An Overview of Secondary Metabolites from Soft Corals of the Genus Capnella over the Five Decades: Chemical Structures, Pharmacological Activities, NMR Data, and Chemical Synthesis.

There has been no specific review on the secondary metabolites from soft corals of the genus Capnella till now. In this work, all secondary metabolites from different species of the title genus were described. It covered the first work from 1974 to May 2024, spanning five decades. In the viewpoint of the general structural features, these chemical constituents were classified into four groups: sesquiterpenes, diterpenes, steroids, and lipids. Additionally, the 1H and 13C NMR data of these metabolites were provided when available in the literature. Among them, sesquiterpenes were the most abundant chemical compositions from soft corals of the genus Capnella. A variety of pharmacological activities of these compounds were evaluated, such as cytotoxic, antibacterial, antifungal, and anti-inflammatory activities. In addition, the chemical synthesis works of several representative sesquiterpenes were provided. This review aims to provide an up-to-date knowledge of the chemical structures, pharmacological activities, and chemical synthesis of the chemical constituents from soft corals of the genus Capnella.

Isolation and characterization of isomeric tibetin spiroethers from the roots of Tanacetum dolichophyllum (Kitam.) Kitam.

The roots of Tanacetum dolichophyllum (Kitam.) Kitam. (syn. Hippolytia dolichophylla (Kitam.) K.Bremer & Humphries) were collected from high altitude area of Munsyari, district Pithoragarh (Uttarakhand, India) yielded essential oil by steam distillation method and the oil was analysed by TLC and GC-MS. The GC-MS analysis of the essential oil sample showed the dominance of two constituents visible in sesquiterpene range. These constituents were isolated by column chromatography. The structures of these compounds were determined on the basis of 1H-NMR,13C NMR, COSY, 135-DEPT, and HRMS (ESI-TOF) spectral data. The two major compounds were identified as isomeric C14-Tibetin spiroethers, namely (E)-2-(2',4'-heptadiyn-1'-ylidene)-1,6-dioxaspiro[4.4]non-3-ene and (Z)-2-(2',4'-heptadiyn-1'-ylidene)-1,6-dioxaspiro[4.4]non-3-ene.

Pretreating and normalizing metabolomics data for statistical analysis.

Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples. Metabolomics is emerging as a powerful tool generally for precision medicine. Particularly, integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease. However, metabolomics data are very complicated. Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis. In this review article, we comprehensively review various methods that are used to preprocess and pretreat metabolomics data, including MS-based data and NMR -based data preprocessing, dealing with zero and/or missing values and detecting outliers, data normalization, data centering and scaling, data transformation. We discuss the advantages and limitations of each method. The choice for a suitable preprocessing method is determined by the biological hypothesis, the characteristics of the data set, and the selected statistical data analysis method. We then provide the perspective of their applications in the microbiome and metabolome research.

GRIN-toolbox: A versatile and light toolbox for NMR inversion.

NMR technique serves as a powerful analytical tool with diverse applications in fields such as chemistry, biology, and material science. However, the effectiveness of NMR heavily relies on data post-processing which is often modeled as regularized inverse problem. Recently, we proposed the Generally Regularized INversion (GRIN) algorithm and demonstrated its effectiveness in NMR data processing. GRIN has been integrated as a friendly graphic user interface-based toolbox which was not detailed in the original paper. In this paper, to make GRIN more practically accessible to NMR practitioners, we focus on introducing the usage of GRIN-Toolbox with processing examples and the corresponding processing graphic interfaces, and the user manual is attached as Supplementary Material. GRIN-Toolbox is versatile and lightweight, where various kinds of data processing tasks can be completed with one click, including but not limited to diffusion-ordered spectroscopy processing, magnetic resonance imaging under-sampling reconstruction, Laplace (diffusion or relaxation) NMR inversion, spectrum denoising, etc. In addition, GRIN-Toolbox could be extended to more applications with user-designed inversion models and freely available at https://github.com/EricLin1993/GRIN.

Practical considerations for rapid and quantitative NMR-based metabolomics.

NMR is a key technology for metabolomics because of its robustness and reproducibility. Herein we discuss practical considerations that extend the utility of NMR spectroscopy. First, the long T1 spin relaxation times of small molecules limits high-throughput data acquisition because most experimental time is lost while waiting for signal recovery. In principle, the addition of a small amount of commercially-available paramagnetic gadolinium chelate allows cost-effective and efficient high-throughput mixture analysis with correct concentration determination. However, idle time caused by slow temperature regulation during sample exchanges, poses a next constraint. We show how, with proper care, NMR sample scanning times can be reduced additionally by a factor of two. Lastly, we describe how equidistant bucketing is a simple and fast procedure for metabolomic fingerprinting. The combination of these advancements help to make NMR metabolomics more versatile than it is today.

Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data.

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open-source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70-108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.

Twelve New Seco-Pregnane Glycosides from Cynanchum taihangense.

For our interest in the potential biologically active and structurally unique steroidal glycosides, continued phytochemical investigation of Cynanchum taihangense was carried out; twelve new seco-pregnane glycosides, cynataihosides I-L (1-4), M-T (7-14), and two known glycosides, glaucoside A (5) and atratcynoside F (6), were isolated from the 95% ethanol extract of Cynanchum taihangense. Two new aglycones were found among compounds 10, 11, 13, and 14. The structures of the glycosides were elucidated based on 1D and 2D NMR spectroscopic data, HR-ESI-MS analysis, and chemical evidence. The cytotoxicity of compounds against three human tumor cell lines (HL-60, THP-1, and PC-3) were evaluated by MTT assay. Compound 11 displayed significant cytotoxicity against THP-1 and PC-3 cell line with IC50 values of 5.08 and 22.75 µm, respectively. Compounds 3 and 14 exhibited moderate and selective cytotoxicity on HL-60 and THP-1 with IC50 values of 17.78 and 16.02 µm, respectively.

Sampling CASE Application for the Quality Control of Published Natural Product Structures.

Structure elucidation with NMR correlation data is dicey, as there is no way to tell how ambiguous the data set is and how reliably it will define a constitution. Many different software tools for computer assisted structure elucidation (CASE) have become available over the past decades, all of which could ensure a better quality of the elucidation process, but their use is still not common. Since 2011, WebCocon has integrated the possibility to generate theoretical NMR correlation data, starting from an existing structural proposal, allowing this theoretical data then to be used for CASE. Now, WebCocon can also read the recently presented NMReDATA format, allowing for uncomplicated access to CASE with experimental data. With these capabilities, WebCocon presents itself as an easily accessible Web-Tool for the quality control of proposed new natural products. Results of this application to several molecules from literature are shown and demonstrate how CASE can contribute to improve the reliability of Structure elucidation with NMR correlation data.

Naturally Occurring Chromone Glycosides: Sources, Bioactivities, and Spectroscopic Features.

Chromone glycosides comprise an important group of secondary metabolites. They are widely distributed in plants and, to a lesser extent, in fungi and bacteria. Significant biological activities, including antiviral, anti-inflammatory, antitumor, antimicrobial, etc., have been discovered for chromone glycosides, suggesting their potential as drug leads. This review compiles 192 naturally occurring chromone glycosides along with their sources, classification, biological activities, and spectroscopic features. Detailed biosynthetic pathways and chemotaxonomic studies are also described. Extensive spectroscopic features for this class of compounds have been thoroughly discussed, and detailed 13C-NMR data of compounds 1-192, have been added, except for those that have no reported 13C-NMR data.

Phytochemical Constituents and Biological Activities of Plants from the Genus Cissampelos.

Cissampelos is a significant genus comprising of approximately 21 species of the medicinal plants (Menispermaceae). The plants of this genus are used in traditional medicine for the treatment of various ailments such as asthma, arthritis, dysentery, hyperglycemia, cardiopathy, hypertension and other related problems. These plants are rich in bioactive dibenzylisoquinoline and aborphine as well as small amounts of other ingredients. In recent years, the chemical constituents and pharmacological activities of Cissampelos genus have been paid more and more attention due to their diversity. Herein, we compile the chemical constituents and biological activities on this genus, and summarize the 13 C-NMR data of the main bioactive ingredients. All information comes from scientific databases such as Google Scholar, PubMed, Sci-Finder, ScienceDirect, Web of Science and CNKI. It provides valuable data for the future research and development of Cissampelos genus.

Fourier and Laplace-like low-field NMR spectroscopy: The perspectives of multivariate and artificial neural networks analyses.

Low field Nuclear Magnetic Resonance (LF-NMR) is a rich source of information for a wide range of samples types. These can be hard or soft solids, such as plastics or elastomers; bulk liquids or liquids absorbed in porous materials, and can come from biomaterials, biological tissues, archaeological artifacts, cultural heritage objects. LF-NMR instruments present a significant advance especially for in situ, ex situ and in vivo measurement of relaxation and diffusion. Moreover, high resolution 1D and 2D spectroscopy, as well as magnetic resonance (MR) imaging are available in these fields. In this work we discuss the advanced analysis of the data measured in LF-NMR from the perspectives of tertiary level that implies the analysis on principal components (PCA), and on the quaternary analysis that uses an artificial neural network (ANN). The principles of PCA and ANN are largely discussed. For the PCA analysis, a series of 52 spectra were analyzed, having been recorded in vivo by LF-NMR. Of these spectra, 38 were generated from normal uterus, 7 by uterus tissue with endometrial cancer, and another 7 were obtained from tissues of women with uterine cervical cancer. The PC1 vs PC2 plot was further analyzed using an artificial neural network, and the results are presented as 2D maps of probability. Furthermore, the perspectives of applying an ANN to solve the problem of Laplace-like inversion are discussed. An example of such ANN was presented and the performance was discussed. Finally, a model of complex ANN, capable to sequentially solve this kind of problems specific to LF-NMR is proposed and discussed.

Using mathematics in MRI data management for glioma assesment.

Our aim is to review the mathematical tools usefulness in MR data management for glioma diagnosis and treatment optimization. MRI does not give access to organs variations in hours or days. However a lot of multiparametric data are generated. Mathematics could help to override this paradox, the aim of this article is to show how. We first make a review on mathematical modelling using equations. Afterwards we present statistical analysis. We provide detailed examples in both sections. We finally conclude, giving some clues on in silico models.

Two new pregnane steroidal glycosides from Cynanchum taihangense.

As our ongoing chemical investigation, two new pregnane steroidal glycosides, cynataihosides G (1), with a new aglycone, and H (2) were isolated from the 95% ethanol extract of Cynanchum taihangense. Their structures were elucidated on the basis of 1 D and 2 D NMR spectral data, HR-ESI-MS analysis and qualitative chemical methods. The compounds were subjected to detect the cytotoxicity against three human tumor cell lines (HL-60, THP-1 and PC-3). The compounds displayed no significant cytotoxicity.Supplemental data for this article can be accessed at https://doi.org/10.1080/14786419.2019.1672682.

Data for the synthesis and characterisation of 2,6-di(bromomethyl)-3,5-bis(alkoxycarbonyl)-4-aryl-1,4-dihydropyridines as important intermediates for synthesis of amphiphilic 1,4-dihydropyridines.

This data file describes the synthetic protocol for preparation of the original 2,6-di(bromomethyl)-3,5-bis(alkoxycarbonyl)-4-aryl-1,4-dihydropyridines. In total, 6 unpublished compounds were obtained and characterised. The 2,6-di(bromomethyl)-1,4-dihydropyridines are mainly used as intermediates for synthesis of various lipid-like compounds based on 1,4-dihydropyridine cycle. All the structures of 2,6-di(bromomethyl)-1,4-dihydropyridines were confirmed by Nuclear Magnetic Resonance (NMR, including 1H NMR and 13C NMR) data. The data provided herein are directly related to the previously published research article - "Novel cationic amphiphilic 1,4-dihydropyridine derivatives for DNA delivery" [1] where three derivatives (2,6-di(bromomethyl)-4-phenyl-1,4-dihydropyridines 2a-c) from six presented in this data file were used as starting materials in synthesis of amphiphilic 1,4-dihydropyridines without any purification and characterisation. Synthesis of other three 2,6-di(bromomethyl)-3,5-bis(alkoxycarbonyl)-4-aryl-1,4-dihydropyridines 2d-f and their characterisation are reported herein at the first time. Information provided in this data file can be used in organic synthesis by other chemists to develop synthetic strategies for the construction of various cationic 1,4-dihydropyridine derivatives and related heterocycles.

BioMagResBank (BMRB) as a Resource for Structural Biology.

The Biological Magnetic Resonance Data Bank (BioMagResBank or BMRB), founded in 1988, serves as the archive for data generated by nuclear magnetic resonance (NMR) spectroscopy of biological systems. NMR spectroscopy is unique among biophysical approaches in its ability to provide a broad range of atomic and higher-level information relevant to the structural, dynamic, and chemical properties of biological macromolecules, as well as report on metabolite and natural product concentrations in complex mixtures and their chemical structures. BMRB became a core member of the Worldwide Protein Data Bank (wwPDB) in 2007, and the BMRB archive is now a core archive of the wwPDB. Currently, about 10% of the structures deposited into the PDB archive are based on NMR spectroscopy. BMRB stores experimental and derived data from biomolecular NMR studies. Newer BMRB biopolymer depositions are divided about evenly between those associated with structure determinations (atomic coordinates and supporting information archived in the PDB) and those reporting experimental information on molecular dynamics, conformational transitions, ligand binding, assigned chemical shifts, or other results from NMR spectroscopy. BMRB also provides resources for NMR studies of metabolites and other small molecules that are often macromolecular ligands and/or nonstandard residues. This chapter is directed to the structural biology community rather than the metabolomics and natural products community. Our goal is to describe various BMRB services offered to structural biology researchers and how they can be accessed and utilized. These services can be classified into four main groups: (1) data deposition, (2) data retrieval, (3) data analysis, and (4) services for NMR spectroscopists and software developers. The chapter also describes the NMR-STAR data format used by BMRB and the tools provided to facilitate its use. For programmers, BMRB offers an application programming interface (API) and libraries in the Python and R languages that enable users to develop their own BMRB-based tools for data analysis, visualization, and manipulation of NMR-STAR formatted files. BMRB also provides users with direct access tools through the NMRbox platform.

Recovering the Missing Regions in Crystal Structures from the Nuclear Magnetic Resonance Measurement Data Using Matrix Completion Method.

Based on matrix completion algorithm, we proposed a simple method to recover the missing regions in the X-ray crystal structures using the corresponding nuclear magnetic resonance (NMR) measurement data for the proteins with both X-ray and NMR experimental data deposited in Protein Data Bank (PDB). By selecting 10 test proteins deposited in PDB and comparing with the standard MODELLER results from the root-mean-square deviation and MolProbity aspects, we validated that our method can provide a better protein structure model, which combines both X-ray crystallographic structure data and NMR data together than MODELLER algorithm. This method is particularly useful for building the initial structures in Molecular Dynamics when studying the protein folding process.

Biologically Active Echinulin-Related Indolediketopiperazines from the Marine Sediment-Derived Fungus Aspergillus niveoglaucus.

Seven known echinulin-related indolediketopiperazine alkaloids (1-7) were isolated from the Vietnamese sediment-derived fungus Aspergillus niveoglaucus. Using chiral HPLC, the enantiomers of cryptoechinuline B (1) were isolated as individual compounds for the first time. (+)-Cryptoechinuline B (1a) exhibited neuroprotective activity in 6-OHDA-, paraquat-, and rotenone-induced in vitro models of Parkinson's disease. (-)-Cryptoechinuline B (1b) and neoechinulin C (5) protected the neuronal cells against paraquat-induced damage in a Parkinson's disease model. Neoechinulin B (4) exhibited cytoprotective activity in a rotenone-induced model, and neoechinulin (7) showed activity in the 6-OHDA-induced model.

Protein structure prediction assisted with sparse NMR data in CASP13.

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.

Bioactive Aliphatic Sulfates from Marine Invertebrates.

The occurrence of sulfated steroids and phenolics in marine organisms is quite widespread, being typically reported from Echinoderms. In contrast, alkane and alkene aliphatic sulfates are considerably rarer with examples being reported from a diverse array of organisms including echinoderms, sponges and ascidians. While no ecological roles for these metabolites have been proposed, they do exhibit a diverse array of biological activities including thrombin inhibition; the ability to induce metamorphosis in larvae; antiproliferative, antibacterial and antifungal properties; and metalloproteinase inhibition. Of particular interest and an avenue for future development is the finding of antifouling properties with low or nontoxic effects to the environment. This review focuses on alkyl sulfates and related sulfamates, their structures and biological activities. Spectroscopic and spectrometric techniques that can be used to recognize the presence of sulfate groups are also discussed, data for which will enhance the ability of researchers to recognize this class of chemically- and biologically-interesting marine natural products.

Data on the characterization of (N-alkylsalicylaldiminato)bis(2-phenylpyridinato)iridium(III).

Herein we report the synthesis, characterization data and photophysical properties of iridium(III) complexes having N-alkylated salicylaldimine and 2-phenylpyridine ligands. The structures of novel iridium complexes were assigned by 1H and 13C NMR, 1H-1H COSY, NOESY, HMQC, HMBC, HRMS, IR and XRD analysis. For further information, we obtained photophysical properties in solution and crystalline states.