Biology subjects

Metkar, M.

Publications and source records attributed to Metkar, M..

6 recordsLinked to original sources

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

mRNAfold: Co-optimization of Global Stability, Local Structure, and Codon Choice via Suboptimal Folding

mRNA medicines hold great promise, but designing sequences with high translation efficiency, robust in-solution stability, and manufacturability remains a major challenge due to the vast combinatorial space of synonymous coding sequences. Computational approaches such as mRNA folding algorithms have emerged as powerful tools by co-optimizing for in-solution stability and translation efficiency, yet current methods face important limitations. Here, we present "mRNAfold", an improved mRNA folding algorithm and software package that addresses these gaps by enabling efficient exploration of diverse near-optimal solutions, incorporating untranslated regions (UTRs), parallel execution, and supporting tunable control over local structural features across the mRNA. Thermodynamically optimized mRNAs from mRNAfold were more stable ({approx} 2-fold) in-solution than those generated by simple GC maximization for the same encoded protein. In addition, mRNAs designed to vary local structure near the start codon while maintaining consistent structure and codon optimality elsewhere showed a complex relationship between local structure near the start codon and protein production in cells. We observed no impact of structure in the start codon region for a set of mRNAs with high codon optimality, but it did impact protein production for a set of mRNAs with lower codon optimality. Together, these results underscore the potential of structure-aware, multi-objective design to improve mRNA medicines and offer a framework for exploring how sequence, structure, and expression are interrelated.

bioinformatics↗

bioRxiv2025

Fine scale structural information substantially improves multivariate regression model for mRNA in-vial degradation prediction

The success of COVID-19 mRNA vaccines has made the in-solution stability optimization of mRNAs a key objective. However, we still lack a complete understanding of sequence metrics that influence mRNA in-solution stability. RNA secondary structure plays a critical role in protecting against hydrolysis, the primary degradation pathway under storage conditions. Yet, the structural metrics that best guide stability-focused mRNA design remain incompletely defined. Global metrics like minimum free energy and average unpaired probability have improved mRNA stability but fail to capture local structural variation relevant to RNA degradation. We demonstrate that base-pairing log odds provide fine-scale, orthogonal insight that complements global metrics and improves stability modeling. Further, by combining local and global features into a parsimonious four-feature regression model, dubbed Stability Regression Analysis using Nucleotide-Derived features (STRAND), we achieve a greater than two-fold reduction in prediction error compared to existing machine learning and deep learning approaches and demonstrate robust generalization across diverse transcript contexts. This compact and interpretable model provides an accurate and reliable framework for predicting mRNAs in-solution stability.

bioinformatics↗

bioRxiv2020

SARS-CoV-2 mRNA Vaccine Development Enabled by Prototype Pathogen Preparedness

A SARS-CoV-2 vaccine is needed to control the global COVID-19 public health crisis. Atomic-level structures directed the application of prefusion-stabilizing mutations that improved expression and immunogenicity of betacoronavirus spike proteins. Using this established immunogen design, the release of SARS-CoV-2 sequences triggered immediate rapid manufacturing of an mRNA vaccine expressing the prefusion-stabilized SARS-CoV-2 spike trimer (mRNA-1273). Here, we show that mRNA-1273 induces both potent neutralizing antibody and CD8 T cell responses and protects against SARS-CoV-2 infection in lungs and noses of mice without evidence of immunopathology. mRNA-1273 is currently in a Phase 2 clinical trial with a trajectory towards Phase 3 efficacy evaluation.

immunology↗

bioRxiv2019

Deep sequencing of pre-translational mRNPs reveals hidden flux through evolutionarily conserved AS-NMD pathways

BackgroundAlternative splicing (AS), which generates multiple mRNA isoforms from single genes, is crucial for the regulation of eukaryotic gene expression. The flux through competing AS pathways cannot be determined by traditional RNA-Seq, however, because different mRNA isoforms can have widely differing decay rates. Indeed, some mRNA isoforms with extremely short half-lives, such as those subject to translation-dependent nonsense-mediated decay (AS-NMD), may be completely overlooked in even the most extensive RNA-Seq analyses. ResultsRNA immunoprecipitation in tandem (RIPiT) of exon junction complex (EJC) components allows for purification of post-splicing mRNA-protein particles (mRNPs) not yet subject to translation (pre-translational mRNPs) and, therefore, translation-dependent mRNA decay. Here we compared EJC RIPiT-Seq to whole cell RNA-Seq data from HEK293 cells. Consistent with expectation, the flux through known AS-NMD pathways is substantially higher than that captured by RNA-Seq. Our EJC RIPiT-Seq also definitively demonstrates that the splicing machinery itself has no ability to detect reading frame. We identified thousands of previously unannotated splicing events; while many can be attributed to "splicing noise", others are evolutionarily-conserved events that produce new AS-NMD isoforms likely involved in maintenance of protein homeostasis. Several of these occur in genes whose overexpression has been linked to poor cancer prognosis. ConclusionsDeep sequencing of RNAs in post-splicing, pre-translational mRNPs provides a means to identify and quantify splicing events without the confounding influence of differential mRNA decay. For many known AS-NMD targets, the NMD-linked AS pathway predominates. EJC RIPiT-Seq also enabled identification of numerous conserved but previously unannotated AS-NMD events.

bioinformatics↗

bioRxiv2018

Higher-Order Organization Principles of Pre-translational mRNPs

Compared to noncoding RNAs (ncRNAs) such as rRNAs and ribozymes, for which high resolution structures abound, little is known about the tertiary structures of mRNAs. In eukaryotic cells, newly made mRNAs are packaged with proteins in highly compacted mRNPs, but the manner of this mRNA compaction is unknown. Here we developed and implemented RIPPLiT (RNA ImmunoPrecipitation and Proximity Ligation in Tandem), a transcriptome-wide method for probing the 3D conformations of RNAs stably-associated with defined proteins, in this case exon junction complex (EJC) core factors. EJCs multimerize with other mRNP components to form megadalton sized complexes that protect large swaths of newly synthesized mRNAs from endonuclease digestion. Unlike ncRNAs, mRNAs behave more like flexible polymers without strong locus-specific interactions. Polymer analysis of proximity ligation data for hundreds of mRNA species demonstrates that pre-translational mammalian mRNPs fold as linear rod-like structures with no strong propensity for 5 and 3 end interaction.

molecular biology↗