Biology subjects

Mathews, D. H.

Publications and source records attributed to Mathews, D. H..

18 recordsLinked to original sources

bioRxiv2026

Longer Is Not Always Better: Effects of Equilibration Length on Umbrella Sampling Estimations for RNA Hairpin Folding Stabilities

Umbrella sampling is widely used to estimate biomolecular free energy landscapes and relative folding stabilities. Although equilibration is a critical component of umbrella sampling workflows, the impact of equilibration length on thermodynamic predictions remains poorly understood. Here, we investigate the effect of equilibration length on relative folding free energy predictions for four RNA hairpins with loop sequences GUGAAA, CUGGGA, GUAAUA, and UUAAUU with helical stems of three base pairs. Umbrella sampling simulations were performed using an end-to-end distance reaction coordinate spanning 15-45 [A], where equilibrium simulations (windows) were spaced at roughly 1 [A] intervals. In these calculations, the hairpin stem-loops were allowed to equilibrate in an end-to-end distance window and then the coordinates were transferred to the next larger end-to-end distance window to equilibrate. Two equilibration lengths, 2 ns and 100 ns per window, were followed by 600 ns of production sampling. Potential of mean force (PMF) profiles were reconstructed using the Weighted Histogram Analysis Method (WHAM) and used to calculate pairwise free energy differences with thermodynamic cycles. Increasing the equilibration length produced substantial, sequence-dependent changes in the reconstructed free energy landscapes. The 100 ns protocol generated markedly flatter PMFs for GUGAAA and UUAAUU and pronounced reshaping of the free energy landscape for GUAAUA. These changes were accompanied by reductions in hydrogen-bonding and stacking interactions, particularly within the intermediate regions of the reaction coordinate. The resulting thermodynamic predictions, as free energy change differences, were therefore highly sensitive to equilibration length. Across nearly all hairpin pairs, the 100 ns equilibration yielded substantially larger magnitude free energy change difference values than the corresponding 2 ns equilibration, with differences that greatly exceeded replica-to-replica variability. Comparison with optical melting measurements and nearest-neighbor thermodynamic predictions revealed that 2 ns equilibration times more closely agreed with experimental values than those obtained using 100 ns equilibration. These findings demonstrate that longer equilibration can systematically alter the structural ensembles sampled during umbrella sampling and amplify predicted stability differences without improving agreement with experiment. More widely, our results highlighted equilibration length as a critical and nontrivial parameter in RNA free energy calculations and demonstrate that increased equilibration does not necessarily lead to more accurate thermodynamic predictions.

biophysics↗

bioRxiv2026

Markov models of SHAPE data improve secondary structure prediction

RNA structure is a key determinant of RNA function and regulation. The coupling of chemical probing technologies, such as SHAPE, with deep sequencing has enabled large-scale experimental characterization of RNA structures in complex samples and under diverse conditions. Furthermore, probing data are often used to guide thermodynamics-based secondary structure prediction algorithms and have been shown to improve their accuracy. However, current algorithms treat these single-nucleotide measurements as statistically independent signals, inherently overlooking short-range dependencies in the data. Here, we show that discretized SHAPE data display context dependence within loop regions and within stem regions and we use Markov models to formally capture such dependencies. We then leverage Markov modeling in the classification of small structure motifs from their discretized SHAPE data signatures and subsequently integrate the classifying feature into the dynamic programming recursions that underlie computational RNA folding. Compared to state-of-the-art SHAPE-guided structure prediction methods, our Markov-informed framework improves prediction performance. Furthermore, we identify SHAPE signatures characteristic of highly stable hairpins, such as GAAA, GCAA, and UUCG tetraloops, and integrate these insights into the folding recursions to further improve predictions. Overall, the proposed framework provides a foundation for context-aware statistical modeling of SHAPE data, particularly in loop regions, where signal characterization has proven challenging due to high variance. This work further demonstrates that finer modeling of SHAPE data has the potential to push the limits of data-guided secondary structure prediction.

bioinformatics↗

bioRxiv2026

Nearest Neighbor Parameters for Estimating RNA Folding Stability with In Vivo-like Conditions

RNAs regulate gene expression and cellular processes, often relying on specific conformations for function. RNA folding is hierarchical and sequence-dependent, with nearest-neighbor thermodynamic models commonly used to predict secondary structure. Current models were developed using optical melting experiments in 1 M NaCl, which does not represent the cellular environment. To address this, we developed a new model in Advanced Dulbecco's Modified Eagle Medium (Adv. DMEM), which mimics mammalian extracellular ionic composition. This in vivo-like model provides RNA folding parameters for helical base stacks and loop motifs. Optical melting experiments revealed helical stacks, particularly tandem G-U pairs, are less stabilizing in Adv. DMEM. Loop parameters were generally destabilizing but highly dependent on both sequence and loop type, with internal loops displaying idiosyncratic behavior. Structure prediction benchmarking revealed minimal differences overall, except for tRNAs, which showed improved prediction reliability and enhanced cloverleaf stability. Notably, tRNAs lack internal loops, suggesting further studies in Adv. DMEM could refine secondary structure predictions. This in vivo-like parameter set is included in the RNAstructure software package. Grounding these parameters in a physiologically relevant environment, we improve the biological relevance of RNA secondary structure predictions and establish a foundation for studying RNA folding under in vivo conditions.

biochemistry↗

bioRxiv2026

40S ribosomal subunits move along the 5' UTR through an eIF4A-independent mechanism in higher eukaryotes

During translation initiation, the 40S small ribosomal subunit is recruited to the mRNA 5' cap and scans the 5' untranslated region (UTR) to locate the start codon. While the mechanism of 40S translocation remains elusive, the RNA helicase eIF4A has long been suspected as the primary molecular motor driving 40S scanning. In this study, we utilized GFP reporter mRNAs to investigate the impact of 5' UTR length on translational efficiency. We found that an 8-fold variation in the length of unstructured 5' UTRs did not lead to substantial changes in translation efficiency in wheat germ extract (WGE) and human HEK293T cell lysate. By contrast, the presence of a stable stem-loop in the middle of the 5' UTR significantly reduced cap-dependent translation. These results suggest that mRNA scanning is not rate-limiting when the UTR is devoid of secondary structure. Inhibition of eIF4A by hippuristanol in cell-free protein synthesis systems yielded an equivalent decrease in translation for mRNAs with short and long unstructured 5' UTRs, indicating that eIF4A may be dispensable for 40S scanning. Our data suggest that helicase-independent one-dimensional diffusion may be the primary mechanism enabling 40S movement along the 5' UTR during initiation.

biochemistry↗

bioRxiv2026

Nearest Neighbor Parameters for Estimating the Folding Stability of RNA Including Pseudouridine

Nearest neighbor parameters are widely used in software for estimating the conformational stability of an RNA sequence folding into a specific structure. Folding stability for RNA with canonical nucleotides A, C, G, and U has been widely studied, but the same is not true for most modified nucleotides. In this work, we present a comprehensive set of nearest neighbor parameters for estimating the folding stability of RNA including pseudouridine in helical or loop contexts. These parameters are derived from 210 optical melting experiments involving helices with pseudouridine-A and pseudouridine-G pairs and with pseudouridine in loop motifs. The experiments include sequences with pseudouridine and U in the same strand, including U-A and U-G pairs, allowing us to consider the folding stability of sequences with both U and pseudouridine. On average, pseudouridine stabilizes RNA folding compared to U in an analogous motif, although this effect is sequence-context dependent. These parameters improve the modeling of folding stability for RNA secondary structures containing pseudouridine. We demonstrate that these parameters successfully model the secondary structure change for Saccharomyces cerevisiae U2 snRNA when two additional inducible pseudouridines are present. These parameters are freely available and incorporated into the RNAstructure software package. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/725682v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@e1167aorg.highwire.dtl.DTLVardef@18ac7f0org.highwire.dtl.DTLVardef@4c909eorg.highwire.dtl.DTLVardef@aa8bca_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

RNA Folding Nearest Neighbor Parameters Including the Modification 1-Methyl-Pseudouridine

Nearest neighbor analysis is commonly used to estimate RNA folding stabilities. In this contribution, we report a set of RNA folding nearest neighbor parameters for estimating free energy change for RNA sequences including 1-methyl-pseudouridine. Development of mRNA vaccines has identified 1-methyl-pseudouridine as a key nucleobase modification for suppressing innate immune responses. However, the contributions of these modifications to RNA folding stability were unclear. Our new parameters provide helical terms for 1-methyl-pseudouridine-adenine and 1-methyl-pseudouridine-guanine base pairs. The parameters also estimate loop stabilities for loops with 1-methyl-pseudouridine or a combination of 1-methyl-pseudouridine and uridine. These parameters are derived using 208 optical melting experiments and tested against an additional 16 optical melting experiments. On average, we find that substitution of uridine with 1-methyl-pseudouridine stabilizes RNA folding, with the extent of stabilization depending on adjacent sequence. The estimation of tRNA folding ensembles for tRNA sequences with 1-methyl-pseudouridine was significantly improved using the new nearest neighbor parameters. The new nearest neighbor parameters are provided as part of the RNAstructure software package. With these parameters, the secondary structures of natural sequences with 1-methyl-pseudouridine and mRNA therapeutics fully substituted with 1-methyl-pseudouridine can be modeled.

bioinformatics↗

bioRxiv2026

Ubiquitous low-energy RNA fluctuations and energetic coupling measured by chemical probing

RNA function is governed by RNA folding, but strategies for measuring RNA folding thermodynamics are limited, and fundamental questions such as the energy of base pair opening remain debated. Here we introduce Probing-Resolved Inference of Molecular Energetics (PRIME) to extract nucleotide-resolution RNA structural energetics from scalable chemical probing experiments. Applying PRIME to diverse RNAs, we find that RNA base pairs and tertiary interactions dynamically open with free energies of 0.5-3 kcal/mol, revealing that RNA nucleotides ubiquitously sample open conformations at biologically accessible energies. PRIME further resolves energetic coupling across RNAs, providing an energetic understanding of RNA structural dynamics and long-range coordination in RNA folding. PRIME represents a widely accessible strategy for interrogating RNA thermodynamics, enabling mechanistic understanding and engineering of RNA biology.

biophysics↗

bioRxiv2025

Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods

The diverse regulatory functions, protein production capacity, and stability of natural and synthetic RNAs are closely tied to their ability to fold into intricate structures. Determining RNA structure is thus fundamental to RNA biology and bioengineering. Among existing approaches to structure determination, computational secondary structure prediction offers a rapid and low-cost strategy and is thus widely used, especially when seeking to identify functional RNA elements in large transcriptomes or screen massive libraries of novel designs. While traditional approaches rely on detailed measurements of folding energetics and/or probabilistic modeling of structural data, recent years have witnessed a surge in deep learning methods, inspired by their tremendous success in protein structure prediction. However, the limited diversity and volume of known RNA structures can impede their ability to accurately predict structures markedly different from the ones they have seen. This is known as the generalization gap and currently poses a major barrier to progress in the field. In this Perspective article, we gauge method generalizability using a new benchmark dataset of structured RNAs we curated from the Protein Data Bank. We also discuss the emergence of deep learning methods for predicting structure probing data and use a new dataset to underscore generalization challenges unique to this domain along with directions for future improvement. Expanding beyond improving predictive accuracy, we review how advances in deep learning have recently enabled scalable and accessible optimization of traditional structure prediction methods and their seamless integration with modern neural networks.

bioinformatics↗

bioRxiv2025

AlignmentFold and AlignmentPartition: Improving the align-then-fold approach for RNA secondary structure prediction

The combination of free energy minimization with sequence covariation analysis is a popular approach for predicting conserved secondary structure using a fixed alignment of homologous RNA sequences. In this work we revisit this approach by developing the new AlignmentFold and AlignmentPartition programs. AlignmentFold and AlignmentPartition predict consensus minimum free energy structures and base pairing probabilities, respectively. We determined new nearest neighbor thermodynamic parameters for gaps and non-canonical base pairs that align with base pairs. These frequently occur for structure prediction with a fixed input alignment. We assessed the impact of alignment prediction method and size on the prediction accuracy. We also assessed the improvement in prediction accuracy conferred by sequence covariation analysis. In contrast to previous solutions, we do not include covariation in the scoring of structure quality and achieve prediction accuracy as good as other tools. AlignmentFold and AlignmentPartition are freely available as part of the RNAstructure software package at https://rna.urmc.rochester.edu/RNAstructure.html.

bioinformatics↗

bioRxiv2025

NMR and molecular dynamics demonstrate the RNA internal loop GAGU is dynamic and adjacent base pairs determine conformational preference

The conformational variability of RNA duplexes with the internal loop 5GAGU/3UGAG was investigated by a combination of nuclear magnetic resonance spectroscopy (NMR) and all-atom molecular dynamics (MD) simulations. A previous study showed that the CG-flanked internal loop in the sequence 5GACGAGUGUCA/3ACUGUGAGCAG existed in a major conformation (conformation I) characterized by Uridines U7 and U7* bulging into solution, A5 and A5* stacking, and with G4-G6* and G6-G4* base pairs closing the loop on either end. It was also determined that a minor conformation existed with a set of closing G-U pairs with a bifurcated hydrogen bond and A-G non-canonical pairs with a single hydrogen bond (conformation II). A maximum hydrogen bonding structure with wobble G-U pairs and A-G pairs with two hydrogen bonds (conformation III) was not observed by NMR or predicted by molecular dynamics. In this work, alternative flanking base pairs A-U, U-A and G-C were studied by substituting the C-G base pair adjacent to the GAGU internal loop. NMR spectra demonstrated changes in conformational preference depending on the identity of the flanking pair. Clustering analyses of structures from MD simulations of C-G- and U-A-flanked duplexes showed transitions between minor conformations II and III with a greater fraction of the structures in conformation II, in spite of the simulations starting in conformation III. In addition, U-A-flanked simulations contained a substantial amount (25%) of structures in an intermediate state between conformations II and III. A-U- and G-C-flanked structures were all in a single cluster whose centroid structure was in conformation II. MD simulations showed a dominance of structure II over structure III, in agreement with NMR data for C-G closure, but in contrast with the NMR data for other closures. Simulations starting in conformation I did not transition to either of II and III, with all structures being in a single cluster for all flanking base pairs except for G-C-closure where 22% of the structures were in a state defined by a C3endo sugar pucker for G4 and G4*. Bases G4 and G4* were also in syn orientation around the glycosidic bond for all flanking pairs while G6 and G6* transitioned between syn and anti orientations for C-G- and A-U-flanked simulations, consistent with NMR observations.

biophysics↗

bioRxiv2024

DecoyFinder: Identification of Contaminants in Sets of Homologous RNA Sequences

MotivationRNA structure is essential for the function of many non-coding RNAs. Using multiple homologous sequences, which share structure and function, secondary structure can be predicted with much higher accuracy than with a single sequence. It can be difficult, however, to establish a set of homologous sequences when their structure is not yet known. We developed a method to identify sequences in a set of putative homologs that are in fact non-homologs. ResultsPreviously, we developed TurboFold to estimate conserved structure using multiple, unaligned RNA homologs. Here, we report that the positive predictive value of TurboFold is significantly reduced by the presence of contamination by non-homologous sequences, although the reduction is less than 1%. We developed a method called DecoyFinder, which applies machine learning trained with features determined by TurboFold, to detect sequences that are not homologous with the other sequences in the set. This method can identify approximately 45% of non-homologous sequences, at a rate of 5% misidentification of true homologous sequences. AvailabilityDecoyFinder and TurboFold are incorporated in RNAstructure, which is provided for free and open source under the GPL V2 license. It can be downloaded at http://rna.urmc.rochester.edu/RNAstructure.html

bioinformatics↗

bioRxiv2023

memerna: Sparse RNA Folding Including Coaxial Stacking

AO_SCPLOWBSTRACTC_SCPLOWDetermining RNA secondary structure is a core problem in computational biology. Fast algorithms for predicting secondary structure are fundamental to this task. We describe a modified formulation of the Zuker-Stiegler algorithm with coaxial stacking, a stabilizing interaction in which the ends of multi-loops are stacked. In particular, optimal coaxial stacking is computed as part of the dynamic programming state, rather than inline. We introduce a new notion of sparsity, which we call replaceability. The modified formulation along with replaceability allows sparsification to be applied to coaxial stacking as well, which increases the speed of the algorithm. We implemented this algorithm in software we call memerna, which we show to have the fastest exact RNA folding implementation out of several popular RNA folding packages supporting coaxial stacking. We also introduce a new notation for secondary structure which includes coaxial stacking, terminal mismatches, and dangles (CTDs) information.

bioinformatics↗

bioRxiv2022

Generation and functional analysis of defective viral genomes during SARS-CoV-2 infection

Defective viral genomes (DVGs) have been identified in many RNA viruses as a major factor influencing antiviral immune response and viral pathogenesis. However, the generation and function of DVGs in SARS-CoV-2 infection are less known. In this study, we elucidated DVG generation in SARS-CoV-2 and its relationship with host antiviral immune response. We observed DVGs ubiquitously from RNA-seq datasets of in vitro infections and autopsy lung tissues of COVID-19 patients. Four genomic hotspots were identified for DVG recombination and RNA secondary structures were suggested to mediate DVG formation. Functionally, bulk and single cell RNA-seq analysis indicated the IFN stimulation of SARS-CoV-2 DVGs. We further applied our criteria to the NGS dataset from a published cohort study and observed significantly higher DVG amount and frequency in symptomatic patients than that in asymptomatic patients. Finally, we observed unusually high DVG frequency in one immunosuppressive patient up to 140 days after admitted to hospital due to COVID-19, first-time suggesting an association between DVGs and persistent viral infections in SARS-CoV-2. Together, our findings strongly suggest a critical role of DVGs in modulating host IFN responses and symptom development, calling for further inquiry into the mechanisms of DVG generation and how DVGs modulate host responses and infection outcome during SARS-CoV-2 infection. ImportanceDefective viral genomes (DVGs) are ubiquitously generated in many RNA viruses, including SARS-CoV-2. Their interference activity to full-length viruses and IFN stimulation provide them the potential for novel antiviral therapies and vaccine development. SARS-CoV-2 DVGs are generated through the recombination of two discontinuous genomic fragments by viral polymerase complex and the recombination is also one of the major mechanisms for the emergence of new coronaviruses. Focusing on the generation and function of SARS-CoV-2 DVGs, these studies identify new hotspots for non-homologous recombination and strongly suggest that the secondary structures within viral genomes mediate the recombination. Furthermore, these studies provide the first evidence for IFN stimulation activity of de novo DVGs during natural SARS-CoV-2 infection. These findings set up the foundation for further mechanism studies of SARS-CoV-2 recombination and provide the evidence to harness DVGs immunostimulatory potential in the development of vaccine and antivirals for SARS-CoV-2.

microbiology↗

bioRxiv2021

RNA Helix Thermodynamics: The End Game

Nearest neighbor parameters for estimating the folding stability of RNA secondary structures are in widespread use. For helices, current parameters penalize terminal AU base pairs relative to terminal GC base pairs. We curated an expanded database of helix stabilities determined by optical melting experiments. Analysis of the updated database shows that terminal penalties depend on the sequence identity of the adjacent penultimate base pair. New nearest neighbor parameters that include this additional sequence dependence accurately predict the measured values of 271 helices in an updated database with a correlation coefficient of 0.982. This refined understanding of helix ends facilitates fitting terms for base pair stacks with GU pairs. Prior parameter sets treated [Formula] separately from other [Formula] stacks. The improved understanding of helix end stability, however, makes the separate treatment unnecessary. Introduction of the additional terms was tested with three optical melting experiments. The average absolute difference between measured and predicted free energy changes at 37{degrees} C for these three duplexes containing terminal adjacent AU and GU pairs improved from 1.38 to 0.27 kcal/mol. This confirms the need for the additional sequence dependence in the model.

biophysics↗

bioRxiv2021

Pre-mRNA Splicing Factor U2AF2 Recognizes Distinct Conformations of Nucleotide Variants at the Center of the pre-mRNA Splice Site Signal

The essential pre-mRNA splicing factor U2AF2 (also called U2AF65) identifies polypyrimidine (Py) tract signals of nascent transcripts, despite length and sequence variations. Previous studies have shown that the U2AF2 RNA recognition motifs (RRM1 and RRM2) preferentially bind uridine-rich RNAs. Nonetheless, the specificity of the RRM1/RRM2 interface for the central Py tract nucleotide has yet to be investigated. We addressed this question by determining crystal structures of U2AF2 bound to a cytidine, guanosine, or adenosine at the central position of the Py tract, and compared U2AF2-bound uridine structures. Local movements of the RNA site accommodated the different nucleotides, whereas the polypeptide backbone remained similar among the structures. Accordingly, molecular dynamics simulations revealed flexible conformations of the central, U2AF2-bound nucleotide. The RNA binding affinities and splicing efficiencies of structure-guided mutants demonstrated that U2AF2 tolerates nucleotide substitutions at the central position of the Py tract. Moreover, enhanced crosslinking and immunoprecipitation of endogenous U2AF2 in human erythroleukemia cells showed uridine-sensitive binding sites with lower sequence conservation at the central nucleotide positions of otherwise uridine-rich, U2AF2-bound splice sites. Altogether, these results highlight the importance of RNA flexibility for protein recognition and take a step towards relating splice site motifs to pre-mRNA splicing efficiencies.

biophysics↗

bioRxiv2021

Secondary Structure Prediction for RNA Sequences Including N6-methyladenosine

There is increasing interest in the roles played by covalently modified nucleotides in mRNAs and non-coding RNAs. New high-throughput sequencing technologies localize these modifications to exact nucleotide positions. There has been, however, and inability to account for these modifications in secondary structure prediction because of a lack of software tools for handling modifications and a lack of thermodynamic parameters for modifications. Here, we report that we solved these issues for N6-methyladenosine (m6A), for the first time allowing secondary structure prediction for a nucleotide alphabet of A, C, G, U, and m6A. We revised the RNAstructure software package to work with any user-defined alphabet of nucleotides. We also developed a set of nearest neighbor parameters for helices and loops containing m6A, using a set of 45 optical melting experiments. Interestingly, N6-methylation decreases the folding stability of structures with adenosines in the middle of a helix, has little effect on the folding stability of adenosines at the ends of helices, and stabilizes the folding stability for structures with unpaired adenosines stacked on the end of a helix. The parameters were tested against an additional two melting experiments, including a consensus sequence for methylation and an m6A dangling end. The utility of the new software was tested using predictions of the structure of a molecular switch in the MALAT1 lncRNA, for which a conformation change is triggered by methylation. Additionally, human transcriptome-wide calculations for the effect of N6-methylation on the probability of an adenosine being buried in a helix compare favorably with PARS structure mapping data. Now users of RNAstructure are able to develop hypothesis for structure-function relationships for RNAs with m6A, including conformational switching triggered by methylation.

bioinformatics↗

bioRxiv2020

LinearTurboFold: Linear-Time RNA Structural Alignment and Conserved Structure Prediction with Applications to Coronaviruses

The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in SARS-CoV-2 genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length, and are thus infeasible for coronaviruses, which possess the longest genomes ([~]30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurbo-Folds purely in silico prediction not only is close to experimentally-guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5 and 3 UTRs ([~]29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies novel conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, siRNAs, CRISPR-Cas13 guide RNAs and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies, and will be a useful tool in fighting the current and future pandemics. Significance StatementConserved RNA structures are critical for designing diagnostic and therapeutic tools for many diseases including COVID-19. However, existing algorithms are much too slow to model the global structures of full-length RNA viral genomes. We present LinearTurboFold, a linear-time algorithm that is orders of magnitude faster, making it the first method to simultaneously fold and align whole genomes of SARS-CoV-2 variants, the longest known RNA virus ([~]30 kilobases). Our work enables unprecedented global structural analysis and captures long-range interactions that are out of reach for existing algorithms but crucial for RNA functions. LinearTurboFold is a general technique for full-length genome studies and can help fight the current and future pandemics.

bioinformatics↗

bioRxiv2018

The formation of intramolecular secondary structure brings mRNA ends in close proximity

A number of protein factors regulate protein synthesis by bridging mRNA ends or untranslated regions (UTRs). Using experimental and computational approaches, we show that mRNAs from various organisms, including humans, have an intrinsic propensity to fold into structures in which the 5 end and 3 end are [≤] 7 nm apart irrespective of mRNA length. Computational estimates performed for [~]22,000 human transcripts indicate that the inherent proximity of the ends is a universal property of most, if not all, mRNA sequences. Only specific RNA sequences, which have low sequence complexity and are devoid of guanosines, are unstructured and exhibit end-to-end distances expected for the random coil conformation of RNA. Our results suggest that the intrinsic proximity of mRNA ends may facilitate binding of translation factors that bridge mRNA 5 and 3 UTRs. Furthermore, our studies provide the basis for measuring, computing and manipulating end-to-end distances and secondary structure in mRNAs in research and biotechnology.

biochemistry↗