Widespread temperature sensitivity and tRNA decay due to mutations in a yeast tRNA.

Microorganisms have universally adapted their RNAs and proteins to survive at a broad range of temperatures and growth conditions. However, for RNAs, there is little quantitative understanding of the effects of mutations on function at high temperatures. To understand how variant tRNA function is affected by temperature change, we used the tRNA nonsense suppressor SUP4oc of the yeast Saccharomyces cerevisiae to perform a high-throughput quantitative screen of tRNA function at two different growth temperatures. This screen yielded comparative values for 9243 single and double variants. Surprisingly, despite the ability of S. cerevisiae to grow at temperatures as low as 15°C and as high as 39°C, the vast majority of variants that could be scored lost half or more of their function when evaluated at 37°C relative to 28°C. Moreover, temperature sensitivity of a tRNA variant was highly associated with its susceptibility to the rapid tRNA decay (RTD) pathway, implying that RTD is responsible for most of the loss of function of variants at higher temperature. Furthermore, RTD may also operate in a met22Δ strain, which was previously thought to fully inhibit RTD. Consistent with RTD acting to degrade destabilized tRNAs, the stability of a tRNA molecule can be used to predict temperature sensitivity with high confidence. These findings offer a new perspective on the stability of tRNA molecules and their quality control at high temperature.

Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs.

Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.

Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures.

RNA secondary structure prediction is widely used to analyze RNA sequences. In an RNA partition function calculation, free energy nearest neighbor parameters are used in a dynamic programming algorithm to estimate statistical properties of the secondary structure ensemble. Previously, partition functions have largely been used to estimate the probability that a given pair of nucleotides form a base pair, the conditional stacking probability, the accessibility to binding of a continuous stretch of nucleotides, or a representative sample of RNA structures. Here it is demonstrated that an RNA partition function can also be used to calculate the exact probability of formation of hairpin loops, internal loops, bulge loops, or multibranch loops at a given position. This calculation can also be used to estimate the probability of formation of specific helices. Benchmarking on a set of RNA sequences with known secondary structures indicated that loops that were calculated to be more probable were more likely to be present in the known structure than less probable loops. Furthermore, highly probable loops are more likely to be in the known structure than the set of loops predicted in the lowest free energy structures.

AccessFold: predicting RNA-RNA interactions with consideration for competing self-structure.

MOTIVATION: There are numerous examples of RNA-RNA complexes, including microRNA-mRNA and small RNA-mRNA duplexes for regulation of translation, guide RNA interactions with target RNA for post-transcriptional modification and small nuclear RNA duplexes for splicing. Predicting the base pairs formed between two interacting sequences remains difficult, at least in part because of the competition between unimolecular and bimolecular structure. RESULTS: Two algorithms were developed for improved prediction of bimolecular RNA structure that consider the competition between self-structure and bimolecular structure. These algorithms utilize two novel approaches to evaluate accessibility: free energy density minimization and pseudo-energy minimization. Free energy density minimization minimizes the folding free energy change per nucleotide involved in an intermolecular secondary structure. Pseudo-energy minimization (called AccessFold) minimizes the sum of free energy change and a pseudo-free energy penalty for bimolecular pairing of nucleotides that are unlikely to be accessible for bimolecular structure. The pseudo-free energy, derived from unimolecular pairing probabilities, is applied per nucleotide in bimolecular pairs, and this approach is able to predict binding sites that are split by unimolecular structures. A benchmark set of 17 bimolecular RNA structures was assembled to assess structure prediction. Pseudo-energy minimization provides a statistically significant improvement in sensitivity over the method that was found in a benchmark to be the most accurate previously available method, with an improvement from 36.8% to 57.8% in mean sensitivity for base pair prediction. AVAILABILITY AND IMPLEMENTATION: Pseudo-energy minimization is available for download as AccessFold, under an open-source license and as part of the RNAstructure package, at: http://rna.urmc.rochester.edu/RNAstructure.html CONTACT: david_mathews@urmc.rochester.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improving RNA secondary structure prediction with structure mapping data.

Methods to probe RNA secondary structure, such as small molecule modifying agents, secondary structure-specific nucleases, inline probing, and SHAPE chemistry, are widely used to study the structure of functional RNA. Computational secondary structure prediction programs can incorporate probing data to predict structure with high accuracy. In this chapter, an overview of current methods for probing RNA secondary structure is provided, including modern high-throughput methods. Methods for guiding secondary structure prediction algorithms using these data are explained, and best practices for using these data are provided. This chapter concludes by listing a number of open questions about how to best use probing data, and what these data can provide.