Mono-valent salt corrections for RNA secondary structures in the ViennaRNA package

A gradient phage-assisted continuous evolution method for screening suppressor tRNAs in Escherichia coli

Suppressor tRNAs, notable for their capability of reading through the stop codon while maintaining normal peptide synthesis, are promising in treating diseases caused by premature termination codons (PTC). However, the lack of effective engineering methods for suppressor tRNAs has curtailed their application potential. Here, we introduce a directed evolution technology that employs phage-assisted continuous evolution (PACE), combined with gradient biosensors featuring various PTCs in the M13 gene III. Utilizing this novel methodology, we have successfully evolved tRNATrp (UGG) reading through the UGA stop codon in Escherichia coli. Massively parallel sequencing revealed that these mutations predominantly occurred in the anticodon loop. Finally, two suppressor tRNATrp (UGA) mutants exhibited over fivefold increases in readthrough efficiency.

Unraveling RNA Conformation Dynamics in Mitochondrial Encephalomyopathy, Lactic Acidosis, and Stroke-like Episode Syndrome with Solid-State Nanopores

This study investigates transfer ribonucleic acid (tRNA) conformational dynamics in the context of MELAS (mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes) using solid-state silicon nitride (SiN) nanopore technology. SiN nanopores in thin membranes with specific dimensions exhibit high signal resolution, enabling real-time and single-molecule electronic detection of tRNA conformational changes. We focus on human mitochondrial tRNALeu(UAA) (mt-Leu(UAA)) that decodes Leu codons UUA/UUG (UUR) during protein synthesis on the mt-ribosome. The single A14G substitution in mt-Leu(UAA) is the major cause of MELAS disease. Measurements of current blockades and dwell times reveal distinct conformational dynamics of the wild-type (WT) and the A14G variant of mt-Leu(UAA) in response to the conserved post-transcriptional m1G9 methylation. While the m1G9-modified WT transcript adopts a more stable structure relative to the unmodified transcript, the m1G9-modified MELAS transcript adopts a less stable structure relative to the unmodified transcript. Notably, these differential features were observed at 0.4 M KCl, but not at 3 M KCl, highlighting the importance of experimental settings that are closer to physiological conditions. This work demonstrates the feasibility of the nanopore platform to discern tRNA molecules that differ by a single-nucleotide substitution or by a single methylation event, providing an important step forward to explore changes in the conformational dynamics of other RNA molecules in human diseases.

Nearest-neighbour parametrization of DNA single, double and triple mismatches at low sodium concentration

DNA mismatches, that is, base pairs different from the canonical AT and CG, are involved in numerous biological processes and can be a problem for technological applications such as PCR amplification. The nearest-neighbour (NN) model is the standard approach for predicting melting temperatures and is used in methods of secondary structure predictions and modelling of hybridization kinetics. However, despite its biological and technological importance, existing NN parameters that include DNA mismatches are incomplete, and those available were obtained from a limited set of melting temperature at high sodium concentration. To our knowledge, there is currently no NN set of parameters for up to three mismatches covering all configurations at low sodium concentrations. Here, we are applying the NN model to a large set of 4096 published melting temperatures, covering all combinations of single, double and triple mismatches. Dealing with such a large set of temperature is challenging in several ways, bringing new methodological problems. Here, optimizing a large number of 252 independent parameters has required the development of a new method where we readjust the seed parameters using the definition of the Gibbs free energy. The new parameters predict the training set within 1.1 °C and the validation set to 2.7 °C.