing set of size 56 composed of toehold-mediated 3-way strand displacement with mismatches and hairpin opening and closing rates: reaction rates are predicted to within a factor of three for 93.4% and 78.5% of reactions for the training and testing sets, respectively