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Dimethyl sulfate (DMS) chemical mapping probes RNA structure, where low reactivity is generally interpreted as Watson-Crick (WC) base pairs and high reactivity as unpaired nucleotides. Studies examining DMS reactivity of RNAs with known 3D structures have identified nucleotides that deviate from this interpretation with distinct solvent accessibility and hydrogen bonding patterns. Understanding the frequency of these outliers and their recurring structural 3D features remains incomplete. To address this knowledge gap, we systematically analyzed DMS reactivity patterns across a library of 7,500 RNA constructs containing two-way junctions with known 3D structures. We observe DMS reactivity exists on a continuum over four orders of magnitude with approximately 10% overlap in reactivity between WC and non-WC nucleotides. We find that non-WC bases with WC-like DMS protection exhibit increased hydrogen bonding and decreased solvent accessibility, whereas WC pairs exhibiting greater DMS reactivity tend to flank junctions, correlating with weaker base stacking and greater junction dynamics. Furthermore, we discover that DMS reactivity values in non-canonical pairs correlate with atomic distances and base pair geometry, enabling discrimination between different 3D conformations. These DMS reactivity patterns indicate that DMS reactivity provides atomic-scale information about RNA 3D conformations, which can be used to model RNA structures and dynamics.

Metabolomics commonly relies on using one-dimensional (1D) 1 H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS) to derive scientific insights from large collections of biological samples. NMR and MS approaches to metabolomics require, among other issues, a data processing pipeline. Quantitative assessment of the performance of these software platforms is challenged by a lack of standardized data sets with "known" outcomes. To resolve this issue, we created a novel simulated LC-MS data set with known peak locations and intensities, defined metabolite differences between groups (i.e., fold change > 2, coefficient of variation ≤ 25%), and different amounts of added Gaussian noise (0, 5, or 10%) and missing features (0, 10, or 20%). This data set was developed to improve benchmarking of existing LC-MS metabolomics software and to validate the updated version of our MVAPACK software, which added gas chromatography-MS and LC-MS functionality to its existing 1D and two-dimensional NMR data processing capabilities. We also included two experimental LC-MS data sets acquired from a standard mixture and Mycobacterium smegmatis cell lysates since a simulated data set alone may not capture all the unique characteristics and variability of real spectra needed to assess software performance properly. Our simulated and experimental LC-MS data sets were processed with the MS-DIAL and XCMSOnline software packages and our MVAPACK toolkit to showcase the utility of our data sets to benchmark MVAPACK against community standards. Our results demonstrate the enhanced objectivity and clarity of software assessment that can be achieved when both simulated and experimental data are employed since distinctly different software performances were observed with the simulated and experimental LC-MS data sets. We also demonstrate that the performance of MVAPACK is equivalent to or exceeds existing LC-MS software programs while providing a single platform for processing and analyzing both NMR and MS data sets.

Despite growing interest in applying RNA's unique structural characteristics to solve diverse biotechnology and nanotechnology problems, there are few computational tools for targeted tertiary design. As a result, RNA 3D design is traditionally slow, resource-consuming, and dependent on expert modeling. In this chapter, we discuss our recently developed software package: RNAMake, a set of applications capable of designing RNA tertiary structures to solve various relevant nanotechnology problems and provide basic thermodynamic calculations for the generated designs. We provide in-depth examples and instructions for designing example RNA nanostructures such as minimal RNA sequences containing a single tertiary contact, generating RNAs that stabilize small-molecule ligands, and building tethers that link ribosomal subunits together. We also highlight the addition of a new Monte Carlo design algorithm and the ability to estimate the thermodynamic contribution of helical elements in RNA 3D structures., (© 2023. Springer Science+Business Media, LLC, part of Springer Nature.)

RNA structures play critical roles in regulating gene expression across all domains of life and viruses. Chemical probing methods coupled with massively parallel sequencing have revolutionized the RNA structure field by enabling the assessment of many structures in their native, physiological context. Previously, we developed Dimethyl-Sulfate-based Mutational Profiling and Sequencing (DMS-MaPseq), which uses DMS to label the Watson-Crick face of open and accessible adenine and cytosine bases in the RNA. We used this approach to determine the genome-wide structures of HIV-1 and SARS-CoV-2 in infected cells, which permitted uncovering new biology and identifying therapeutic targets. Due to the simplicity and ease of the experimental procedure, DMS-MaPseq has been adopted by labs worldwide. However, bioinformatic analysis remains a substantial hurdle for labs that often lack the necessary infrastructure and computational expertise. Here we present a modern web-based interface that automates the analysis of chemical probing profiles from raw sequencing files (http://rnadreem.org). The availability of a simple web-based platform for DMS-MaPseq analysis will dramatically expand studies of RNA structure and aid in the design of structure-based therapeutics., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Biological systems demonstrate exquisite three dimensional (3D) control over crystal nucleation and growth using soft micro/nanoenvironments, such as vesicles, for reagent transport and confinement. It remains challenging to mimic such biomineralization processes using synthetic systems. A synthetic mineralization strategy applicable to the synthesis of artificial magnetosomes with programmable magnetic domains is described. This strategy relies on the compartmentalization of precursors in surfactant-stabilized liquid microdroplets which, when contacted, spontaneously form lipid bilayers that support reagent transport and interface-confined magnetite nucleation and growth. The resulting magnetic domains are polarized and thus readily manipulated using magnetic fields or assembled using droplet-droplet interactions. This strategy presents a new, liquid phase procedure for the synthesis of vesicles with geometrically controlled inorganic features that would be difficult to produce otherwise. The artificial magnetosomes demonstrated could find use in, for example, drug/cargo delivery, droplet microfluidics, and formulation science.