Biology subjects

Kierzek, E.

Publications and source records attributed to Kierzek, E..

8 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

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

Pan-cancer discovery of driver mutations in long noncoding RNAs reveals widespread functional rewiring of RNA regulatory elements

Most somatic mutations in cancer occur outside protein-coding genes, yet the functional impact of these mutations remains largely unknown. Long noncoding RNAs (lncRNAs) represent a major class of cancer-promoting genes whose molecular mechanisms are poorly understood. While individual driver mutations in lncRNAs have been identified, detecting such driver lncRNAs at scale requires large tumour genome cohorts. We analyse 12,631 cancer genomes from the 100,000 Genomes Project (100kGP) and identify 121 lncRNAs under positive selection across 19 cancer types. These driver lncRNAs are independently supported by functional genomic screens, germline predisposing variants, mutual exclusivity with protein-coding drivers, and independent oncogenic lncRNA catalogues. Overall, approximately two-thirds of analysed tumours harbour at least one lncRNA driver mutation. Leveraging the depth of this dataset, we demonstrate that somatic mutations preferentially target and remodel RNA-binding protein (RBP) interaction sites to potentiate oncogenic lncRNAs, including MALAT1, SNHG14 and NEAT1. From these data, we derive a model in which somatic mutations liberate oncogenic lncRNAs from repressive RNA:protein interactions. This work expands the number and nature of cancer driver genes, identifies targets for RNA-directed therapies, and demonstrates that with large tumour mutation catalogues we can dissect the molecular mechanisms of noncoding genes.

Cancer Biology↗

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↗

bioRxiv2024

Accurate in silico predictions of modified RNA interactions to a prototypical RNA-binding protein with {lambda}-dynamics

RNA-binding proteins shape biology through their widespread functions in RNA biochemistry. Their function requires the recognition of specific RNA motifs for targeted binding. These RNA binding elements can be composed of both unmodified and chemically modified RNAs, of which over 170 chemical modifications have been identified in biology. Unmodified RNA sequence preferences for RNA-binding proteins have been widely studied, with numerous methods available to identify their preferred sequence motifs. However, only a few techniques can detect preferred RNA modifications, and no current method can comprehensively screen the vast array of hundreds of natural RNA modifications. Prior work demonstrated that {lambda}-dynamics is an accurate in silico method to predict RNA base binding preferences of an RNA-binding antibody. This work extends that effort by using {lambda}-dynamics to predict unmodified and modified RNA binding preferences of human Pumilio, a prototypical RNA binding protein. A library of RNA modifications was screened at eight nucleotide positions along the RNA to identify modifications predicted to affect Pumilio binding. Computed binding affinities were compared with experimental data to reveal high predictive accuracy. In silico force field accuracies were also evaluated between CHARMM and Amber RNA force fields to determine the best parameter set to use in binding calculations. This work demonstrates that {lambda}-dynamics can predict RNA interactions to a bona fide RNA-binding protein without the requirements of chemical reagents or new methods to experimentally test binding at the bench. Advancing in silico methods like {lambda}-dynamics will unlock new frontiers in understanding how RNA modifications shape RNA biochemistry.

biochemistry↗

bioRxiv2021

Secondary structure of subgenomic RNA M of SARS-CoV-2

SARS-CoV-2 belongs the Coronavirinae family. As other coronaviruses, SARS-CoV-2 is enveloped and possesses positive-sense, single-stranded RNA genome of [~] 30 kb. Genome RNA is used as the template for replication and transcription. During these processes, positive-sense genomic RNA (gRNA) and subgenomic RNAs (sgRNAs) are created. Several studies showed importance of genomic RNA secondary structure in SARS-CoV-2 replication. However, the structure of sgRNAs have remained largely unsolved so far. In this study, we performed probing of sgRNA M of SARS-CoV-2 in vitro. This is the first experimentally informed secondary structure model of sgRNA M, which presents features likely to be important in sgRNA M function. The knowledge about sgRNA M provides insights to better understand virus biology and could be used for designing new therapeutics.

molecular biology↗

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↗