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Starting from (MeO)3SiCH2Cl (10) and Ph2(H)SiCH2OH (16), respectively, the (hydroxymethyl)diphenyl(piperidinoalkyl)silanes (HOCH2)Ph2Si(CH2)2NC5H10 (6) and (HOCH2)Ph2Si(CH2)3NC5H10 (8) have been synthesized [10→Ph2(MeO)SiCH2Cl (11)→Ph2(CH2=CH)SiCH2Cl (12)→Ph2(CH2=CH)SiCH2OAc (13)→Ph2(CH2=CH)SiCH2OH (14)→Ph2(CH2=CH)SiCH2OSiMe3 (15)→6; 16→Ph2(H)SiCH2OSiMe3 (17)→8; NC5H10 = piperidino]. N-Quaternization of 6 and 8 with MeI gave the corresponding methiodides 7 and 9, respectively. As shown by IR-spectroscopic studies, compounds 6 and 8 form intramolecular O-H···N hydrogen bonds in solution (CCl4). In the crystal, 6 (space group Pna21; two crystallographically independent molecules) also forms intramolecular O-H···N hydrogen bonds whereas 8 (space group P1̅) forms intermolecular O-H···N hydrogen bonds leading to the formation of centrosymmetric dimers (single-crystal X-ray diffraction studies). The (hydroxymethyl) silanes 6-9 and the related silanols (HO)Ph2Si(CH2)2NC5H 10 (sila-pridinol; 1), sila-pridinol methiodide (2), (HO)Ph2Si(CH2)3NC5H10 (sila-difenidol; 3) and sila-difenidol methiodide (4) were investigated for their antimuscarinic properties. In functional pharmacological experiments as well as in radioligand competition studies, all compounds behaved as simple competitive antagonists at muscarinic M1-, M2-, M3- and M4-receptors. In general, the silanols 1-4 displayed higher receptor affinities (up to 100-fold) than the corresponding (hydroxymethyl) silanes 6-9 . In the (hydroxymethyl)silane series, compound 7 was found to be the most potent muscarinic antagonist [pA2/pKi= 8,71/8,6 (M1), 8,23/7,8 (M2), 8,19/7,8 (M3); pKi = 8,2 (M4)]. In the silanol series, the related compound 2 showed the most interesting antimuscarinic properties [pA2/pKi = 10,37/9,6 (M1), 8,97/8,8 (M2), 9,08/8,8 (M3); pKi = 9,4 (M4)].
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.
1H, 13C, and 15N backbone chemical shift assignments of coronavirus-2 non-structural protein Nsp10
(2020)
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.
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 current outbreak of the highly infectious COVID-19 respiratory disease is caused by the novel coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). To fight the pandemic, the search for promising viral drug targets has become a cross-border common goal of the international biomedical research community. Within the international Covid19-NMR consortium, scientists support drug development against SARS-CoV-2 by providing publicly available NMR data on viral proteins and RNAs. The coronavirus nucleocapsid protein (N protein) is an RNA-binding protein involved in viral transcription and replication. Its primary function is the packaging of the viral RNA genome. The highly conserved architecture of the coronavirus N protein consists of an N-terminal RNA-binding domain (NTD), followed by an intrinsically disordered Serine/Arginine (SR)-rich linker and a C-terminal dimerization domain (CTD). Besides its involvement in oligomerization, the CTD of the N protein (N-CTD) is also able to bind to nucleic acids by itself, independent of the NTD. Here, we report the near-complete NMR backbone chemical shift assignments of the SARS-CoV-2 N-CTD to provide the basis for downstream applications, in particular site-resolved drug binding studies.
The ongoing pandemic caused by the Betacoronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) demonstrates the urgent need of coordinated and rapid research towards inhibitors of the COVID-19 lung disease. The covid19-nmr consortium seeks to support drug development by providing publicly accessible NMR data on the viral RNA elements and proteins. The SARS-CoV-2 genome encodes for approximately 30 proteins, among them are the 16 so-called non-structural proteins (Nsps) of the replication/transcription complex. The 217-kDa large Nsp3 spans one polypeptide chain, but comprises multiple independent, yet functionally related domains including the viral papain-like protease. The Nsp3e sub-moiety contains a putative nucleic acid-binding domain (NAB) with so far unknown function and consensus target sequences, which are conceived to be both viral and host RNAs and DNAs, as well as protein-protein interactions. Its NMR-suitable size renders it an attractive object to study, both for understanding the SARS-CoV-2 architecture and drugability besides the classical virus’ proteases. We here report the near-complete NMR backbone chemical shifts of the putative Nsp3e NAB that reveal the secondary structure and compactness of the domain, and provide a basis for NMR-based investigations towards understanding and interfering with RNA- and small-molecule-binding by Nsp3e.
Plants, fungi and algae are important components of global biodiversity and are fundamental to all ecosystems. They are the basis for human well-being, providing food, materials and medicines. Specimens of all three groups of organisms are accommodated in herbaria, where they are commonly referred to as botanical specimens.The large number of specimens in herbaria provides an ample, permanent and continuously improving knowledge base on these organisms and an indispensable source for the analysis of the distribution of species in space and time critical for current and future research relating to global biodiversity. In order to make full use of this resource, a research infrastructure has to be built that grants comprehensive and free access to the information in herbaria and botanical collections in general. This can be achieved through digitization of the botanical objects and associated data.The botanical research community can count on a long-standing tradition of collaboration among institutions and individuals. It agreed on data standards and standard services even before the advent of computerization and information networking, an example being the Index Herbariorum as a global registry of herbaria helping towards the unique identification of specimens cited in the literature.In the spirit of this collaborative history, 51 representatives from 30 institutions advocate to start the digitization of botanical collections with the overall wall-to-wall digitization of the flat objects stored in German herbaria. Germany has 70 herbaria holding almost 23 million specimens according to a national survey carried out in 2019. 87% of these specimens are not yet digitized. Experiences from other countries like France, the Netherlands, Finland, the US and Australia show that herbaria can be comprehensively and cost-efficiently digitized in a relatively short time due to established workflows and protocols for the high-throughput digitization of flat objects.Most of the herbaria are part of a university (34), fewer belong to municipal museums (10) or state museums (8), six herbaria belong to institutions also supported by federal funds such as Leibniz institutes, and four belong to non-governmental organizations. A common data infrastructure must therefore integrate different kinds of institutions.Making full use of the data gained by digitization requires the set-up of a digital infrastructure for storage, archiving, content indexing and networking as well as standardized access for the scientific use of digital objects. A standards-based portfolio of technical components has already been developed and successfully tested by the Biodiversity Informatics Community over the last two decades, comprising among others access protocols, collection databases, portals, tools for semantic enrichment and annotation, international networking, storage and archiving in accordance with international standards. This was achieved through the funding by national and international programs and initiatives, which also paved the road for the German contribution to the Global Biodiversity Information Facility (GBIF).Herbaria constitute a large part of the German botanical collections that also comprise living collections in botanical gardens and seed banks, DNA- and tissue samples, specimens preserved in fluids or on microscope slides and more. Once the herbaria are digitized, these resources can be integrated, adding to the value of the overall research infrastructure. The community has agreed on tasks that are shared between the herbaria, as the German GBIF model already successfully demonstrates.We have compiled nine scientific use cases of immediate societal relevance for an integrated infrastructure of botanical collections. They address accelerated biodiversity discovery and research, biomonitoring and conservation planning, biodiversity modelling, the generation of trait information, automated image recognition by artificial intelligence, automated pathogen detection, contextualization by interlinking objects, enabling provenance research, as well as education, outreach and citizen science.We propose to start this initiative now in order to valorize German botanical collections as a vital part of a worldwide biodiversity data pool.
Immersion freezing is the most relevant heterogeneous ice nucleation mechanism through which ice crystals are formed in mixed-phase clouds. In recent years, an increasing number of laboratory experiments utilizing a variety of instruments have examined immersion freezing activity of atmospherically relevant ice-nucleating particles. However, an intercomparison of these laboratory results is a difficult task because investigators have used different ice nucleation (IN) measurement methods to produce these results. A remaining challenge is to explore the sensitivity and accuracy of these techniques and to understand how the IN results are potentially influenced or biased by experimental parameters associated with these techniques.
Within the framework of INUIT (Ice Nuclei Research Unit), we distributed an illite-rich sample (illite NX) as a representative surrogate for atmospheric mineral dust particles to investigators to perform immersion freezing experiments using different IN measurement methods and to obtain IN data as a function of particle concentration, temperature (T), cooling rate and nucleation time. A total of 17 measurement methods were involved in the data intercomparison. Experiments with seven instruments started with the test sample pre-suspended in water before cooling, while 10 other instruments employed water vapor condensation onto dry-dispersed particles followed by immersion freezing. The resulting comprehensive immersion freezing data set was evaluated using the ice nucleation active surface-site density, ns, to develop a representative ns(T) spectrum that spans a wide temperature range (−37 °C < T < −11 °C) and covers 9 orders of magnitude in ns.
In general, the 17 immersion freezing measurement techniques deviate, within a range of about 8 °C in terms of temperature, by 3 orders of magnitude with respect to ns. In addition, we show evidence that the immersion freezing efficiency expressed in ns of illite NX particles is relatively independent of droplet size, particle mass in suspension, particle size and cooling rate during freezing. A strong temperature dependence and weak time and size dependence of the immersion freezing efficiency of illite-rich clay mineral particles enabled the ns parameterization solely as a function of temperature. We also characterized the ns(T) spectra and identified a section with a steep slope between −20 and −27 °C, where a large fraction of active sites of our test dust may trigger immersion freezing. This slope was followed by a region with a gentler slope at temperatures below −27 °C. While the agreement between different instruments was reasonable below ~ −27 °C, there seemed to be a different trend in the temperature-dependent ice nucleation activity from the suspension and dry-dispersed particle measurements for this mineral dust, in particular at higher temperatures. For instance, the ice nucleation activity expressed in ns was smaller for the average of the wet suspended samples and higher for the average of the dry-dispersed aerosol samples between about −27 and −18 °C. Only instruments making measurements with wet suspended samples were able to measure ice nucleation above −18 °C. A possible explanation for the deviation between −27 and −18 °C is discussed. Multiple exponential distribution fits in both linear and log space for both specific surface area-based ns(T) and geometric surface area-based ns(T) are provided. These new fits, constrained by using identical reference samples, will help to compare IN measurement methods that are not included in the present study and IN data from future IN instruments.
Immersion freezing is the most relevant heterogeneous ice nucleation mechanism through which ice crystals are formed in mixed-phase clouds. In recent years, an increasing number of laboratory experiments utilizing a variety of instruments have examined immersion freezing activity of atmospherically relevant ice nucleating particles (INPs). However, an inter-comparison of these laboratory results is a difficult task because investigators have used different ice nucleation (IN) measurement methods to produce these results. A remaining challenge is to explore the sensitivity and accuracy of these techniques and to understand how the IN results are potentially influenced or biased by experimental parameters associated with these techniques.
Within the framework of INUIT (Ice Nucleation research UnIT), we distributed an illite rich sample (illite NX) as a representative surrogate for atmospheric mineral dust particles to investigators to perform immersion freezing experiments using different IN measurement methods and to obtain IN data as a function of particle concentration, temperature (T), cooling rate and nucleation time. Seventeen measurement methods were involved in the data inter-comparison. Experiments with seven instruments started with the test sample pre-suspended in water before cooling, while ten other instruments employed water vapor condensation onto dry-dispersed particles followed by immersion freezing. The resulting comprehensive immersion freezing dataset was evaluated using the ice nucleation active surface-site density (ns) to develop a representative ns(T) spectrum that spans a wide temperature range (−37 °C < T < −11 °C) and covers nine orders of magnitude in ns.
Our inter-comparison results revealed a discrepancy between suspension and dry-dispersed particle measurements for this mineral dust. While the agreement was good below ~ −26 °C, the ice nucleation activity, expressed in ns, was smaller for the wet suspended samples and higher for the dry-dispersed aerosol samples between about −26 and −18 °C. Only instruments making measurement techniques with wet suspended samples were able to measure ice nucleation above −18 °C. A possible explanation for the deviation between −26 and −18 °C is discussed. In general, the seventeen immersion freezing measurement techniques deviate, within the range of about 7 °C in terms of temperature, by three orders of magnitude with respect to ns. In addition, we show evidence that the immersion freezing efficiency (i.e., ns) of illite NX particles is relatively independent on droplet size, particle mass in suspension, particle size and cooling rate during freezing. A strong temperature-dependence and weak time- and size-dependence of immersion freezing efficiency of illite-rich clay mineral particles enabled the ns parameterization solely as a function of temperature. We also characterized the ns (T) spectra, and identified a section with a steep slope between −20 and −27 °C, where a large fraction of active sites of our test dust may trigger immersion freezing. This slope was followed by a region with a gentler slope at temperatures below −27 °C. A multiple exponential distribution fit is expressed as ns(T) = exp(23.82 × exp(−exp(0.16 × (T + 17.49))) + 1.39) based on the specific surface area and ns(T) = exp(25.75 × exp(−exp(0.13 × (T + 17.17))) + 3.34) based on the geometric area (ns and T in m−2 and °C, respectively). These new fits, constrained by using an identical reference samples, will help to compare IN measurement methods that are not included in the present study and, thereby, IN data from future IN instruments.