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Long non-coding RNAs are a very versatile class of molecules that can have important roles in regulating a cells function, including regulating other genes on the transcriptional level. One of these mechanisms is that RNA can directly interact with DNA thereby recruiting additional components such as proteins to these sites via an RNA:dsDNA triplex formation. We genetically deleted the triplex forming sequence (FendrrBox) from the lncRNA Fendrr in mice and found that this FendrrBox is partially required for Fendrr function in vivo. We found that the loss of the triplex forming site in developing lungs causes a dysregulation of gene programs associated with lung fibrosis. A set of these genes contain a triplex site directly at their promoter and are expressed in lung fibroblasts. We biophysically confirmed the formation of an RNA:dsDNA triplex with target promoters in vitro. We found that Fendrr with the Wnt signalling pathway regulates these genes, implicating that Fendrr synergizes with Wnt signalling in lung fibrosis.

RNA.DNA:DNA triple helix (triplex) formation is a form of RNA–DNA interaction which regulates gene expression but is difficult to study experimentally in vivo. This makes accurate computational prediction of such interactions highly important in the field of RNA research. Current predictive methods use canonical Hoogsteen base pairing rules, which whilst biophysically valid, may not reflect the plastic nature of cell biology. Here, we present the first optimization approach to learn a probabilistic model describing RNA–DNA interactions directly from motifs derived from triplex sequencing data. We find that there are several stable interaction codes, including Hoogsteen base pairing and novel RNA–DNA base pairings, which agree with in vitro measurements. We implemented these findings in TriplexAligner, a program that uses the determined interaction codes to predict triplex binding. TriplexAligner predicts RNA–DNA interactions identified in all-to-all sequencing data more accurately than all previously published tools in human and mouse and also predicts previously studied triplex interactions with known regulatory functions. We further validated a novel triplex interaction using biophysical experiments. Our work is an important step towards better understanding of triplex formation and allows genome-wide analyses of RNA–DNA interactions.

Photocleavable protecting groups (PPGs) play a pivotal role in numerous studies. They enable controlled release of small effector molecules to induce biochemical function. The number of PPGs attached to a variety of effector molecules has grown rapidly in recent years satisfying the high demand for new applications. However, until now molecules carrying PPGs have been designed to activate function only in a single direction, namely the release of the effector molecule. Herein, we present the new approach Two-PPGs-One-Molecule (TPOM) that exploits the orthogonal photolysis of two photoprotecting groups to first release the effector molecule and then to modify it to suppress its induced effect. The moiety resembling the tyrosyl side chain of the translation inhibitor puromycin was synthetically modified to the photosensitive ortho-nitrophenylalanine that cyclizes upon near UV-irradiation to an inactive puromycin cinnoline derivative. Additionally, the modified puromycin analog was protected by the thio-coumarylmethyl group as the second PPG. This TPOM strategy allows an initial wavelength-selective activation followed by a second light-induced deactivation. Both photolysis processes were spectroscopically studied in the UV/vis- and IR-region. In combination with quantum-chemical calculations and time-resolved NMR spectroscopy, the photoproducts of both activation and deactivation steps upon illumination were characterized. We further probed the translation inhibition effect of the new synthesized puromycin analog upon light activation/deactivation in a cell-free GFP translation assay. TPOM as a new method for precise triggering activation/deactivation of effector molecules represents a valuable addition for the control of biological processes with light.

RNA:DNA:DNA triple helix (triplex) formation regulates gene expression, but is difficult to study experimentally in vivo. This makes accurate computational prediction of triplex formation highly important in the field of RNA research. Current predictive methods have used canonical Hoogsteen base pairing rules, which whilst biophysically valid, may not reflect the plastic nature of cell biology. Herein, we present TriplexAligner, a local alignment tool implementing probabilistic scoring matrices learned from triplex-forming sequences captured in published triplexRNA-seq and triplexDNA-seq experiments. Short, conserved sequence elements were found to be enriched at points of triplex formation. Probabilistic mapping codes between RNA and DNA sequences were learned by Expectation-Maximisation, and used as scoring matrices for local alignment. TriplexAligner predicts RNA-DNA interactions identified in all-to-all sequencing data more accurately than previously published tools, and also predicts previously studied triplex interactions with known regulatory functions.

The computation of correct features is an essential phase for efficient data representation and benchmarked accuracy in text recognition systems. The offline text lacks dynamic information regarding the writing order or nature of trajectories of stroke. Recovery of drawing order technique helps to retrieve trajectory of a stroke. This information aids in computing dynamic feature vector based on chain codes or trajectory points for text recognition. The present work proposes a dynamic feature extraction approach based on recovery of drawing order to understand scene text in Indic script Gurmukhi. An inhouse dataset of strokes was obtained from 820 real time Gurmukhi signboard images. Stroke recognition was performed using Conv1D, SVM and HMM classifiers. Best recognition results were achieved using SVM and Conv1D as 82.88% and 84.67%. The major objective of present study is to propose dynamic features based recognition scheme for Indic scripts signboard images suitable for real-life applications.

The SARS-CoV-2 genome encodes for approximately 30 proteins. Within the international project COVID19-NMR, we distribute the spectroscopic analysis of the viral proteins and RNA. Here, we report NMR chemical shift assignments for the protein Nsp3b, a domain of Nsp3. The 217-kDa large Nsp3 protein contains multiple structurally independent, yet functionally related domains including the viral papain-like protease and Nsp3b, a macrodomain (MD). In general, the MDs of SARS-CoV and MERS-CoV were suggested to play a key role in viral replication by modulating the immune response of the host. The MDs are structurally conserved. They most likely remove ADP-ribose, a common posttranslational modification, from protein side chains. This de-ADP ribosylating function has potentially evolved to protect the virus from the anti-viral ADP-ribosylation catalyzed by poly-ADP-ribose polymerases (PARPs), which in turn are triggered by pathogen-associated sensing of the host immune system. This renders the SARS-CoV-2 Nsp3b a highly relevant drug target in the viral replication process. We here report the near-complete NMR backbone resonance assignment (1H, 13C, 15N) of the putative Nsp3b MD in its apo form and in complex with ADP-ribose. Furthermore, we derive the secondary structure of Nsp3b in solution. In addition, 15N-relaxation data suggest an ordered, rigid core of the MD structure. These data will provide a basis for NMR investigations targeted at obtaining small-molecule inhibitors interfering with the catalytic activity of Nsp3b.

The ongoing pandemic of the respiratory disease COVID-19 is caused by the SARS-CoV-2 (SCoV2) virus. SCoV2 is a member of the Betacoronavirus genus. The 30 kb positive sense, single stranded RNA genome of SCoV2 features 5'- and 3'-genomic ends that are highly conserved among Betacoronaviruses. These genomic ends contain structured cis-acting RNA elements, which are involved in the regulation of viral replication and translation. Structural information about these potential antiviral drug targets supports the development of novel classes of therapeutics against COVID-19. The highly conserved branched stem-loop 5 (SL5) found within the 5'-untranslated region (5'-UTR) consists of a basal stem and three stem-loops, namely SL5a, SL5b and SL5c. Both, SL5a and SL5b feature a 5'-UUUCGU-3' hexaloop that is also found among Alphacoronaviruses. Here, we report the extensive H-1, C-13 and N-15 resonance assignment of the 37 nucleotides (nts) long sequence spanning SL5b and SL5c (SL5b +c), as basis for further in-depth structural studies by solution NMR spectroscopy., Biomolecular NMR Assignments, 16, ISSN:1874-270X, ISSN:1874-2718

The stem-loop (SL1) is the 5'-terminal structural element within the single-stranded SARS-CoV-2 RNA genome. It is formed by nucleotides 7–33 and consists of two short helical segments interrupted by an asymmetric internal loop. This architecture is conserved among Betacoronaviruses. SL1 is present in genomic SARS-CoV-2 RNA as well as in all subgenomic mRNA species produced by the virus during replication, thus representing a ubiquitous cis-regulatory RNA with potential functions at all stages of the viral life cycle. We present here the 1H, 13C and 15N chemical shift assignment of the 29 nucleotides-RNA construct 5_SL1, which denotes the native 27mer SL1 stabilized by an additional terminal G-C base-pair.

The SARS-CoV-2 virus is the cause of the respiratory disease COVID-19. As of today, therapeutic interventions in severe COVID-19 cases are still not available as no effective therapeutics have been developed so far. Despite the ongoing development of a number of effective vaccines, therapeutics to fight the disease once it has been contracted will still be required. Promising targets for the development of antiviral agents against SARS-CoV-2 can be found in the viral RNA genome. The 5′- and 3′-genomic ends of the 30 kb SCoV-2 genome are highly conserved among Betacoronaviruses and contain structured RNA elements involved in the translation and replication of the viral genome. The 40 nucleotides (nt) long highly conserved stem-loop 4 (5_SL4) is located within the 5′-untranslated region (5′-UTR) important for viral replication. 5_SL4 features an extended stem structure disrupted by several pyrimidine mismatches and is capped by a pentaloop. Here, we report extensive 1H, 13C, 15N and 31P resonance assignments of 5_SL4 as the basis for in-depth structural and ligand screening studies by solution NMR spectroscopy.

The current pandemic situation caused by the Betacoronavirus SARS-CoV-2 (SCoV2) highlights the need for coordinated research to combat COVID-19. A particularly important aspect is the development of medication. In addition to viral proteins, structured RNA elements represent a potent alternative as drug targets. The search for drugs that target RNA requires their high-resolution structural characterization. Using nuclear magnetic resonance (NMR) spectroscopy, a worldwide consortium of NMR researchers aims to characterize potential RNA drug targets of SCoV2. Here, we report the characterization of 15 conserved RNA elements located at the 5' end, the ribosomal frameshift segment and the 3'-untranslated region (3'-UTR) of the SCoV2 genome, their large-scale production and NMR-based secondary structure determination. The NMR data are corroborated with secondary structure probing by DMS footprinting experiments. The close agreement of NMR secondary structure determination of isolated RNA elements with DMS footprinting and NMR performed on larger RNA regions shows that the secondary structure elements fold independently. The NMR data reported here provide the basis for NMR investigations of RNA function, RNA interactions with viral and host proteins and screening campaigns to identify potential RNA binders for pharmaceutical intervention.

The international Covid19-NMR consortium aims at the comprehensive spectroscopic characterization of SARS-CoV-2 RNA elements and proteins and will provide NMR chemical shift assignments of the molecular components of this virus. The SARS-CoV-2 genome encodes approximately 30 different proteins. Four of these proteins are involved in forming the viral envelope or in the packaging of the RNA genome and are therefore called structural proteins. The other proteins fulfill a variety of functions during the viral life cycle and comprise the so-called non-structural proteins (nsps). Here, we report the near-complete NMR resonance assignment for the backbone chemical shifts of the non-structural protein 10 (nsp10). Nsp10 is part of the viral replication-transcription complex (RTC). It aids in synthesizing and modifying the genomic and subgenomic RNAs. Via its interaction with nsp14, it ensures transcriptional fidelity of the RNA-dependent RNA polymerase, and through its stimulation of the methyltransferase activity of nsp16, it aids in synthesizing the RNA cap structures which protect the viral RNAs from being recognized by the innate immune system. Both of these functions can be potentially targeted by drugs. Our data will aid in performing additional NMR-based characterizations, and provide a basis for the identification of possible small molecule ligands interfering with nsp10 exerting its essential role in viral replication.

We report here on the nuclear magnetic resonance (NMR) 19 F screening of 14 RNA targets with different secondary and tertiary structure to systematically assess druggability of RNAs. Our RNA targets include representative bacterial riboswitches that naturally bind with nanomolar affinity and high specificity to cellular metabolites of low molecular weight. Based on counter‐screens against five DNAs and five proteins, we can show that RNA can be specifically targeted. To demonstrate the quality of the initial fragment library that has been designed for easy follow‐up chemistry, we further show how to increase binding affinity from an initial fragment hit by chemistry that links the identified fragment to the intercalator acridine. Thus, we achieve low micromolar binding affinity without losing binding specificity between two different terminator structures.

The efficient feature extraction technique plays a pivotal role in the text recognition systems. A stable and an effective feature set can help to achieve benchmarked accuracy in text recognition systems. In the present study, we aim to recognize the Gurmukhi signboard image classes using the dynamic, static and hybrid feature sets. The static features consist of the zoning and diagonal features, while dynamic features are based on the recovery of drawing order technique. The hybrid feature set comprises both static and dynamic features. An in-house dataset of the Gurmukhi characters has been developed from 820 real-time Gurmukhi signboard images as no benchmarked Gurmukhi signboard dataset is available publicly. 43 distinct classes are obtained for Gurmukhi middle zone characters having 9,544 strokes. The stroke recognition has been performed using the Support Vector Machine and Conv1D deep learning method. Best overall recognition accuracy using a hybrid feature set have been achieved using the Support Vector Machine and Conv1D deep learning method as 91.37% and 93.39% respectively. The primary objective of the proposed study is to present a comparison of the static, dynamic and hybrid features based recognition scheme for various image classes obtained from the Gurmukhi signboard images. This work can be extended for other Indic scripts as it is suitable for real-life applications.

Initiation of bacterial translation requires that the ribosome-binding site in mRNAs adopts single-stranded conformations. In Gram-negative bacteria the ribosomal protein S1 (rS1) is a key player in resolving of structured elements in mRNAs. However, the exact mechanism of how rS1 unfolds persistent secondary structures in the translation initiation region (TIR) is still unknown. Here, we show by NMR spectroscopy that Vibrio vulnificus rS1 displays a unique architecture of its mRNA-binding domains, where domains D3 and D4 provide the mRNA-binding platform and cover the nucleotide binding length of the full-length rS1. D5 significantly increases rS1’s chaperone activity, although it displays structural heterogeneity both in isolation and in presence of the other domains, albeit to varying degrees. The heterogeneity is induced by the switch between the two equilibrium conformations and is triggered by an order-to-order transition of two mutually exclusive secondary structures (β-strand-to-α-helix) of the ‘AERERI’ sequence. The conformational switching is exploited for melting of structured 5′-UTR’s, as the conformational heterogeneity of D5 can compensate the entropic penalty of complex formation. Our data thus provides a detailed understanding of the intricate coupling of protein and RNA folding dynamics enabling translation initiation of structured mRNAs.

A Gram-negative, alkalotolerant bacterium, isolated from the soil continually drained with industrial wastewater and identified as gamma-proteobacterium by partial 16S rRNA sequence analysis, produced a polyphenol oxidase, which showed laccase but not tyrosinase activity. The organism grew well from pH 6 to 10 and produced laccase maximally at pH 10. The enzyme was stable from pH 3 to 10.6 for at least 24 h and was optimally active at 55 °C and pH 6.5 in a 5 min assay.

SARS‐CoV‐2 contains a positive single‐stranded RNA genome of approximately 30 000 nucleotides. Within this genome, 15 RNA elements were identified as conserved between SARS‐CoV and SARS‐CoV‐2. By nuclear magnetic resonance (NMR) spectroscopy, we previously determined that these elements fold independently, in line with data from in vivo and ex‐vivo structural probing experiments. These elements contain non‐base‐paired regions that potentially harbor ligand‐binding pockets. Here, we performed an NMR‐based screening of a poised fragment library of 768 compounds for binding to these RNAs, employing three different 1H‐based 1D NMR binding assays. The screening identified common as well as RNA‐element specific hits. The results allow selection of the most promising of the 15 RNA elements as putative drug targets. Based on the identified hits, we derive key functional units and groups in ligands for effective targeting of the RNA of SARS‐CoV‐2., The RNA genome of SARS‐CoV‐2 contains 15 independently folded regulatory RNA elements. By NMR‐based fragment screening, the RNA target space for small molecule binding, functional mapping and inhibition is defined.

12 pags., 4 figs., 3 tabs., SARS-CoV-2 (SCoV2) and its variants of concern pose serious challenges to the public health. The variants increased challenges to vaccines, thus necessitating for development of new intervention strategies including anti-virals. Within the international Covid19-NMR consortium, we have identified binders targeting the RNA genome of SCoV2. We established protocols for the production and NMR characterization of more than 80 % of all SCoV2 proteins. Here, we performed an NMR screening using a fragment library for binding to 25 SCoV2 proteins and identified hits also against previously unexplored SCoV2 proteins. Computational mapping was used to predict binding sites and identify functional moieties (chemotypes) of the ligands occupying these pockets. Striking consensus was observed between NMR-detected binding sites of the main protease and the computational procedure. Our investigation provides novel structural and chemical space for structure-based drug design against the SCoV2 proteome., Work at BMRZ is supported by the state of Hesse. Work in Covid19-NMR was supported by the Goethe Corona Funds, by the IWBEFRE-program 20007375 of state of Hesse, the DFG through CRC902: “Molecular Principles of RNA-based regulation.” and through infrastructure funds (project numbers: 277478796, 277479031, 392682309, 452632086, 70653611) and by European Union’s Horizon 2020 research and innovation program iNEXT-discovery under grant agreement No 871037. BY-COVID receives funding from the European Union’s Horizon Europe Research and Innovation Programme under grant agreement number 101046203. “INSPIRED” (MIS 5002550) project, implemented under the Action “Reinforcement of the Research and Innovation Infrastructure,” funded by the Operational Program “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the EU (European Regional Development Fund) and the FP7 REGPOT CT-2011-285950—“SEE-DRUG” project (purchase of UPAT’s 700 MHz NMR equipment). The support of the CERM/CIRMMP center of Instruct-ERIC is gratefully acknowledged. This work has been funded in part by a grant of the Italian Ministry of University and Research (FISR2020IP_02112, ID-COVID) and by Fondazione CR Firenze. A.S. is supported by the Deutsche Forschungsgemeinschaft [SFB902/B16, SCHL2062/2-1] and the Johanna Quandt Young Academy at Goethe [2019/AS01]. M.H. and C.F. thank SFB902 and the Stiftung Polytechnische Gesellschaft for the Scholarship. L.L. work was supported by the French National Research Agency (ANR, NMR-SCoV2-ORF8), the Fondation de la Recherche Médicale (FRM, NMR-SCoV2-ORF8), FINOVI and the IR-RMN-THC Fr3050 CNRS. Work at UConn Health was supported by grants from the US National Institutes of Health (R01 GM135592 to B.H., P41 GM111135 and R01 GM123249 to J.C.H.) and the US National Science Foundation (DBI 2030601 to J.C.H.). Latvian Council of Science Grant No. VPP-COVID-2020/1-0014. National Science Foundation EAGER MCB-2031269. This work was supported by the grant Krebsliga KFS-4903-08-2019 and SNF-311030_192646 to J.O. P.G. (ITMP) The EOSC Future project is co-funded by the European Union Horizon Programme call INFRAEOSC-03-2020—Grant Agreement Number 101017536. Open Access funding enabled and organized by Projekt DEAL