Cryptic tRNAs in chaetognath mitochondrial genomes

The chaetognaths constitute a small and enigmatic phylum of little marine invertebrates. Both nuclear and mitochondrial genomes have numerous originalities, some phylum-specific. Until recently, their mitogenomes seemed containing only one tRNA gene (trnMet), but a recent study found in two chaetognath mitogenomes two and four tRNA genes. Moreover, apparently two conspecific mitogenomes have different tRNA gene numbers (one and two). Reanalyses by tRNAscan-SE and ARWEN softwares of the five available complete chaetognath mitogenomes suggest numerous additional tRNA genes from different types. Their total number never reaches the 22 found in most other invertebrates using that genetic code. Predicted error compensation between codon-anticodon mismatch and tRNA misacylation suggests translational activity by tRNAs predicted solely according to secondary structure for tRNAs predicted by tRNAscan-SE, not ARWEN. Numbers of predicted stop-suppressor (antitermination) tRNAs coevolve with predicted overlapping, frameshifted protein coding genes including stop codons. Sequence alignments in secondary structure prediction with non-chaetognath tRNAs suggest that the most likely functional tRNAs are in intergenic regions, as regular mt-tRNAs. Due to usually short intergenic regions, generally tRNA sequences partially overlap with flanking genes. Some tRNA pairs seem templated by sense-antisense strands. Moreover, 16S rRNA genes, but not 12S rRNAs, appear as tRNA nurseries, as previously suggested for multifunctional ribosomal-like protogenomes.     

B‐factor profile prediction for RNA flexibility using support vector machines

Determining the flexibility of structured biomolecules is important for understanding their biological functions. One quantitative measurement of flexibility is the atomic Debye‐Waller factor or temperature B‐factor. Most existing studies are limited to temperature B‐factors of proteins and their prediction. Only one method attempted to predict temperature B‐factors of ribosomal RNA. Here, we developed and compared machine‐learning techniques in prediction of temperature B‐factors of RNAs. The best model based on Support Vector Machines yields Pearson's correction coefficient at 0.51 for fivefold cross validation and 0.50 for the independent test. Analysis of the performance indicates that the model has the best performance on rRNAs, tRNAs, and protein‐bound RNAs, for long chains in particular. The server is available at http://sparks-lab.org/server/RNAflex . © 2017 Wiley Periodicals, Inc.     

Separation of transfer RNA and 5S ribosomal RNA using capillary electrophoresis

Capillary gel electrophoresis and capillary electrophoresis using entangled polymer solutions was investigated for their applicability for the separation of low-molecular-mass RNAs (transfer RNA and 5S ribosomal RNA), with a size range of 70–135 nucleotides, from bacteria. Cross-linked polyacrylamide gel-filled capillaries (3 and 5%) were used for capillary gel electrophoresis. Good resolution was obtained suing gel-filled capillaries only for small tRNAs with lengths to 79 nucleotides, larger tRNAs and 5S rRNA could not be resolved using this method. Buffers containing sieving additives were employed to improve separations of RNA by capillary electrophoresis using entangled polymer solutions. The use of linear sieving polymers in buffers resolved 5S rRNA and tRNAs, even when they possessed only different secondary structure or small differences in length (1–5 nucleotides).     

Ant colony optimization for predicting RNA folding pathways

RNA folding dynamics plays important roles in various functions of RNAs. To date, coarse-grained modeling has been successfully employed to simulate RNA folding dynamics on the energy landscape composed of secondary structures. In such a modeling, the energy barrier height between metastable structures is a key parameter that crucially affects the simulation results. Although a number of approaches ranging from the exact method to heuristic ones are available to predict the barrier heights, developing an efficient heuristic for this purpose is still an algorithmic challenge.We developed a novel RNA folding pathway prediction method, ACOfoldpath, based on Ant Colony Optimization (ACO). ACO is a widely used powerful combinatorial optimization algorithm inspired from the food-seeking behavior of ants. In ACOfoldpath, to accelerate the folding pathway prediction, we reduce the search space by utilizing originally devised structure generation rules. To evaluate the performance of the proposed method, we benchmarked ACOfoldpath on the known nineteen conformational RNA switches. As a result, ACOfoldpath successfully predicted folding pathways better than or comparable to the previous heuristics. The results of RNA folding dynamics simulations and pseudoknotted pathway predictions are also presented.     

Tertiary interactions determine the accuracy of RNA folding

RNAs must fold into unique three-dimensional structures to function in the cell, but how each polynucleotide finds its native structure is not understood. To investigate whether the stability of the tertiary structure determines the speed and accuracy of RNA folding, docking of a tetraloop with its receptor in a bacterial group I ribozyme was perturbed by site-directed mutagenesis. Disruption of the tetraloop or its receptor destabilizes tertiary interactions throughout the ribozyme by 2-3 kcal/mol, demonstrating that tertiary interactions form cooperatively in the transition from a native-like intermediate to the native state. Nondenaturing PAGE and RNase T1 digestion showed that base pairs form less homogeneously in the mutant RNAs during the transition from the unfolded state to the intermediate. Thus, tertiary interactions between helices bias the ensemble of secondary structures toward native-like conformations. Time-resolved hydroxyl radical footprinting showed that the wild-type ribozyme folds completely within 5-20 ms. By contrast, only 40-60% of a tetraloop mutant ribozyme folds in 30-40 ms, with the remainder folding in 30-200 s via nonnative intermediates. Therefore, destabilization of tetraloop-receptor docking introduces an alternate folding pathway in the otherwise smooth energy landscape of the wild-type ribozyme. Our results show that stable tertiary structure increases the flux through folding pathways that lead directly and rapidly to the native structure.     

Prediction of protein folding rates from primary sequences using hybrid sequence representation

The ability to predict protein folding rates constitutes an important step in understanding the overall folding mechanisms. Although many of the prediction methods are structure based, successful predictions can also be obtained from the sequence. We developed a novel method called prediction of protein folding rates (PPFR), for the prediction of protein folding rates from protein sequences. PPFR implements a linear regression model for each of the mainstream folding dynamics including two-, multi-, and mixed-state proteins. The proposed method provides predictions characterized by strong correlations with the experimental folding rates, which equal 0.87 for the two- and multistate proteins and 0.82 for the mixed-state proteins, when evaluated with out-of-sample jackknife test. Based on in-sample and out-of-sample tests, the PPFR's predictions are shown to be better than most of other sequence only and structure-based predictors and complementary to the predictions of the most recent sequence-based QRSM method. We show that simultaneous incorporation of several characteristics, including the sequence, physiochemical properties of residues, and predicted secondary structure provides improved quality. This hybridized prediction model was analyzed to reveal the complementary factors that can be used in tandem to predict folding rates. We show that bigger proteins require more time for folding, higher helical and coil content and the presence of Phe, Asn, and Gln may accelerate the folding process, the inclusion of Ile, Val, Thr, and Ser may slow down the folding process, and for the two-state proteins increased beta-strand content may decelerate the folding process. Finally, PPFR provides strong correlation when predicting sequences with low similarity.     

RNA degradation in cell extracts: real-time monitoring by fluorescence resonance energy transfer

Short noncoding RNAs are increasingly recognized as key regulators of essential cellular processes such as RNA interference. A better understanding of the processes by which such RNAs are degraded is necessary to expand our knowledge of these processes and our ability to harness them. To this end we have developed a novel fluorescence resonance energy transfer (FRET) assay to monitor in real-time the degradation kinetics of short RNAs by a purified RNase and S100 cytosolic HeLa cell extract. An unstructured RNA is found to be degraded more rapidly than a stem-loop RNA under all conditions tested except for low concentrations of cell extract, showing that secondary structure confers protection against RNase activity. The assay also allows for the quantitative comparison of inhibitors such as Contrad70 and aurin tricarboxylic acid (ATA). Finally, gel electrophoretic FRET analysis confirms that HeLa cell extract is dominated by 5' to 3' exonucleolytic activity.     

Integrated prediction of protein folding and unfolding rates from only size and structural class

Protein stability, folding and unfolding rates are all determined by the multidimensional folding free energy surface, which in turn is dictated by factors such as size, structure, and amino-acid sequence. Work over the last 15 years has highlighted the role of size and 3D structure in determining folding rates, resulting in many procedures for their prediction. In contrast, unfolding rates are thought to depend on sequence specifics and be much more difficult to predict. Here we introduce a minimalist physics-based model that computes one-dimensional folding free energy surfaces using the number of aminoacids (N) and the structural class (α-helical, all-β, or α-β) as only protein-specific input. In this model N sets the overall cost in conformational entropy and the net stabilization energy, whereas the structural class defines the partitioning of the stabilization energy between local and non-local interactions. To test its predictive power, we calibrated the model empirically and implemented it into an algorithm for the PREdiction of Folding and Unfolding Rates (PREFUR). We found that PREFUR predicts the absolute folding and unfolding rates of an experimental database of 52 proteins with accuracies of ±0.7 and ±1.4 orders of magnitude, respectively (relative to experimental spans of 6 and 8 orders of magnitude). Such prediction uncertainty for proteins vastly varying in size and structure is only two-fold larger than the differences in folding (±0.34) and unfolding rates (±0.7) caused by single-point mutations. Moreover, PREFUR predicts protein stability with an accuracy of ±6.3 kJ mol(-1), relative to the 5 kJ mol(-1) average perturbation induced by single-point mutations. The remarkable performance of our simplistic model demonstrates that size and structural class are the major determinants of the folding landscapes of natural proteins, whereas sequence variability only provides the final 10-20% tuning. PREFUR is thus a powerful bioinformatic tool for the prediction of folding properties and analysis of experimental data.     

Learning the Fastest RNA Folding Path Based on Reinforcement Learning and Monte Carlo Tree Search

RNA molecules participate in many important biological processes, and they need to fold into well-defined secondary and tertiary structures to realize their functions. Like the well-known protein folding problem, there is also an RNA folding problem. The folding problem includes two aspects: structure prediction and folding mechanism. Although the former has been widely studied, the latter is still not well understood. Here we present a deep reinforcement learning algorithms 2dRNA-Fold to study the fastest folding paths of RNA secondary structure. 2dRNA-Fold uses a neural network combined with Monte Carlo tree search to select residue pairing step by step according to a given RNA sequence until the final secondary structure is formed. We apply 2dRNA-Fold to several short RNA molecules and one longer RNA 1Y26 and find that their fastest folding paths show some interesting features. 2dRNA-Fold is further trained using a set of RNA molecules from the dataset bpRNA and is used to predict RNA secondary structure. Since in 2dRNA-Fold the scoring to determine next step is based on possible base pairings, the learned or predicted fastest folding path may not agree with the actual folding paths determined by free energy according to physical laws.     

Effects of DNA secondary structure on oligonucleotide probe binding efficiency

Secondary structure motifs in nucleic acid probes generally impair intended hybridization reactions and so efforts to predict and avoid such structures are commonly employed in probe design schemes. Another key facet of probe design that has received much less attention, however, is that secondary structure at targeted probe binding site regions may also impair hybridization. Thus, evaluation of both probe and target site secondary structures together should improve hybridization prediction and design effectiveness. Several challenges confound this goal, including imperfect empirical rules and parameters underlying predictions and the fact that folding algorithms scale poorly with respect to sequence length. Here, we attempt to quantify the consequences of target site structure on predicted hybridization using sequences sampled from the human genome. We also provide a methodology for choosing a reasonable “window size” around target sites that is as small as possible without compromising folding algorithm prediction accuracy.     

Length-Dependent Deep Learning Model for RNA Secondary Structure Prediction

Deep learning methods for RNA secondary structure prediction have shown higher performance than traditional methods, but there is still much room to improve. It is known that the lengths of RNAs are very different, as are their secondary structures. However, the current deep learning methods all use length-independent models, so it is difficult for these models to learn very different secondary structures. Here, we propose a length-dependent model that is obtained by further training the length-independent model for different length ranges of RNAs through transfer learning. 2dRNA, a coupled deep learning neural network for RNA secondary structure prediction, is used to do this. Benchmarking shows that the length-dependent model performs better than the usual length-independent model.     

Matrix-assisted laser desorption/ionization mass spectrometry screening for pseudouridine in mixtures of small RNAs by chemical derivatization, RNase digestion and signature products

We have developed a method to screen for pseudouridines in complex mixtures of small RNAs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). First, the unfractionated crude mixture of tRNAs is digested to completion with an endoribonuclease, such as RNase T1, and the digestion products are examined using MALDI-MS. Individual RNAs are identified by their signature digestion products, which arise through the detection of unique mass values after nuclease digestion. Next, the endonuclease digest is derivatized using N-cyclohexyl-N'-(2-morpholinoethyl)carbodiimide metho-p-toluenesulfonate (CMCT), which selectively modifies all pseudouridine, thiouridine and 2-methylthio-6-isopentenyladenosine nucleosides. MALDI-MS determination of the CMCT-derivatized endonuclease digest reveals the presence of pseudouridine through a 252 Da mass increase over the underivatized digest. Proof-of-concept experiments were conducted using a mixture of Escherichia coli transfer RNAs and endoribonucleases T1 and A. More than 80% of the expected pseudouridines from this mixture were detected using this screening approach, even on an unfractionated sample of tRNAs. This approach should be particularly useful in the identification of putative pseudouridine synthases through detection of their target RNAs and can provide insight into specific small RNAs that may contain pseudouridine.     

NMR-assisted prediction of RNA secondary structure: identification of a probable pseudoknot in the coding region of an R2 retrotransposon

As the rate of functional RNA sequence discovery escalates, high-throughput techniques for reliable structural determination are becoming crucial for revealing the essential features of these RNAs in a timely fashion. Computational predictions of RNA secondary structure quickly generate reasonable models but suffer from several approximations, including overly simplified models and incomplete knowledge of significant interactions. Similar problems limit the accuracy of predictions for other self-folding polymers, including DNA and peptide nucleic acid (PNA). The work presented here demonstrates that incorporating unassigned data from simple nuclear magnetic resonance (NMR) experiments into a dynamic folding algorithm greatly reduces the potential folding space of a given RNA and therefore increases the confidence and accuracy of modeling. This procedure has been packaged into an NMR-assisted prediction of secondary structure (NAPSS) algorithm that can produce pseudoknotted as well as non-pseudoknotted secondary structures. The method reveals a probable pseudoknot in the part of the coding region of the R2 retrotransposon from Bombyx mori that orchestrates second-strand DNA cleavage during insertion into the genome.     

Conformational dynamics of bistable RNAs studied by time-resolved NMR spectroscopy

The structural transition between two alternate conformations of bistable RNAs has been characterized by time-resolved NMR spectroscopy. The mechanism, kinetics, and thermodynamics underlying the global structural transition of bistable RNAs were delineated. Both bistable RNA conformations and a partial unstructured RNA of identical sequence could be trapped using photolabile protecting groups. This trapping allowed for an investigation of the initial folding from an unfolded RNA to one of the preferred conformations of the bistable RNA and of the structural transitions involved. Folding of the secondary structure elements occurs rapidly, while the global structural transition of the bistable RNA occurs on a time scale of minutes and shows marked temperature dependence. Comparison of these results with bistable systems previously investigated leads to the prediction of activation enthalpies (DeltaH++) associated with global structural transitions in RNA.     

Strong correlation between SHAPE chemistry and the generalized NMR order parameter (S2) in RNA

The functions of most RNA molecules are critically dependent on the distinct local dynamics that characterize secondary structure and tertiary interactions and on structural changes that occur upon binding by proteins and small molecule ligands. Measurements of RNA dynamics at nucleotide resolution set the foundation for understanding the roles of individual residues in folding, catalysis, and ligand recognition. In favorable cases, local order in small RNAs can be quantitatively analyzed by NMR in terms of a generalized order parameter, S2. Alternatively, SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) chemistry measures local nucleotide flexibility in RNAs of any size using structure-sensitive reagents that acylate the 2'-hydroxyl position. In this work, we compare per-residue RNA dynamics, analyzed by both S2 and SHAPE, for three RNAs: the HIV-1 TAR element, the U1A protein binding site, and the Tetrahymena telomerase stem loop 4. We find a very strong correlation between the two measurements: nucleotides with high SHAPE reactivities consistently have low S2 values. We conclude that SHAPE chemistry quantitatively reports local nucleotide dynamics and can be used with confidence to analyze dynamics in large RNAs, RNA-protein complexes, and RNAs in vivo.     

RNA Hairpin Folding in the Crowded Cell

Precise secondary and tertiary structure formation is critically important for the cellular functionality of ribonucleic acids (RNAs). RNA folding studies were mainly conducted in vitro, without the possibility of validating these experiments inside cells. Here, we directly resolve the folding stability of a hairpin‐structured RNA inside live mammalian cells. We find that the stability inside the cell is comparable to that in dilute physiological buffer. On the contrary, the addition of in vitro artificial crowding agents, with the exception of high‐molecular‐weight PEG, leads to a destabilization of the hairpin structure through surface interactions and reduction in water activity. We further show that RNA stability is highly variable within cell populations as well as within subcellular regions of the cytosol and nucleus. We conclude that inside cells the RNA is subject to (localized) stabilizing and destabilizing effects that lead to an on average only marginal modulation compared to diluted buffer.     

Mass spectrometry profiling analysis enables the identification of new modifications in ribosomal RNA

Ribosomal RNAs (rRNAs) provide the structural framework of ribosomes and play critical roles in protein translation. In ribosome biogenesis, rRNAs acquire various modifications that can influence the structure and catalytic activity of ribosomes. However, rRNA modifications in plants have yet to be fully defined. Herein, we proposed a method to purify rRNAs by a successive isolation with different strategies, including polyA-based mRNA depletion and agarose gel electrophoresis-based purification, with which highly pure rRNAs could be obtained. In addition, we developed a liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) method to systematically profile and characterize modifications from the isolated highly pure plant 18S rRNA and 25S rRNA. LC-ESI-MS/MS analysis showed that 10 and 12 kinds of modifications were present in plant 18S rRNA and 25S rRNA, respectively. Notably, among these identified modifications, 2 kinds of modifications of N²,N²-dimethylguanosine (m^2,2G) and N⁶,N⁶-dimethyladenosine (m^6,6A) in 18S rRNA, and 4 kinds of modifications of m^2,2G, m^6,6A, N7-methylguanosine (m⁷G) and 3-methyluridin (m³U) in 25S rRNA, were first reported to be present in plants. Moreover, exposure of Arabidopsis thaliana to cadmium (Cd) led to significant changes of modifications in both 18S rRNA and 25S rRNA of plants, indicating that rRNA modifications play important roles in response to environmental stress. The discovery of new modifications in plant rRNAs improves the spectra of plant rRNA modifications and may promote the investigation of the functional roles of plant ribosomes in regulating gene expression.     

Novel multi-epitope protein containing conserved epitopes from different Leishmania species as potential vaccine candidate: Integrated immunoinformatics and molecular dynamics approach

Leishmaniosis, caused by intracellular parasites of the genus Leishmania, has become a serious public health problem around the world, and for which there are currently extensive limitations. In this work, a theoretical model was proposed for the development of a multi-epitope vaccine. The protein GP63 of the parasite was selected for epitopes prediction, due to its important biological role for the infection process and abundance. IEDB tools were used to determine epitopes B and T in Leishmania braziliensis; besides, other conserved epitopes in three species were selected. To improve immunogenicity, 50S ribosomal protein L7 / L12 (ID: P9WHE3) was used as a domain of adjuvant in the assembly process. The folding arrangement of the vaccine was obtained through homologous modeling multi-template with MODELLER v9.21, and a Ramachandran plot analysis was done. Furthermore, physicochemical properties were described with the ProtParam tool and secondary structure prediction combining GOR-IV and SOPMA tools. Finally, a molecular dynamics simulation (50 ns) was performed to establish flexibility and conformational changes. The analysis of the results indicates high conservancy in the epitopes predicted among the four species. Moreover, Ramachandran plot, physicochemical parameters, and secondary structure prediction suggest a stable conformation of the vaccine, after a minimum conformational change that was evaluated with the free energy landscape. The conformational change does not drive any substantial change for epitope exposition on the surface. The vaccine proposed could be tested experimentally to guide new approaches in the development of pan-vaccines; vaccines with regions conserved in multiple species.     

High-molecular-weight rRNAs of cotton seeds

The electrophoretic mobilities of the 25S and 18S rRNAs of cotton seeds in polyacrylamide gel have been studied. A pyrimidyl-RNase hydrolysate of the high-molecular-weight rRNAs was separated into isopleths containing up to decanucleotides. The mono-, di-, and trinucleotide isopleths were separated, respectively, into CP and Up; ApCp, GpCp, ApUp, and GpUp; ApApCp, GpApCp, ApGpCp, ApApUp, GpGpCp, ApGpUp + GpApUp, and GpGpUp on a KhZh 1305 microcolumn liquid chromatograph.