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1. Detection and Quantification of RNA Phosphorothioate Modifications Using Mass Spectrometry

   https://doi.org/10.1002/cpnc.113  

   Wu, Ying; Zheng, Ya Ying; Lin, Qishan; Sheng, Jia (August 2020, Current Protocols in Nucleic Acid Chemistry)

   Abstract This article describes a protocol for detecting and quantifying RNA phosphorothioate modifications in cellular RNA samples. Starting from solid‐phase synthesis of phosphorothioate RNA dinucleotides, followed by purification with reversed‐phase HPLC, phosphorothioate RNA dinucleotide standards are prepared for UPLC‐MS and LC‐MS/MS methods. RNA samples are extracted from cells using TRIzol reagent, then digested with a nuclease mixture and analyzed by mass spectrometry. UPLC‐MS is employed first to identify RNA phosphorothioate modifications. An optimized LC‐MS/MS method is then employed to quantify the frequency of RNA phosphorothioate modifications in a series of model cells. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Synthesis, purification, and characterization of RNA phosphorothioate dinucleotides Basic Protocol 2: Digestion of RNA samples extracted from cells Basic Protocol 3: Detection and quantification of RNA phosphorothioate modifications by mass spectrometry 

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2. Partner‐specific prediction of RNA‐binding residues in proteins: A critical assessment

   https://doi.org/10.1002/prot.25639  

   Jung, Yong; EL‐Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant G. (December 2018, Proteins: Structure, Function, and Bioinformatics)

   Abstract RNA‐protein interactions play essential roles in regulating gene expression. While some RNA‐protein interactions are “specific”, that is, the RNA‐binding proteins preferentially bind to particular RNA sequence or structural motifs, others are “non‐RNA specific.” Deciphering the protein‐RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein‐RNA interfaces, there is a need for computational methods to identify RNA‐binding residues in proteins. While most of the existing computational methods for predicting RNA‐binding residues in RNA‐binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner‐specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner‐specific protein‐RNA interface prediction tools, PS‐PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA‐specificity metric (RSM), for quantifying the RNA‐specificity of the RNA binding residues predicted by such tools. Our results show that the RNA‐binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner‐agnostic metrics, RNA partner‐specific methods are outperformed by the state‐of‐the‐art partner‐agnostic methods. We conjecture that either (a) the protein‐RNA complexes in PDB are not representative of the protein‐RNA interactions in nature, or (b) the current methods for partner‐specific prediction of RNA‐binding residues in proteins fail to account for the differences in RNA partner‐specific versus partner‐agnostic protein‐RNA interactions, or both. 

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3. RNA language models predict mutations that improve RNA function

   https://doi.org/10.1038/s41467-024-54812-y  

   Shulgina, Yekaterina; Trinidad, Marena_I; Langeberg, Conner_J; Nisonoff, Hunter; Chithrananda, Seyone; Skopintsev, Petr; Nissley, Amos_J; Patel, Jaymin; Boger, Ron_S; Shi, Honglue; et al (December 2024, Nature Communications)

   Abstract Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to theEscherichia coliribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function. 

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4. The Naturally Occurring m1A RNA Modification Can Be Efficiently Incorporated into RNA by SARS-CoV-2 RdRp

   https://doi.org/10.21203/rs.3.rs-3615885/v1  

   Apostle, Alexander; Arneson, Reed; Yuan, Yinan; Fang, Shiyue (November 2023, Research Square)

   Abstract When it is in the template RNA, the naturally occurring m¹A epitranscriptomic RNA modification was recently reported to be able to stop the RNA polymerization reaction catalyzed by the RNA dependent RNA polymerase (RdRp) of SARS-CoV-2. In this report, we report that m¹A via its triphosphate form (m¹ATP) can be incorporated into RNA by the same RdRp. These two findings point a new direction for antiviral drug development based on m¹A for combatting COVID-19. More broadly, it is possible that the large pool of epigenetic RNA as well as DNA modifications could serve as a treasury for drug discovery aimed at combating various infectious and other diseases. 

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5. Composition and RNA binding specificity of metazoan RNase MRP

   https://doi.org/10.1093/nar/gkaf829  

   Liu, Yuan; He, Shiyang; Pyo, Kawon; Zheng, Shanshan; Chen, Meijuan; Braschi, Bryony; Cheloufi, Sihem; Slavov, Nikolai; Marzluff, William F.; Murn, Jernej (August 2025, Nucleic Acids Research)

   Abstract Ribonuclease (RNase) MRP is a conserved RNA-based enzyme best known for its essential role in the maturation of ribosomal RNA (rRNA) in eukaryotes. However, the composition and RNA substrate specificity of this multisubunit ribonucleoprotein complex in higher eukaryotes remain a mystery. Here, we identify NEPRO and C18ORF21 (which we renamed RMP64 and RMP24, respectively) as constitutive subunits of metazoan RNase MRP. These proteins are unique to RNase MRP and absent from the closely related RNase P, which processes transfer RNA (tRNA) precursors and tRNA-like substrates. We find that RMP64 and RMP24 are integral subunits of RNase MRP, stabilize its catalytic RNA, and are required for rRNA maturation and cell proliferation. Leveraging these discoveries, we identify a broad suite of in vivo RNA-binding targets of each enzyme, including potential cleavage sites at nucleotide resolution. Our findings identify the first metazoan RNase MRP-specific protein subunits and define the RNA-targeting repertoire of this essential enzyme in mammalian cells. 

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