Identification and structure elucidation by NMR spectroscopy

Synthesis of Anti-Inflammatory Drugs' Chalcone Derivatives and a Study of Their Conformational Properties Through a Combination of Nuclear Magnetic Resonance Spectroscopy and Molecular Modeling

BACKGROUND

In this study, two chalcone analogs were synthesized through in silico and experimental methods, and their potential to inhibit the lipoxygenase enzyme, which plays a role in the inflammation pathway, was assessed. Specifically, this study is a continuation of previous research in which chalcone derivatives were synthesized and characterized.

OBJECTIVES/METHODS

In the current work, we present the re-synthesis of two chalcones, with a focus on their docking studies, NMR analysis, and dynamic simulations. The structure of each chalcone was elucidated through a combination of Nuclear Magnetic Resonance (NMR) and Density Functional Theory (DFT). The substituent effect on the absorption spectrum of the two chalcone derivatives was studied.

RESULTS

A "LOX-chalcone" complex, predicted by docking studies, was further examined using molecular dynamics (MD) simulations to evaluate the stability of the complex. After fully characterizing the "LOX-chalcone" complexes in silico, the atomic details of each chalcone's interaction with LOX-1 and 5-LOX were revealed through Saturation Transfer Difference (STD) NMR (Nuclear Magnetic Resonance). Finally, their selectivity profile was investigated against human 15-LOX-1 and general Lipoxidase activity.

CONCLUSIONS

The in silico methods suggest that chalcones could be promising lead compounds for drug designs targeting the LOX enzyme.

Pure Shift NMR in Continuous Flow

Flow NMR is an expanding analytical approach with applications that include in-line analysis for process control and optimisation, and real-time reaction monitoring. The samples monitored by flow NMR are typically mixtures that yield complex 1D 1H spectra. "Pure shift" NMR is a powerful approach to simplifying 1H NMR spectra, but its standard implementation is not compatible with continuous flow because of interference between sample motion and the position-dependent spin manipulations that are required in pure shift NMR. Here we show that pure shift NMR spectra can be successfully collected for continuously flowing samples, thanks to an adapted acquisition scheme, robust solvent suppression, and a velocity-compensation strategy. The resulting method is used to collect ultrahigh resolution reaction monitoring data. Pure shift NMR spectra are expected to benefit many applications of flow NMR.

Optimizing Extraction Solvents for Plant Chemical Analysis Using 1H NMR and Principal Component Analysis

Natural products, particularly plants, remain a vital source of bioactive compounds owing to their unparalleled metabolic diversity across pharmaceuticals, cosmetics, foods, and agriculture. However, this diversity, encompassing not only a multitude of compounds but also their varying chemical and physical properties, presents a challenge in their effective utilization. Targeted analysis of specific metabolites, as well as untargeted approaches covering a wide metabolite range, necessitate optimal extraction solvents tailored to meet diverse requirements. Achieving optimization requires two crucial components: analytical methods capable of capturing a broad spectrum of metabolites and effective data analysis to derive meaningful conclusions. In this regard, 1H nuclear magnetic resonance (NMR) spectroscopy combined with principal component analysis (PCA) emerges as a promising approach for optimization. In this protocol, employing two model plants, leaves of basil (Ocimum basilicum) and lettuce (Lactura sativa), we deduce optimal extraction solvents from mixtures of methanol-water, acetonitrile, and chloroform, leveraging 1H NMR and PCA analyses.

On the Performance of Second-Order Polarization Propagator Methods in the Calculation of 1JFC and nJFH NMR Spin-Spin Coupling Constants

This study evaluates the performance of doubles-corrected random phase approximation (RPA) and higher random phase approximation (HRPA) approaches in predicting nuclear magnetic resonance (NMR) coupling constants involving fluorine. Their performance is benchmarked against experimental data and compared with that of higher-level theoretical methods, specifically second-order polarization propagator (SOPPA) and SOPPA(CCSD). Additionally, we discuss their performance relative to density functional theory (DFT). We find that RPA(D) is severely constrained by (near) triplet instabilities, while HRPA(D) demonstrates markedly improved stability. Statistical analysis reveals stronger patterns for carbon-fluorine couplings across the methods and systems investigated compared with fluorine-hydrogen couplings. While SOPPA-based methodologies prove to be superior in accuracy, HRPA(D) shows promising performance in reducing the computational burden of these calculations, albeit with a tendency to underestimate the coupling strength. These findings highlight the potential of HRPA(D) as a practical alternative to SOPPA methods, even for such difficult properties as NMR spin-spin coupling constants involving fluorine, emphasizing its role in improving predictive accuracy and efficiency across diverse chemical environments.

Tuning and Matching Error-Compensated, Quantitative Solid-State Nuclear Magnetic Resonance

NMR spectroscopy has long been recognized as a powerful quantitative analytical tool. Quantification is commonly done against internal and external standards. A third approach is to quantify against an electronic reference, which combines the advantages of the two methods. The implementation of this approach in solid-state NMR is more challenging due to the single-coil design of double resonance probes. In this study, a novel approach for implementing the electronic referencing method in solid-state NMR by injecting the reference signal using a broadband antenna installed near the NMR receiver coil is presented. This method demonstrates excellent accuracy and precision, as it remains robust to changes in the electronic conditions of the probe, including tuning and matching errors.

Deciphering molecular structures: NMR spectroscopy and quantum mechanical insights of halogenated 4H-Chromenediones

Sesquiterpene lactones (SL) represent a class of secondary metabolites found in the Asteraceae family, notable for their unique structures. The SL α-santonin (1) and its derivatives are worthy of mention due to their diverse biological properties. Additionally, 4H-chromenes and 4H-chromones are appealing frameworks holding the capability to be used as structural motifs for new drugs. Furthermore, unambiguous structural elucidation is crucial for developing novel compounds for diverse applications. In this context, it is common to find in the literature molecules erroneously assigned. Therefore, the use of quantum mechanical calculations to simulate NMR chemical shifts has emerged as a valuable strategy. In this work, we conceived the synthesis of two halogenated 4H-chromenediones derived from photosantonic acid (2), a photoproduct arising from irradiation of α-santonin (1) in the ultraviolet region. The structure of the chlorinated and brominated products was determined by NMR analysis, with the aid of quantum mechanical calculations at the B3LYP/6-311 + G(2d,p)//M062x/6-31 + G(d,p) level of theory. All analyses were in agreement and led to the assignment of the brominated 4H-chromene-2,7-dione as (3S,3aS,5aR,9bS)-5a-(2-bromopropan-2-yl)-3-methyl-3,3a,5,5a,8,9b-hexahydro-4H-furo[2,3-f]chromene-2,7-dione (11b) and of the chlorinated 4H-chromene-2,7-dione as (3S,3aS,5aR,9bS)-5a-(2-chloropropan-2-yl)-3-methyl-3,3a,5,5a,8,9b-hexahydro-4H-furo[2,3-f]chromene-2,7-dione (12b). The diastereoselectivities of the reactions were explained based on products and intermediates formation energy calculated using B3LYP/6-31 + G(d,p) as the level of theory. Structures 11b and 12b were identified as the thermodynamic and kinetic products of the reaction among all candidates. Consequently, the strategy utilized in this study is robust and successfully illustrates the use of quantum mechanical calculations in the structural elucidation of new compounds with potential applications as novel drugs or products.

Preparation of AIEgen-based near-infrared afterglow luminescence nanoprobes for tumor imaging and image-guided tumor resection

Fluorescence imaging represents a vital tool in modern biology, oncology and biomedical applications. Afterglow luminescence (AGL), which circumvents the light scattering and tissue autofluorescence interference associated with real-time excitation source, shows remarkably increased imaging sensitivity and depth. Here we present a protocol for the design and synthesis of AGL nanoprobes with an aggregation-induced emission (AIE) effect to simultaneously red shift and amplify the afterglow signal for tumor imaging and image-guided tumor resection. The nanoprobe (AGL AIE dot) is composed of an enol ether format of Schaap's agent and a near-infrared AIE fluorogen (AIEgen) (tetraphenylethylene-phenyl-dicyanomethylene-4H-chromene, TPE-Ph-DCM) to suppress the nonradiative dissipation pathway. Pre-irradiating AGL AIE dots with white light could generate singlet oxygen to convert Schaap's agent to its 1,2-dioxetane format, thus initializing the AGL process. With the aid of AIEgen, the AGL shows simultaneously red shifted emission maximum (from ~540 nm to ~625 nm) and enhanced intensity (by 3.2-fold), facilitating better signal-to-background ratio, imaging sensitivity and depth. Intriguingly, the activated AGL can last for over 10 days. Compared with conventional approaches, our method provides a new solution to concurrently red shift and amplify afterglow signals for better in vivo imaging outcomes. The preparation of AGL AIE dots takes ~2 days, the in vitro characterization takes ~10 days (less than 1 day if not involving afterglow kinetic profile study) and the tumor imaging and image-guided tumor resection takes ~7 days. These procedures can be easily reproduced and amended after standard laboratory training in chemical synthesis and animal handling.

High-Throughput Compound Quality Assessment with High-Mass-Resolution Acoustic Ejection Mass Spectrometry: An Automatic Data Processing Toolkit

Pharmacological screening heavily relies on the reliability of compound libraries. To ensure the accuracy of screening results, fast and reliable quality control (QC) of these libraries is essential. While liquid chromatography (LC) with ultraviolet (UV) or mass spectrometry (MS) detection has been employed for molecule QC on small sample sets, the analytical throughput becomes a bottleneck when dealing with large libraries. Acoustic ejection mass spectrometry (AEMS) is a high-throughput analytical platform that covers a broad range of chemical structural space. In this study, we present the utilization of an AEMS system equipped with a high-resolution MS analyzer for high-throughput compound QC. To facilitate efficient data processing, which is a key challenge for such a high-throughput application, we introduce an automatic data processing toolkit that allows for the high-throughput assessment of the sample standards' quantitative and qualitative characteristics, including purity calculation with the background processing option. Moreover, the toolkit includes a module for quantitatively comparing spectral similarity with the reference library. Integrating the described high-resolution AEMS system with the data processing toolkit effectively eliminates the analytical bottleneck, enabling a rapid and reliable compound quality assessment of large-scale compound libraries.

In vitro cytotoxic activity on KATO-III cancer cell lines of mangiferolic acid purified from Thai Tetragonula laeviceps propolis

Gastric cancer is a global health concern, but current treatment with chemotherapy and surgery is often inadequate, prompting the exploration of alternative treatments. Propolis is a natural substance collected by bees known for its diverse properties linked to floral sources. The Dichloromethane Partitioned Extract (DPE) from Tetragonula laeviceps propolis, in Bankha district, Thailand was previously shown to possess significant cytotoxicity against KATO-III gastric cancer cells, while showing lower cytotoxicity toward WI-38 normal fibroblast cells. Here, the DPE was further fractionated by column chromatography, identified active fractions, and subjected to structural analysis using nuclear magnetic resonance spectroscopy. Cytotoxicity against KATO-III cells was reevaluated, and programmed cell death was analyzed using flow cytometry. Expression levels of cancer-related genes were measured using quantitative real-time reverse transcriptase PCR. Cardol C15:2 (compound 1) and mangiferolic acid (MF; compound 2) were discovered in the most active fractions following structural analysis. MF exhibited strong cytotoxicity against KATO-III cells (IC50 of 4.78-16.02 μg/mL), although this was less effective than doxorubicin (IC50 of 0.56-1.55 μg/mL). Morphological changes, including decreased cell density and increased debris, were observed in KATO-III cells treated with 30 μg/mL of MF. Significant induction of late-stage apoptosis and necrosis, particularly at 48 and 72 h, suggested potential DNA damage and cell cycle arrest, evidenced by an increased proportion of sub-G1 and S-phase cells. Doxorubicin, the positive control, triggered late apoptosis but caused more necrosis after 72 h. Furthermore, MF at 30 μg/mL significantly increased the expression level of COX2 and NFκB genes linked to inflammation and cell death pathways. This upregulation was consistent at later time points (48 and 72 h) and was accompanied by increased expression of CASP3 and CASP7 genes. These findings suggest MF effectively induces cell death in KATO-III cells through late apoptosis and necrosis, potentially mediated by upregulated inflammation-related genes.

Automated nuclear magnetic resonance fingerprinting of mixtures

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for qualitative and quantitative analysis. However, for complex mixtures, determining the speciation from NMR spectra can be tedious and sometimes even unfeasible. On the other hand, identifying and quantifying structural groups in a mixture from NMR spectra is much easier than doing the same for components. We call this group-based approach "NMR fingerprinting." In this work, we show that NMR fingerprinting can even be performed in an automated way, without expert knowledge, based only on standard NMR spectra, namely, 13C, 1H, and 13C DEPT NMR spectra. Our approach is based on the machine-learning method of support vector classification (SVC), which was trained here on thousands of labeled pure-component NMR spectra from open-source data banks. We demonstrate the applicability of the automated NMR fingerprinting using test mixtures, of which spectra were taken using a simple benchtop NMR spectrometer. The results from the NMR fingerprinting agree remarkably well with the ground truth, which was known from the gravimetric preparation of the samples. To facilitate the application of the method, we provide an interactive website (https://nmr-fingerprinting.de), where spectral information can be uploaded and which returns the NMR fingerprint. The NMR fingerprinting can be used in many ways, for example, for process monitoring or thermodynamic modeling using group-contribution methods-or simply as a first step in species analysis.

Nuclear Magnetic Resonance Fingerprints and Mini DNA Markers for the Authentication of Cinnamon Species Ingredients Used in Food and Natural Health Products

Cinnamomum verum (syn C. zeylanicum) is considered 'true' cinnamon. However, it is reported that less expensive sources of cinnamon from C. cassia (syn C. aromaticum), C. loureiroi, and C. burmannii (toxic coumarin) may be used in the place of C. verum. We lack the quality assurance tools that are required to differentiate C. verum from other cinnamon species when verifying that the correct species is sourced from ingredient suppliers. The current research on cinnamon species authentication using DNA tools is limited to a few species and the use of high-quality DNA extracted from raw leaf materials. The cinnamon bark traded in the supply chain contains much less DNA and poorer-quality DNA than leaves. Our research advances DNA methods to authenticate cinnamon, as we utilized full-length chloroplast genomes via a genome skimming approach for C. burmannii and C. cassia to facilitate the design of optimal mini DNA markers. Furthermore, we developed and validated the use of NMR fingerprints for several commercial cinnamon species, including the quantification of 16 molecules. NMR fingerprints provided additional data that were useful for quality assessment in cinnamon extract powders and product consistency. Both the new mini DNA markers and NMR fingerprints were tested on commercial cinnamon products.

Recent advances on erythorbyl fatty acid esters as multi-functional food emulsifiers

Over the past few decades, food scientists have investigated a wide range of emulsifiers to manufacture stable and safe emulsion-based food products. More recently, the development of emulsifiers with multi-functionality, which is the ability to have more than two functions, has been considered as a promising strategy for resolving rancidification and microbial contamination in emulsions. Erythorbyl fatty acid esters (EFEs) synthesized by enzymatic esterification of hydrophilic erythorbic acid and hydrophobic fatty acid have been proposed as multi-functional emulsifiers since they simultaneously exhibit amphiphilic, antioxidative, and antibacterial properties in both aqueous and emulsion systems. This review provides current knowledge about EFEs in terms of enzymatic synthesis and multi-functionality. All processes for synthesizing and identifying EFEs are discussed. Each functionality of EFEs and the proposed mechanism are described with analytical methodologies and experimental details. It would provide valuable insights into the development and application of a multi-functional emulsifier in food emulsion chemistry.

OrbiFragsNets. A tool for automatic annotation of orbitrap MS2 spectra using networks grade as selection criteria

We introduce OrbiFragsNets, a tool for automatic annotation of MS2 spectra generated by Orbitrap instruments, as well as the concepts of chemical consistency and fragments networks. OrbiFragsNets takes advantage of the specific confidence interval for each peak in every MS2 spectrum, which is an unclear idea across the high-resolution mass spectrometry literature. The spectrum annotations are expressed as fragments networks, a set of networks with the possible combinations of annotations for the fragments. The model behind OrbiFragsNets is briefly described here and explained in detail in the constantly updated manual available in the GitHub repository. This new approach in MS2 spectrum de novo automatic annotation proved to perform as good as well established tools such as RMassBank and SIRIUS.•A new approach on automatic annotation of Orbitrap MS2 spectra is introduced.•Possible spectrum annotation are described as independent consistent networks, with annotations for each fragment as nodes, and annotations for the mass difference between fragments as edges.•Annotation process is described as the selection of the most connected fragments network.

Preparative three-dimensional GC and nuclear magnetic resonance for the isolation and identification of two sesquiterpene ethers from Dictyota Dichotoma

Separation science plays a crucial role in the isolation of novel compounds contained in complex matrices. Yet their rationale employment needs preliminary structure elucidation, which usually requires sufficient aliquots of grade substances to characterize the molecule by nuclear magnetic resonance experiments. In this study, two peculiar oxa-tricycloundecane ethers were isolated by means of preparative multidimensional gas chromatography from the brown alga species Dictyota dichotoma (Huds.) Lam., aiming to assign their 3D structures. Density functional theory simulations were carried out to select the correct configurational species matching the experimental NMR data (in terms of enantiomeric couples). In this case, the theoretical approach was crucial as the protonic signal overlap and spectral overcrowding were preventing any other unambiguous structural information. Just after the identification through the density functional theory data matching of the correct relative configuration it was possible to verify an enhanced self-consistency with the experimental data, confirming the stereochemistry. The results obtained further pave the way toward structure elucidation of highly asymmetric molecules, whose configuration cannot be inferred by other means or strategies.

SpectraFP: a new spectra-based descriptor to aid in cheminformatics, molecular characterization and search algorithm applications

We have developed an algorithm to generate a new spectra-based descriptor, called SpectraFP, in order to digitalize the chemical shifts of ¹³C NMR spectra, as well as potentially important data from other spectroscopic techniques. This descriptor is a fingerprint vector with defined sizes and values of 0 and 1, with the ability to correct chemical shift fluctuations. To explore the applicability of SpectraFP, we outlined two application scenarios: (1) the prediction of six functional groups by machine learning (ML) models and (2) the search for structures based on the similarity between the query spectrum and spectra in an experimental database, both in the SpectraFP format. For each functional group, five ML models were built and validated following the OECD principles: internal and external validations, applicability domains, and mechanistic interpretations. All the models resulted in high goodness-of-fit for the training and test sets with MCC respectively between 0.626 and 0.909 and 0.653 and 0.917, and J ranging from 0.812 to 0.957 and 0.825 to 0.961. Using the SHAP (SHapley Additive exPlanations) approach, the mechanistic interpretations of the models were explored; the results indicated that the most important variables for model decision making were coherent with the expected chemical shifts for each functional group. Several metrics, including Tanimoto, geometric, arithmetic, and Tversky, can be used to perform the similarity calculation for the search algorithm. This algorithm can also incorporate additional variables, such as the correction parameter and the difference between the amount of signals in the query spectrum and the database spectra, while preserving its high performance speed. We hope that our descriptor can link information from spectroscopic/spectrometric techniques with ML models to expand the possibilities in understanding the field of cheminformatics. All databases and algorithms developed for this work are open sources and freely accessible.

A Single-Scan Ultraselective Heteronuclear Polarization Transfer Method for Unambiguous Complex Structure Assignment

Complex nuclear magnetic resonance (NMR) signals of organic compounds containing multiple analogous substructures or mixtures pose a significant challenge to structural identification, thus resulting in frequent misassignment of structures. The GEMSTONE method, a single-scan technique that selectively excites a specific proton signal among the crowded NMR signals, was recently proposed as a solution. However, its extension to the polarization transfer method for heteronuclear spin systems was unsuccessful. Here, we present an extension method that addresses the altered heteronuclear polarization transfer efficiency and enables the acquisition of ultraselective 13C and 1H-13C correlation NMR subspectra with hertz-level signal selectivity in both dimensions. We demonstrate the effectiveness of this technique in the structural analysis of a chromopeptide pharmaceutical and a diastereomeric mixture fungicide.

Hyperpolarized NMR metabolomics

Hyperpolarized NMR is a promising approach to address the sensitivity limits of conventional NMR metabolomics approaches, which currently fails to detect minute metabolite concentrations in biological samples. This review describes how tremendous signal enhancement offered by dissolution-dynamic nuclear polarization and parahydrogen-based techniques can be fully exploited for molecular omics sciences. Recent developments, including the combination of hyperpolarization techniques with fast multi-dimensional NMR implementation and quantitative workflows are described, and a comprehensive comparison of existing hyperpolarization techniques is proposed. High-throughput, sensitivity, resolution and other relevant challenges that should be tackled for a general application of hyperpolarized NMR in metabolomics are discussed.

Neuroprotective activity of novel phenanthrene derivative from Grewia tiliaefolia by in vitro and in silico studies

Medicinal plants possess range of phytochemicals accountable for their diverse biological activities. Presently, such compounds have been isolated from medicinal plants, characterized and evaluated for their pharmacological potential. In the present study, the efforts have been made to isolate the compound(s) from Grewia tiliaefolia Vahl., plant known for its ameliorative effect on brain related diseases such as anxiety, depression, cognitive disorders and Parkinson's disease. Plant extract was subjected to isolation of compound(s) using column chromatography and isolated compound was characterized by NMR FTIR and LCMS. The isolated compound was novel with the IUPAC name of the compound is propyl 3-hydroxy-10,13-dimethyl-6,7,8,9,10,11,12,13,14,15,16,17-dodecahydro-3H-cyclopenta[a]phenanthrene-17-carboxylate, designated as A-1 and has not been reported before. A-1 was further evaluated for its antioxidant potential using in vitro antioxidant assays (2,2-diphenyl-1-picryl-hydrazyl-hydrate, DPPH assay and reducing power assay, RPA). Also, Acetylcholinesterase (AChE) inhibitory potential of A-1 and extract was analysed. Results showed that A-1 exhibited significantly higher antioxidant activity in both DPPH and RPA assay as compared to plant extract. In case of AChE inhibitory activity again, A-1 has shown significantly higher activity as compared to plant extract. In silico study was conducted to predict its action on proteins playing crucial role in neurological and neurodegenerative disorders such as gamma amino butyric acid (GABA) receptor and glutamate α amino-3-hydroxyl-5-methyl-4-isoxazolepropionic acid (Glu AMPA) receptor in epilepsy and AChE enzyme in Alzheimer's diseases. The compound has shown interaction in following order: AChE > GABA receptor > Glu AMPA receptor. Further, molecular dynamic simulations and ADME studies of A-1 and AChE enzyme revealed that A-1 yielded good results in all parameters and hence can relieve Alzheimer's like symptoms.

Sherlock-A Free and Open-Source System for the Computer-Assisted Structure Elucidation of Organic Compounds from NMR Data

The structure elucidation of small organic molecules (<1500 Dalton) through 1D and 2D nuclear magnetic resonance (NMR) data analysis is a potentially challenging, combinatorial problem. This publication presents Sherlock, a free and open-source Computer-Assisted Structure Elucidation (CASE) software where the user controls the chain of elementary operations through a versatile graphical user interface, including spectral peak picking, addition of automatically or user-defined structure constraints, structure generation, ranking and display of the solutions. A set of forty-five compounds was selected in order to illustrate the new possibilities offered to organic chemists by Sherlock for improving the reliability and traceability of structure elucidation results.

A pilot study for fragment identification using 2D NMR and deep learning

This paper presents a proof of concept of a method to identify substructures in 2D NMR spectra of mixtures using a bespoke image-based convolutional neural network application. This is done using HSQC and HMBC spectra separately and in combination. The application can reliably detect substructures in pure compounds, using a simple network. Results indicate that it can work for mixtures when trained on pure compounds only. HMBC data and the combination of HMBC and HSQC show better results than HSQC alone in this pilot study.

High-performance thin-layer chromatography coupled attenuated total reflectance-Fourier-transform infrared and NMR spectroscopy-based identification of α-amylase inhibitor from the aerial part of Asparagus racemosus Willd

INTRODUCTION

α-Amylase inhibitors from natural sources are of interest for new drug development for the treatment of diabetes mellitus (DM). High-performance thin-layer chromatography (HPTLC) coupled bioassay guided isolation of bioactive compounds has been improved within last few years.

OBJECTIVE

A microchemical derivatised HPTLC-coupled attenuated total reflectance-Fourier-transform infrared (ATR-FTIR) and nuclear magnetic resonance (NMR) spectroscopy was employed for profiling α-amylase inhibitor from the aerial part of Asparagus racemosus Willd.

METHODOLOGY

Asparagus racemosus Willd. aerial part extracted with different solvents (n-hexane, chloroform, ethyl acetate, and methanol) and assayed to detect free radical scavengers and α-amylase inhibitor by 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay and starch-iodine assay method, respectively. HPTLC-coupled ATR-FTIR and NMR spectroscopy was used to identify the α-amylase inhibitor.

RESULTS

Methanolic extract of A. racemosus showed highest antioxidant activity (21.99 μg GAE/μL) where n-hexane extract showed lowest antioxidant activity (5.87 μg GAE/μL). The α-amylase inhibition was recorded as highest and lowest in ethyl acetate extract (13.13 AE/μL) and n-hexane extract (3.92 AE/μL), respectively. The deep blue zone of α-amylase sprayed TLC plate of extracts with hR_F  = 72 analysed for ATR-FTIR and NMR spectroscopy which revealed the presence of stigmasterol is responsible for α-amylase inhibition.

CONCLUSION

The present work establishes the α-amylase inhibiting properties of A. racemosus maintaining its use for the treatment of DM as a traditional medicine. Bioassay guided isolation through HPTLC-coupled ATR-FTIR and NMR spectroscopy offers an effective method for the exploration of bioactive compounds such as α-amylase inhibitor from complex plant extracts.

Recent Advances in Mass Spectrometry-Based Structural Elucidation Techniques

Mass spectrometry (MS) has become the central technique that is extensively used for the analysis of molecular structures of unknown compounds in the gas phase. It manipulates the molecules by converting them into ions using various ionization sources. With high-resolution MS, accurate molecular weights (MW) of the intact molecular ions can be measured so that they can be assigned a molecular formula with high confidence. Furthermore, the application of tandem MS has enabled detailed structural characterization by breaking the intact molecular ions and protonated or deprotonated molecules into key fragment ions. This approach is not only used for the structural elucidation of small molecules (MW < 2000 Da), but also crucial biopolymers such as proteins and polypeptides; therefore, MS has been extensively used in multiomics studies for revealing the structures and functions of important biomolecules and their interactions with each other. The high sensitivity of MS has enabled the analysis of low-level analytes in complex matrices. It is also a versatile technique that can be coupled with separation techniques, including chromatography and ion mobility, and many other analytical instruments such as NMR. In this review, we aim to focus on the technical advances of MS-based structural elucidation methods over the past five years, and provide an overview of their applications in complex mixture analysis. We hope this review can be of interest for a wide range of audiences who may not have extensive experience in MS-based techniques.

NMR-Based Configurational Assignments of Natural Products: How Floating Chirality Distance Geometry Calculations Simplify Gambling with 2N-1 Diastereomers

Using NMR data, the assignment of the correct 3D configuration and conformation to unknown natural products is of pivotal importance in pharmaceutical and medicinal chemistry. In this report, we quantify the probability of configurational assignments to judge the quality of structural elucidations using Bayesian inference in combination with floating-chirality distance geometry simulations. Based on reference-free NOE/ROE data, residual dipolar couplings (RDCs), and residual quadrupolar couplings (RQCs) in various combinations, we demonstrate how the relative configurations of three natural compounds, namely, jatrohemiketal (1), artemisinin (2), and Taxol (3), can be unambiguously established without the necessity to carry out time-consuming DFT-based configurational and conformational analyses. Our results quantitatively describe how reliably molecular geometries can be inferred from experimental NMR data, thereby unequivocally unveiling remaining assignment ambiguities. The methodology presented here will dramatically reduce the risk of incorrect structural assignments based on the overinterpretation of incomplete data and DFT-based structure models in chemistry.

Characterization of new psychoactive substances by integrating benchtop NMR to multi-technique databases

New psychoactive substances (NPS) have become a serious threat for public health due to their ability to be sold in the street or on internet. NPS are either derived from commercial drugs which are misused (recreational rather than medical use) or whose structure is slightly modified. To regulate NPS, it is essential to accurately characterize them, either to recognize molecules that were previously identified or to quickly elucidate the structure of unknown ones. Most approaches rely on the determination of the exact mass obtained by high‐resolution mass spectrometry requiring expensive equipment. This motivated us to develop a workflow in which the elucidation is assisted with databases and does not need the exact mass. This workflow combines 1D and 2D NMR measurements performed on a benchtop spectrometer with IR spectroscopy, for creating a multi‐technique database to characterize pure and mixed NPS. The experimental database was created with 57 entries mostly coming from seizures, mainly cathinones, cannabinoids, amphetamines, arylcyclohexylamines, and fentanyl. A blind validation of the workflow was carried out on a set of six unknown seizures. In the first three cases, AF, AB‐FUBINACA, and a mixture of 2C‐I and 2C‐E could be straightforwardly identified with the help of their reference spectra in the database. The two next samples were elucidated for the first time with the help of the database to reveal NEK and MPHP substances. Finally, a precise quantification of each characterized NPS was obtained in order to track NPS trafficking networks.

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

Deactivation of catalysts in simultaneous reversible and irreversible parahydrogen NMR signal enhancement, and the role of co-ligands in the stabilization of the reversible method

Signal Amplification by Reversible Exchange (SABRE) and hydrogeneable Parahydrogen Induced Polarization (hPHIP) can enhance weak NMR signals, and thus increase the range of NMR applications. Here, using an N-heterocyclic carbene Ir-based catalyst, simultaneous SABRE and hPHIP was achieved for the compound with an N-donor site and an acetylene triple bond. It was demonstrated that the interplay between SABRE and hPHIP can be manipulated. Specifically, it was found that the hPHIP effect could be almost completely suppressed, while stable SABRE was observed in subsequent consecutive experiments. The presented results have the potential to increase the numbers of parahydrogen hyperpolarizable molecules.

Studying Metabolism by NMR-Based Metabolomics

During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.

NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations

Floating chirality restrained distance geometry (fc-rDG) calculations are used to directly evolve structures from NMR data such as NOE-derived intramolecular distances or anisotropic residual dipolar couplings (RDCs). In contrast to evaluating pre-calculated structures against NMR restraints, multiple configurations (diastereomers) and conformations are generated automatically within the experimental limits. In this report, we show that the "unphysical" rDG pseudo energies defined from NMR violations bear statistical significance, which allows assigning probabilities to configurational assignments made that are fully compatible with the method of Bayesian inference. These "diastereomeric differentiabilities" then even become almost independent of the actual values of the force constants used to model the restraints originating from NOE or RDC data.

Cross-Modal Retrieval between 13C NMR Spectra and Structures for Compound Identification Using Deep Contrastive Learning

Library matching using carbon-13 nuclear magnetic resonance (¹³C NMR) spectra has been a popular method adopted in compound identification systems. However, the usability of existing approaches has been restricted as enlarging a library containing both a chemical structure and spectrum is a costly and time-consuming process. Therefore, we propose a fundamentally different, novel approach to match ¹³C NMR spectra directly against a molecular structure library. We develop a cross-modal retrieval between spectrum and structure (CReSS) system using deep contrastive learning, which allows us to search a molecular structure library using the ¹³C NMR spectrum of a compound. In the test of searching 41,494 ¹³C NMR spectra against a reference structure library containing 10.4 million compounds, CReSS reached a recall@10 accuracy of 91.64% and a processing speed of 0.114 s per query spectrum. When further incorporating a filter with a molecular weight tolerance of 5 Da, CReSS achieved a new remarkable recall@10 of 98.39%. Furthermore, CReSS has potential in detecting scaffolds of novel structures and demonstrates great performance for the task of structural revision. CReSS is built and developed to bridge the gap between ¹³C NMR spectra and structures and could be generally applicable in compound identification.

Construction of a simple dual-channel fluorescence chemosensor for Cu2+ ion and GSSG detection and its mitochondria-targeting bioimaging applications

Numerous chemosensors have been developed for next-generation detection systems because of their ease of use and promising characteristics to distinguish signals between various analytes binding. However, given their typically poor emission response and arduous preparation methods, very few chemosensing probes have been commercialized to date. In this work, a simple, naphthoquinone-based mitochondria-targeting chemosensor (CIA) has been fabricated for the simultaneous detection of Cu²⁺ and GSSG (glutathione oxidized) through an "on-off" mode in a buffered semi-aqueous solution. Significantly, the CIA chemosensor showed a sensitive detection response towards Cu²⁺ and GSSG with low detection limits (0.309 μM, and 0.226 μM, respectively). In addition, the detection mechanism of CIA was thoroughly verified and confirmed using numerous analytical techniques. Furthermore, CIA was utilized as a sequential fluorescence biomarker to detect Cu²⁺ in human cervical cancer cell lines. These findings indicate that the chemosensor CIA can discriminate human cancer cells from normal cells. The CIA was also confirmed to possess the ability to target mitochondria. More importantly, the present CIA chemosensor detected Cu²⁺ in zebrafish larvae, indicating the probe has tissue penetration ability.

Mitochondria-targeted acridine-based dual-channel fluorescence chemosensor for detection of Sn4+ and Cr2O72- ions in water and its application in discriminative detection of cancer cells

The goal of the present work was to fabricate a new low-cost, easy-to-prepare, dual-channel fluorescence chemosensor comprised of acridine-diphenylacetyl moieties (NDA) to enable remarkable Sn⁴⁺ detection in water and biological medium. The resulting NDA-Sn⁴⁺ complex was utilized for the distinguished identification of Cr₂O₇^2- ions from other anions and biomolecules. These investigations involve the absorption, fluorescence, and electrochemical methods for the detection of Sn⁴⁺ and Cr₂O₇^2- ions in pure water. The mechanism for NDA-mediated Sn⁴⁺ detection was experimentally determined by FT-IR, NMR titrations, mass (ESI) analyses, and DFT calculations. The obtained results indicate that the NDA chemosensor possessed excellent performance characteristics including good water solubility and compatibility, quick response time (less than 10 s), high sensitivity (Sn⁴⁺ = 0.268 μM and Cr₂O₇^2- = 0.160 μM), and selectivity against coexisting metals, anions, amino acids, and peptides. The chemosensor NDA induced negligible toxicity in live cells and was successfully utilized as a biomarker for the tracking of Sn⁴⁺ in human normal and cancer cells. More importantly, NDA demonstrates distinguished recognition of Sn⁴⁺ in human cancer cells rather than in normal live cells. Additionally, NDA was shown to act as a mitochondria-targeted probe in FaDu cells.

Roles and applications of enzymes for resistant pollutants removal in wastewater treatment

Resistant pollutants like oil, grease, pharmaceuticals, pesticides, and plastics in wastewater are difficult to be degraded by traditional activated sludge methods. These pollutants are prevalent, posing a great threat to aquatic environments and organisms since they are toxic, resistant to natural biodegradation, and create other serious problems. As a high-efficiency biocatalyst, enzymes are proposed for the treatment of these resistant pollutants. This review focused on the roles and applications of enzymes in wastewater treatment. It discusses the influence of enzyme types and their sources, enzymatic processes in resistant pollutants remediation, identification and ecotoxicity assay of enzymatic transformation products, and typically employed enzymatic wastewater treatment systems. Perspectives on the major challenges and feasible future research directions of enzyme-based wastewater treatment are also proposed.

Computer Assisted Structure Elucidation (CASE): Current and future perspectives

The first efforts for the development of methods for Computer-Assisted Structure Elucidation (CASE) were published more than 50 years ago. CASE expert systems based on one-dimensional (1D) and two-dimensional (2D) Nuclear Magnetic Resonance (NMR) data have matured considerably by now. The structures of a great number of complex natural products have been elucidated and/or revised using such programs. In this article, we discuss the most likely directions in which CASE will evolve. We act on the premise that a synergistic interaction exists between CASE, new NMR experiments, and methods of computational chemistry, which are continuously being improved. The new developments in NMR experiments (long-range correlation experiments, pure-shift methods, coupling constants measurement and prediction, residual dipolar couplings [RDCs]), and residual chemical shift anisotropies [RCSAs], evolution of density functional theory (DFT), and machine learning algorithms will have an influence on CASE systems and vice versa. This is true also for new techniques for chemical analysis (Atomic Force Microscopy [AFM], "crystalline sponge" X-ray analysis, and micro-Electron Diffraction [micro-ED]), which will be used in combination with expert systems. We foresee that CASE will be utilized widely and become a routine tool for NMR spectroscopists and analysts in academic and industrial laboratories. We believe that the "golden age" of CASE is still in the future.

The DEPTQ+ Experiment: Leveling the DEPT Signal Intensities and Clean Spectral Editing for Determining CHn Multiplicities

We propose a new ¹³C DEPTQ⁺ NMR experiment, based on the improved DEPTQ experiment, which is designed to unequivocally identify all carbon multiplicities (Cq, CH, CH₂, and CH₃) in two experiments. Compared to this improved DEPTQ experiment, the DEPTQ⁺ is shorter and the different evolution delays are designed as spin echoes, which can be tuned to different ¹J_CH values; this is especially valuable when a large range of ¹J_CH coupling constants is to be expected. These modifications allow (i) a mutual leveling of the DEPT signal intensities, (ii) a reduction in J cross-talk in the Cq/CH spectrum, and (iii) more consistent and cleaner CH₂/CH₃ edited spectra. The new DEPTQ⁺ is expected to be attractive for fast ¹³C analysis of small-to medium sized molecules, especially in high-throughput laboratories. With concentrated samples and/or by exploiting the high sensitivity of cryogenically cooled ¹³C NMR probeheads, the efficacy of such investigations may be improved, as it is possible to unequivocally identify all carbon multiplicities, with only one scan, for each of the two independent DEPTQ⁺ experiments and without loss of quality.

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

Model-Free Approach for the Configurational Analysis of Marine Natural Products

The NMR-based configurational analysis of complex marine natural products is still not a routine task. Different NMR parameters are used for the assignment of the relative configuration: NOE/ROE, homo- and heteronuclear J couplings as well as anisotropic parameters. The combined distance geometry (DG) and distance bounds driven dynamics (DDD) method allows a model-free approach for the determination of the relative configuration that is invariant to the choice of an initial starting structure and does not rely on comparisons with (DFT) calculated structures. Here, we will discuss the configurational analysis of five complex marine natural products or synthetic derivatives thereof: the cis-palau’amine derivatives 1a and 1b, tetrabromostyloguanidine (1c), plakilactone H (2), and manzamine A (3). The certainty of configurational assignments is evaluated in view of the accuracy of the NOE/ROE data available. These case studies will show the prospective breadth of application of the DG/DDD method.

Opportunities and Limitations for Assigning Relative Configurations of Antibacterial Bislactones using GIAO NMR Shift Calculations

Four new bislactones, dihydroacremonol (1), clonostachyone (2), acremodiol B (3), and acremodiol C (4), along with one known compound, hymeglusin (5), were isolated from cultures of two fungal strains (MSX59876 and MSX59260). Both strains were identified based on phylogenetic analysis of molecular data as Clonostachys spp.; yet, they biosynthesized a suite of related, but different, secondary metabolites. Given the challenges associated with elucidating the structures and configurations of bislactones, GIAO NMR calculations were tested as a complement to traditional NMR and HRESIMS experiments. Fortuitously, the enantiomer of the new natural product (4) was known as a synthetic compound, and the predicted configuration from GIAO NMR calculations (i.e., for the relative configuration) and optical rotation calculations (i.e., for the absolute configuration) matched those of the synthesis product. These results engendered confidence in using similar procedures, particularly the mixture of GIAO NMR shift calculations coupled with an orthogonal technique, to predict the configuration of 1-3; however, there were important limitations, which are discussed for each of these. The metabolites displayed antimicrobial activities, with compounds 1 and 4 being the most potent against Staphylococcus aureus with MICs of 1 and 4 μg/mL, respectively.

Database Independent Automated Structure Elucidation of Organic Molecules Based on IR, 1H NMR, 13C NMR, and MS Data

Herein, we report a computational algorithm that follows a spectroscopist-driven elucidation process of the structure of an organic molecule based on IR, 1H and 13C NMR, and MS tabular data. The algorithm is independent from database searching and is based on a bottom-up approach, building the molecular structure from small structural fragments visible in spectra. It employs an analytical combinatorial approach with a graph search technique to determine the connectivity of structural fragments that is based on the analysis of the NMR spectra, to connect the identified structural fragments into a molecular structure. After the process is completed, the interface lists the compound candidates, which are visualized by the WolframAlpha computational knowledge engine within the interface. The candidates are ranked according to the predefined rules for analyzing the spectral data. The developed elucidator has a user-friendly web interface and is publicly available (http://schmarnica.si).

Thielavins: tuned biosynthesis and LR-HSQMBC for structure elucidation

A series of thielavins I, V, and Q (1-3) and the previously undescribed thielavin Z₈ (4) were isolated from cultures of a fungal Shiraia-like sp. (strain MSX60519) that were grown under a suite of media and light conditions, with enhanced biosynthesis noted using rice as a substrate with 12:12 h light:dark cycles. Conversely, oatmeal medium and continuous white light-emitting diode light exposure negatively affected the production of these compounds, at least by strain MSX60519. The structure of 4 was determined using NMR spectroscopic data and mass fragmentation patterns. Of note, the utility of LR-HSQMBC and NOESY NMR experiments in the structural elucidation of these hydrogen-deficient natural products was demonstrated. Compounds 1-4 exhibited cytotoxic activity at the micromolar level against human breast, ovarian, and melanoma cancer cell lines.

Automated Methods for Identification and Quantification of Structural Groups from Nuclear Magnetic Resonance Spectra Using Support Vector Classification

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for elucidating the structure of unknown components and the composition of liquid mixtures. However, these tasks are often tedious and challenging, especially if complex samples are considered. In this work, we introduce automated methods for the identification and quantification of structural groups in pure components and mixtures from NMR spectra using support vector classification. As input, a ¹H NMR spectrum and a ¹³C NMR spectrum of the liquid sample (pure component or mixture) that is to be analyzed is needed. The first method, called group-identification method, yields qualitative information on the structural groups in the sample. The second method, called group-assignment method, provides the basis for a quantitative analysis of the sample by identifying the structural groups and assigning them to signals in the ¹³C NMR spectrum of the sample; quantitative information can then be obtained with readily available tools by simple integration. We demonstrate that both methods, after being trained to NMR spectra of nearly 1000 pure components, yield excellent predictions for pure components that were not part of the training set as well as mixtures. The structural group-specific information obtained with the presented methods can, e.g., be used in combination with thermodynamic group-contribution methods to predict fluid properties of unknown samples.

Simplifying LR-HSQC spectra using a triple-quantum filter: The LR-HTQC experiment

Long-range heteronuclear single quantum correlation (LR-HSQC) experiments may be applied for detecting long-range correlations but suffer from two disadvantages, common to all heteronuclear long-range correlation experiments: (i) The information density in LR-HSQC spectra may be too high to be used directly without "filtering out" shorter range correlations, and (ii) often, substantial differences in intensity among cross peaks exist, potentially hampering the visualization of weak, often crucial cross peaks. In this contribution, we propose a modified LR-HSQC experiment, the LR-HTQC experiment (Long-Range Heteronuclear Triple Quantum Correlation) that partially solves the problems aforementioned. We show theoretically and experimentally that the LR-HTQC experiment removes the intense cross peaks of CH spin pairs, substantially reduces the medium intensity of cross peaks originating from CHH' spin systems, whereas the typically weak intensity of cross peaks of CHH'H″ and C(H)_n, _{n > 3} spin systems is less affected. Consequently, the LR-HTQC experiment affords simplified long-range heteronuclear shift correlation spectra and scales down large intensity differences among different types of cross peaks, although a certain general reduction of signal intensities has to be accepted.

Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications

Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.

Salazinic Acid-Derived Depsidones and Diphenylethers with α-Glucosidase Inhibitory Activity from the Lichen Parmotrema dilatatum

Three new depsidones, parmosidones F - G (1 - 2), and 8'-O-methylsalazinic acid (3), and 3 new diphenylethers, parmetherines A - C (4 - 6), together with 2 known congeners were isolated from the whole thalli of Parmotrema dilatatum, a foliose chlorolichen. Their structures were unambiguously determined by extensive spectroscopic analyses and comparison with literature data. The isolated polyphenolics were assayed for their α-glucosidase inhibitory activities. Newly reported benzylated depsidones 1: and 2: in particular inhibited α-glucosidase with IC₅₀ values of 2.2 and 4.3 µM, respectively, and are thus more potent than the positive control, acarbose.

Induction of apoptosis in human bladder cancer cells by triterpenoids isolated from Holarrhena antidysenterica through differential reactive oxygen species generation

A novel triterpenoid, holarol(1),3β-lup-20(31)-en-3,29,30-triol along with one seco-triterpenoid, dihydrocanaric acid(2) and one known pentacyclic triterpenoid, betulin(3) have been isolated from Holarrhena antidysenterica (L.)Wall. (Family: Apocynaceae). The structures of the compounds were elucidated by extensive IR, 1D, 2D NMR and mass spectrometric analysis. The optimised geometry of (1) was calculated by density-functional theory (DFT) using M06-2X hybrid functional and 6-31 G(D) basis set. The compounds showed differential cytotoxic activities in the cell lines-HeLa, EAC, Raji and T24. Seco-triterpenoid (2) showed highest sensitivity (IC₅₀: 1.710 μg/mL) against the bladder cancer cell line T24 followed by (1) (IC₅₀ 9.698 µg/mL) and (3) (IC₅₀ 11.769 µg/mL). Compound (1) showed highest reactive oxygen species (ROS) generation in T24 cell line followed by (3) and (2) resulting in induction of apoptosis through activation of caspase, cleavage of PARP and reduction of Bcl-2/Bax ratio. Thus compounds (1), (2) along with (3) could be potent anticancer agents.

Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens

In this review, applications of various molecular modelling methods in the study of estrogens and xenoestrogens are summarized. Selected biomolecules that are the most commonly chosen as molecular modelling objects in this field are presented. In most of the reviewed works, ligand docking using solely force field methods was performed, employing various molecular targets involved in metabolism and action of estrogens. Other molecular modelling methods such as molecular dynamics and combined quantum mechanics with molecular mechanics have also been successfully used to predict the properties of estrogens and xenoestrogens. Among published works, a great number also focused on the application of different types of quantitative structure-activity relationship (QSAR) analyses to examine estrogen's structures and activities. Although the interactions between estrogens and xenoestrogens with various proteins are the most commonly studied, other aspects such as penetration of estrogens through lipid bilayers or their ability to adsorb on different materials are also explored using theoretical calculations. Apart from molecular mechanics and statistical methods, quantum mechanics calculations are also employed in the studies of estrogens and xenoestrogens. Their applications include computation of spectroscopic properties, both vibrational and Nuclear Magnetic Resonance (NMR), and also in quantum molecular dynamics simulations and crystal structure prediction. The main aim of this review is to present the great potential and versatility of various molecular modelling methods in the studies on estrogens and xenoestrogens.