Ribocentre: a database of ribozymes

An ontology-based knowledge graph for representing interactions involving RNA molecules

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

Direct testing of natural twister ribozymes from over a thousand organisms reveals a broad tolerance for structural imperfections

Twister ribozymes are an extensively studied class of nucleolytic RNAs. Thousands of natural twisters have been proposed using sequence homology and structural descriptors. Yet, most of these candidates have not been validated experimentally. To address this gap, we developed CHiTA (Cleavage High-Throughput Assay), a high-throughput pipeline utilizing massively parallel oligonucleotide synthesis and next-generation sequencing to test putative ribozymes en masse in a scarless fashion. As proof of principle, we applied CHiTA to a small set of known active and mutant ribozymes. We then used CHiTA to test two large sets of naturally occurring twister ribozymes: over 1, 600 previously reported putative twisters and ∼1, 000 new candidate twisters. The new candidates were identified computationally in ∼1, 000 organisms, representing a massive increase in the number of ribozyme-harboring organisms. Approximately 94% of the twisters we tested were active and cleaved site-specifically. Analysis of their structural features revealed that many substitutions and helical imperfections can be tolerated. We repeated our computational search with structural descriptors updated from this analysis, whereupon we identified and confirmed the first intrinsically active twister ribozyme in mammals. CHiTA broadly expands the number of active twister ribozymes found in nature and provides a powerful method for functional analyses of other RNAs.

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FURNA: A database for functional annotations of RNA structures

Despite the increasing number of 3D RNA structures in the Protein Data Bank, the majority of experimental RNA structures lack thorough functional annotations. As the significance of the functional roles played by noncoding RNAs becomes increasingly apparent, comprehensive annotation of RNA function is becoming a pressing concern. In response to this need, we have developed FURNA (Functions of RNAs), the first database for experimental RNA structures that aims to provide a comprehensive repository of high-quality functional annotations. These include Gene Ontology terms, Enzyme Commission numbers, ligand-binding sites, RNA families, protein-binding motifs, and cross-references to related databases. FURNA is available at https://seq2fun.dcmb.med.umich.edu/furna/ to enable quick discovery of RNA functions from their structures and sequences.

Chemically Cross-Linked Hammerhead Ribozyme as an Efficient RNA Interference Tool

RNA-cleaving ribozymes are promising candidates as general tools of RNA interference (RNAi) in gene manipulation. However, compared with other RNA systems, such as siRNA and CRISPR technologies, the ribozyme tools are still far from broad applications on RNAi due to their poor performance in the cellular context. In this work, we report an efficient RNAi tool based on chemically modified hammerhead ribozyme (HHR). By the introduction of an intramolecular linkage into the minimal HHR to reconstruct the distal interaction within the tertiary ribozyme structure, this cross-linked HHR exhibits efficient RNA substrate cleavage activities with almost no sequence constraint. Cellular experiments suggest that both exogenous and endogenous RNA expression can be dramatically knocked down by this HHR tool with levels comparable to those of siRNA. Unlike the widely applied protein-recruiting RNA systems (siRNA and CRISPR), this ribozyme tool functions solely on RNA itself with great simplicity, which may provide a new approach for gene manipulation in both fundamental and translational studies.

Surface-Accelerated String Method for Locating Minimum Free Energy Paths

We present a surface-accelerated string method (SASM) to efficiently optimize low-dimensional reaction pathways from the sampling performed with expensive quantum mechanical/molecular mechanical (QM/MM) Hamiltonians. The SASM accelerates the convergence of the path using the aggregate sampling obtained from the current and previous string iterations, whereas approaches like the string method in collective variables (SMCV) or the modified string method in collective variables (MSMCV) update the path only from the sampling obtained from the current iteration. Furthermore, the SASM decouples the number of images used to perform sampling from the number of synthetic images used to represent the path. The path is optimized on the current best estimate of the free energy surface obtained from all available sampling, and the proposed set of new simulations is not restricted to being located along the optimized path. Instead, the umbrella potential placement is chosen to extend the range of the free energy surface and improve the quality of the free energy estimates near the path. In this manner, the SASM is shown to improve the exploration for a minimum free energy pathway in regions where the free energy surface is relatively flat. Furthermore, it improves the quality of the free energy profile when the string is discretized with too few images. We compare the SASM, SMCV, and MSMCV using 3 QM/MM applications: a ribozyme methyltransferase reaction using 2 reaction coordinates, the 2'-O-transphosphorylation reaction of Hammerhead ribozyme using 3 reaction coordinates, and a tautomeric reaction in B-DNA using 5 reaction coordinates. We show that SASM converges the paths using roughly 3 times less sampling than the SMCV and MSMCV methods. All three algorithms have been implemented in the FE-ToolKit package made freely available.

Enzyme Databases in the Era of Omics and Artificial Intelligence

Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.

Unlocking the potential of RNA-based therapeutics in the lung: current status and future directions

Awareness of RNA-based therapies has increased after the widespread adoption of mRNA vaccines against SARS-CoV-2 during the COVID-19 pandemic. These mRNA vaccines had a significant impact on reducing lung disease and mortality. They highlighted the potential for rapid development of RNA-based therapies and advances in nanoparticle delivery systems. Along with the rapid advancement in RNA biology, including the description of noncoding RNAs as major products of the genome, this success presents an opportunity to highlight the potential of RNA as a therapeutic modality. Here, we review the expanding compendium of RNA-based therapies, their mechanisms of action and examples of application in the lung. The airways provide a convenient conduit for drug delivery to the lungs with decreased systemic exposure. This review will also describe other delivery methods, including local delivery to the pleura and delivery vehicles that can target the lung after systemic administration, each providing access options that are advantageous for a specific application. We present clinical trials of RNA-based therapy in lung disease and potential areas for future directions. This review aims to provide an overview that will bring together researchers and clinicians to advance this burgeoning field.

G·U base pairing motifs in long non-coding RNAs

Long non-coding RNAs (lncRNAs) are recently-discovered transcripts involved in gene expression regulation and associated with diseases. Despite the unprecedented molecular complexity of these transcripts, recent studies of the secondary and tertiary structure of lncRNAs are starting to reveal the principles of lncRNA structural organization, with important functional implications. It therefore starts to be possible to analyze lncRNA structures systematically. Here, using a set of prototypical and medically-relevant lncRNAs of known secondary structure, we specifically catalogue the distribution and structural environment of one of the first-identified and most frequently occurring non-canonical Watson-Crick interactions, the G·U base pair. We compare the properties of G·U base pairs in our set of lncRNAs to those of the G·U base pairs in other well-characterized transcripts, like rRNAs, tRNAs, ribozymes, and riboswitches. Furthermore, we discuss how G·U base pairs in these targets participate in establishing interactions with proteins or miRNAs, and how they enable lncRNA tertiary folding by forming intramolecular or metal-ion interactions. Finally, by identifying highly-G·U-enriched regions of yet unknown function in our target lncRNAs, we provide a new rationale for future experimental investigation of these motifs, which will help obtain a more comprehensive understanding of lncRNA functions and molecular mechanisms in the future.

The Moon-Forming Impact and the Autotrophic Origin of Life

The Moon-forming impact vaporized part of Earth's mantle, and turned the rest into a magma ocean, from which carbon dioxide degassed into the atmosphere, where it stayed until water rained out to form the oceans. The rain dissolved CO2 and made it available to react with transition metal catalysts in the Earth's crust so as to ultimately generate the organic compounds that form the backbone of microbial metabolism. The Moon-forming impact was key in building a planet with the capacity to generate life in that it converted carbon on Earth into a homogeneous and accessible substrate for organic synthesis. Today all ecosystems, without exception, depend upon primary producers, organisms that fix CO2 . According to theories of autotrophic origin, it has always been that way, because autotrophic theories posit that the first forms of life generated all the molecules needed to build a cell from CO2 , forging a direct line of continuity between Earth's initial CO2 -rich atmosphere and the first microorganisms. By modern accounts these were chemolithoautotrophic archaea and bacteria that initially colonized the crust and still inhabit that environment today.

Elucidating the Self-cleavage Dynamics of Hairpin Ribozyme by Mode-decomposed Infrared Spectroscopy

While catalytic reactions of biomolecular processes play an indispensable role in life, extracting the underlying molecular picture often remains challenging. Based on ab initio simulations of the self-cleavage reaction of hairpin ribozyme, mode-decomposed infrared spectra, and cosine similarity analysis to correlate the product with reactant IR spectra, we demonstrate a strategy to extract molecular details from characteristic spectral changes. Our results are in almost quantitative agreement with the experimental IR band library of nucleic acids and suggest that the spectral range of 800-1200 cm-1 is particularly valuable to monitor self-cleavage. Importantly, the cosine similarities also disclose that IR peaks subject to slight shifts due to self-cleavage might be unrelated, while strongly shifting resonances can correspond to the same structural dynamics. This framework of correlating complex IR spectra at the molecular level along biocatalytic reaction pathways is broadly applicable.

Group I Intron as a Potential Target for Antifungal Compounds: Development of a Trans-Splicing High-Throughput Screening Strategy

The search for safe and efficient new antifungal compounds for agriculture has led to more efforts in finding new modes of action. This involves the discovery of new molecular targets, including coding and non-coding RNA. Rarely found in plants and animals but present in fungi, group I introns are of interest as their complex tertiary structure may allow selective targeting using small molecules. In this work, we demonstrate that group I introns present in phytopathogenic fungi have a self-splicing activity in vitro that can be adapted in a high-throughput screening to find new antifungal compounds. Ten candidate introns from different filamentous fungi were tested and one group ID intron found in F. oxysporum showed high self-splicing efficiency in vitro. We designed the Fusarium intron to act as a trans-acting ribozyme and used a fluorescence-based reporter system to monitor its real time splicing activity. Together, these results are opening the way to study the druggability of such introns in crop pathogen and potentially discover small molecules selectively targeting group I introns in future high-throughput screenings.