Structure Identification Of Compounds Research Articles

Interaction of Acinetobacter sp. RIT 592 induces the production of broad-spectrum antibiotics in Exiguobacterium sp. RIT 594.

Antimicrobial resistance (AMR) is one of the most alarming global public health challenges of the 21st century. Over 3 million antimicrobial-resistant infections occur in the United States annually, with nearly 50,000 cases being fatal. Innovations in drug discovery methods and platforms are crucial to identify novel antibiotics to combat AMR. We present the isolation and characterization of potentially novel antibiotic lead compounds produced by the cross-feeding of two rhizosphere bacteria, Acinetobacter sp. RIT 592 and Exiguobacterium sp. RIT 594. We used solid-phase extraction (SPE) followed by liquid chromatography (LC) to enrich antibiotic extracts and subsequently mass spectrometry (MS) analysis of collected fractions for compound structure identification and characterization. The MS data were processed through the Global Natural Product Social Molecular Networking (GNPS) database. The supernatant from RIT 592 induced RIT 594 to produce a cocktail of antimicrobial compounds active against Gram-positive and negative bacteria. The GNPS analysis indicated compounds with known antimicrobial activity in the bioactive samples, including oligopeptides and their derivatives. This work emphasizes the utility of microbial community-based platforms to discover novel clinically relevant secondary metabolites. Future work includes further structural characterization and antibiotic activity evaluation of the individual compounds against pathogenic multidrug-resistant (MDR) bacteria.

Applications of Nuclear Magnetic Resonance Spectroscopy to Traditional Chinese Medicine

Background In recent years, the focus of traditional Chinese medicine (TCM) research has gradually shifted from pure chemical research to looking at the combination of chemistry with the life sciences. However, due to the complexity of the chemical composition of Chinese medicinal plants, understanding material structure through in vivo and in vitro studies is a bottleneck in biological analysis. Nuclear magnetic resonance (NMR) spectroscopy has numerous unique advantages over other analytical techniques, and has developed into a crucial tool in Chinese medicine research. This review aims to analyze and summarize the application of NMR under the multiple fields of TCM. Methods The online databases PubMed, Web of Science, Embase, Cochrane Library, and CNKI were using the search terms “Nuclear magnetic resonance spectrometry,” “NMR,”“Chinese medicine,” “TCM,” “Herbal medicine,” were from January 1, 2010 to June 1, 2023 Results The research scopes were 375 after being selected. The latest applications of NMR to TCM including its application to metabolomics, structural identification and efficacy exploration, and authentication and quality control. There were 116 articles on metabolism, 196 articles on compound structure identification and 63 articles on quality control. The metabolomic study of NMR is in line with the multicomponent, multitarget, and overall comprehensive effect of TCM. The structural identification of the chemical components of TCM is more helpful for the discovery of other biologically active substances with high efficacy and few side effects. NMR provides support in quality control and planting site selection of TCM. Conclusion NMR has become an essential tool and makes great contributions to the field of TCM research, the innovation in the application of NMR technology will aid with the research on the material basis of TCM.

Machine learning for identification of silylated derivatives from mass spectra

MotivationCompound structure identification is using increasingly more sophisticated computational tools, among which machine learning tools are a recent addition that quickly gains in importance. These tools, of which the method titled Compound Structure Identification:Input Output Kernel Regression (CSI:IOKR) is an excellent example, have been used to elucidate compound structure from mass spectral (MS) data with significant accuracy, confidence and speed. They have, however, largely focused on data coming from liquid chromatography coupled to tandem mass spectrometry (LC–MS).Gas chromatography coupled to mass spectrometry (GC–MS) is an alternative which offers several advantages as compared to LC–MS, including higher data reproducibility. Of special importance is the substantial compound coverage offered by GC–MS, further expanded by derivatization procedures, such as silylation, which can improve the volatility, thermal stability and chromatographic peak shape of semi-volatile analytes. Despite these advantages and the increasing size of compound databases and MS libraries, GC–MS data have not yet been used by machine learning approaches to compound structure identification.ResultsThis study presents a successful application of the CSI:IOKR machine learning method for the identification of environmental contaminants from GC–MS spectra. We use CSI:IOKR as an alternative to exhaustive search of MS libraries, independent of instrumental platform and data processing software. We use a comprehensive dataset of GC–MS spectra of trimethylsilyl derivatives and their molecular structures, derived from a large commercially available MS library, to train a model that maps between spectra and molecular structures. We test the learned model on a different dataset of GC–MS spectra of trimethylsilyl derivatives of environmental contaminants, generated in-house and made publicly available. The results show that 37% (resp. 50%) of the tested compounds are correctly ranked among the top 10 (resp. 20) candidate compounds suggested by the model. Even though spectral comparisons with reference standards or de novo structural elucidations are neccessary to validate the predictions, machine learning provides efficient candidate prioritization and reduction of the time spent for compound annotation.

Rapid identification and isolation of neuraminidase inhibitors from mockstrawberry (Duchesnea indica Andr.) based on ligand fishing combined with HR-ESI-Q-TOF-MS

Neuraminidase inhibitors (NAIs) are the mainstay antiviral drugs against influenza infection. In this study, a ligand fishing protocol was developed to screen NAIs using neuraminidase immobilized magnetic beads (NA-MB). After verifying the feasibility of NA-MB with an artificial mixture including NA inhibitors and non-inhibitors, the developed ligand fishing protocol was applied to screen NAIs from the crude extracts of Duchesnea indica Andr. Twenty-four NA binding compounds were identified from the normal butanol (n-BuOH) extract of D. indica as potential NAIs by high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC–Q-TOF-MS) assisted with Compound Structure Identification (CSI):FingerID, including 12 ellagitannins, 4 brevifolin derivatives, 3 ellagic acid derivatives, and 4 flavonoids. Among them, 9 compounds were isolated and tested for in vitro NA inhibitory activities against NA from Clostridium perfringens, and from oseltamivir sensitive and resistant influenza A virus strains. The results indicate that compound B23 has the NA inhibitory activities in both the oseltamivir sensitive and resistant viral NA, with half maximal inhibitory concentration (IC50) values of 197.9 and 125.4 μmol/L, respectively. Moreover, B23 can obviously reduce the replication of oseltamivir sensitive and resistant viruses in Madin–Darby canine kidney (MDCK) cells at the concentrations of 40 and 200 μmol/L.

AMPK Activation of Flavonoids from Psidium guajava Leaves in L6 Rat Myoblast Cells and L02 Human Hepatic Cells.

Objective To isolate the hypoglycemic bioactive components from leaves of Psidium guajava and evaluate their AMP-activated protein kinase (AMPK) activities. Methods A variety of column chromatography was used for the isolation of compounds, and nuclear magnetic resonance (NMR) and mass spectrum (MS) were used for the structure identification of compounds. AMP-activated protein kinase (AMPK) activity of compounds obtained from leaves of Psidium guajava was evaluated in L6 rat myoblast cells and L02 human hepatic cells by western blot. Results Six principal flavonoids largely present in the leaves of Psidium guajava, quercetin (1), quercetin-3-O-α-L-arabinofuranoside (2), quercetin-3-O-α-L-arabinopyranoside (3), quercetin-3-O-β-D-galactopyranoside (4), quercetin-3-O-β-D-glucopyranoside (5), and quercetin-3-O-β-D-xylopyranoside (6), were obtained and compound 1–6 exhibited significant activity on AMPK activation both in L6 cells and L02 cells (p < 0.01) compared with Control. In particular, the effects of quercetin on AMPK activation were extremely significant compared with Control (p < 0.001). Conclusions These findings demonstrated that these flavonoids had potential for the activation of AMPK and hypoglycemic activity.

Gas Chromatography-Tandem Mass Spectrometry of Lignin Pyrolyzates with Dopant-Assisted Atmospheric Pressure Chemical Ionization and Molecular Structure Search with CSI:FingerID.

Dopant-assisted atmospheric pressure chemical ionization (dAPCI) is a soft ionization method rarely used for gas chromatography-mass spectrometry (GC-MS). The current study combines GC-dAPCI with tandem mass spectrometry (MS/MS) for analysis of a complex mixture such as lignin pyrolysis analysis. To identify the structures of volatile lignin pyrolysis products, collision-induced dissociation (CID) MS/MS using a quadrupole time-of-flight mass spectrometer (QTOFMS) and pseudo MS/MS through in-source collision-induced dissociation (ISCID) using a single stage TOFMS are utilized. To overcome the lack of MS/MS database, Compound Structure Identification (CSI):FingerID is used to interpret CID spectra and predict best matched structures from PubChem library. With this approach, a total of 59 compounds were positively identified in comparison to only 22 in NIST database search of GC-EI-MS dataset. This study demonstrates the effectiveness of GC-dAPCI-MS/MS to overcome the limitations of traditional GC-EI-MS analysis when EI-MS database is not sufficient. Graphical Abstract ᅟ.