Recent advances in the structure elucidation of small organic molecules by the LSD software

Twenty years of nmrshiftdb2: A case study of an open database for analytical chemistry

In October 2003, 20 years ago, the open-source and open-content database NMRshiftDB was announced. Since then, the database, renamed as nmrshiftdb2 later, has been continuously available and is one of the longer-running projects in the field of open data in chemistry. After 20 years, we evaluate the success of the project and present lessons learnt for similar projects.

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.

Deazaflavin cofactor boosts earthworms Henlea bioluminescence

The bioluminescence of Siberian earthworms Henlea sp. was found to be enhanced by two low molecular weight activators, termed ActH and ActS, found in the hot extracts. The fluorescence emission maximum of the activators matches the bioluminescence spectrum that peaks at 464 nm. We purified 4.3 and 8.8 micrograms of ActH and ActS from 200 worms and explored them using orbitrap HRMS with deep fragmentation and 1D/2D NMR equipped with cryoprobes. Their chemical structures were ascertained using chemical shift prediction services, structure elucidation software and database searches. ActH was identified as the riboflavin analoge archaeal cofactor F0, namely 7,8-didemethyl-8-hydroxy-5-deazariboflavin. ActS is a novel compound, namely ActH sulfated at the 3' ribityl hydroxyl. We designed and implemented a new four step synthesis strategy forActH that outperformed previous synthetic approaches. The synthetic ActH was identical to the natural one and activated Henlea sp. bioluminescence. The bioluminescence enhancement factor X was measured at different ActH concentrations and the Michaelis constant K_m = 0.22 ± 0.01 μM was obtained by nonlinear regression. At an excess of synthetic ActH, the factor X was saturated at X_max = 33.3 ± 0.5, thus opening an avenue to further characterisation of the Henlea sp. bioluminescence system. ActH did not produce bioluminescence without the luciferin with an as yet unknown chemical structure. We propose that ActH and the novel sulfated deazariboflavin ActS either emit the light of the Henlea sp. bioluminescence and/or accept hydride(s) donor upon luciferin oxidation.

Identifying molecular functional groups of organic compounds by deep learning of NMR data

We preprocess the raw nuclear magnetic resonance (NMR) spectrum and extract key features by using two different methodologies, called equidistant sampling and peak sampling for subsequent substructure pattern recognition. We also provide a strategy to address the imbalance issue frequently encountered in statistical modeling of NMR data set and establish two conventional support vector machine (SVM) and K-nearest neighbor (KNN) models to assess the capability of two feature selections, respectively. Our results in this study show that the models using the selected features of peak sampling outperform those using equidistant sampling. Then we build the recurrent neural network (RNN) model trained by data collected from peak sampling. Furthermore, we illustrate the easier optimization of hyperparameters and the better generalization ability of the RNN deep learning model by detailed comparison with traditional machine learning SVM and KNN models.

Parallel NMR Supersequences: Ten Spectra in a Single Measurement

The principles employed in parallel NMR and MRI are applied to NMR supersequences yielding as many as ten 2D NMR spectra in one measurement. We present a number of examples where two NOAH (NMR by Ordered Acquisition using 1H-detection) supersequences are recorded in parallel, thus dramatically increasing the information content obtained in a single NMR experiment. The two parallel supersequences entangled by time-sharing schemes (IPAP-seHSQC, HSQC-COSY, and HSQC-TOCSY) incorporate also modified (sequential and/or interleaved) conventional pulse schemes (modules), including HMBC, TOCSY, COSY, CLIP-COSY, NOESY, and ROESY. Such parallel supersequences can be tailored for specific applications, for instance, the analysis and characterization of molecular structure of complex organic molecules from a single measurement. In particular, the CASPER software was used to establish the structure of a tetrasaccharide, β-LNnTOMe, with a high degree of confidence from a single measurement involving a parallel NOAH-5 supersequence.

NMReDATA: Tools and applications

The nuclear magnetic resonance extracted data (NMReDATA) format has been proposed as a way to store, exchange, and disseminate nuclear magnetic resonance (NMR) data and physical and chemical metadata of chemical compounds. In this paper, we report on analytical workflows that take advantage of the uniform and standardized NMReDATA format. We also give access to a repository of sample data, which can serve for validating software packages that encode or decode files in NMReDATA format.

Computational methods for NMR and MS for structure elucidation III: More advanced approaches

The structural assignment of natural products, even with the very sophisticated one-dimensional and two-dimensional (1D and 2D) spectroscopic methods available today, is still a tedious and time-consuming task. Mass spectrometry (MS) is generally used for molecular mass determination, molecular formula generation and MS/MSn fragmentation patterns of molecules. In the meantime, nuclear magnetic resonance (NMR) spectroscopy provides spectra (e. g. 1 H, 13C and correlation spectra) whose interpretation allows the structure determination of known or unknown compounds. With the advance of high throughput studies, like metabolomics, the fast and automated identification or annotation of natural products became highly demanded. Some growing tools to meet this demand apply computational methods for structure elucidation. These methods act on characteristic parameters in the structural determination of small molecules. We have numbered and herein present existing and reputed computational methods for peak picking analysis, resonance assignment, nuclear Overhauser effect (NOE) assignment, combinatorial fragmentation and structure calculation and prediction. Fully automated programs in structure determination are also mentioned, together with their integrated algorithms used to elucidate the structure of a metabolite. The use of these automated tools has helped to significantly reduce errors introduced by manual processing and, hence, accelerated the structure identification or annotation of compounds.

The role of computer-assisted structure elucidation (CASE) programs in the structure elucidation of complex natural products

Computer-assisted structure elucidation can help to determine the structures of complex natural products while minimizing the risk of structure errors.

Computational Methodologies in the Exploration of Marine Natural Product Leads

Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.

Identification of tentative marker in Corvina and Primitivo wines with CMC-se

This paper introduces CMC-se-a program for computer-assisted structure elucidation. In the experimental part, the combination of modern analytical methods (LC-SPE-NMR/MS) and structure elucidation software is used for the identification of tentative markers in red wines.

Tutorial for the structure elucidation of small molecules by means of the LSD software

Automatic structure elucidation of small molecules by means of the "logic for structure elucidation" (LSD) software is introduced in the context of the automatic exploitation of chemical shift correlation data and with minimal input from chemical shift values. The first step in solving a structural problem by means of LSD is the extraction of pertinent data from the 1D and 2D spectra. This operation requires the labeling of the resonances and of their correlations; its reliability highly depends on the quality of the spectra. The combination of COSY, HSQC, and HMBC spectra results in proximity relationships between nonhydrogen atoms that are associated in order to build the possible solutions of a problem. A simple molecule, camphor, serves as an example for the writing of an LSD input file and to show how solution structures are obtained. An input file for LSD must contain a nonambiguous description of each atom, or atom status, which includes the chemical element symbol, the hybridization state, the number of bound hydrogen atoms and the formal electric charge. In case of atom status ambiguity, the pyLSD program performs clarification by systematically generating the status of the atoms. PyLSD also proposes the use of the nmrshiftdb algorithm in order to rank the solutions of a problem according to the quality of the fit between the experimental carbon-13 chemical shifts, and the ones predicted from the proposed structures. To conclude, some hints toward future uses and developments of computer-assisted structure elucidation by LSD are proposed.

Computer-aided Dereplication and Structure Elucidation of Natural Products at the University of Reims

Natural product chemistry began in Reims, France, in a pharmacognosy research laboratory whose main emphasis was the isolation and identification of bioactive molecules, following the guidelines of chemotaxonomy. The structure elucidation of new compounds of steadily increasing complexity favored the emergence of methodological work in nuclear magnetic resonance. As a result, our group was the first to report the use of proton-detected heteronuclear chemical shift correlation spectra for the computer-assisted structure elucidation of small organic molecules driven by atom proximity relationships and without relying on databases. The early detection of known compounds appeared as a necessity in order to deal more efficiently with complex plant extracts. This goal was reached by an original combination of mixture fractionation by centrifugal partition chromatography, analysis by ¹³ C NMR, digital data reduction and alignment, hierarchical data clustering, and computer database search.

Human- and computer-accessible 2D correlation data for a more reliable structure determination of organic compounds. Future roles of researchers, software developers, spectrometer managers, journal editors, reviewers, publisher and database managers toward artificial-intelligence analysis of NMR spectra

The introduction of a universal data format to report the correlation data of 2D NMR spectra such as COSY, HSQC and HMBC spectra will have a large impact on the reliability of structure determination of small organic molecules. These lists of assigned cross peaks will bridge signals found in NMR 1D and 2D spectra and the assigned chemical structure. The record could be very compact, human and computer readable so that it can be included in the supplementary material of publications and easily transferred into databases of scientific literature and chemical compounds. The records will allow authors, reviewers and future users to test the consistency and, in favorable situations, the uniqueness of the assignment of the correlation data to the associated chemical structures. Ideally, the data format of the correlation data should include direct links to the NMR spectra to make it possible to validate their reliability and allow direct comparison of spectra. In order to take the full benefits of their potential, the correlation data and the NMR spectra should therefore follow any manuscript in the review process and be stored in open-access database after publication. Keeping all NMR spectra, correlation data and assigned structures together at all time will allow the future development of validation tools increasing the reliability of past and future NMR data. This will facilitate the development of artificial intelligence analysis of NMR spectra by providing a source of data than can be used efficiently because they have been validated or can be validated by future users. Copyright © 2016 John Wiley & Sons, Ltd.

Characterization and Synthesis of Eudistidine C, a Bioactive Marine Alkaloid with an Intriguing Molecular Scaffold

An extract of Eudistoma sp. provided eudistidine C (1), a heterocyclic alkaloid with a novel molecular framework. Eudistidine C (1) is a racemic natural product composed of a tetracyclic core structure further elaborated with a p-methoxyphenyl group and a phenol-substituted aminoimidazole moiety. This compound presented significant structure elucidation challenges due to the large number of heteroatoms and fully substituted carbons. These issues were mitigated by application of a new NMR pulse sequence (LR-HSQMBC) optimized to detect four- and five-bond heteronuclear correlations and the use of computer-assisted structure elucidation software. Synthesis of eudistidine C (1) was accomplished in high yield by treating eudistidine A (2) with 4(2-amino-1H-imidazol-5-yl)phenol (4) in DMSO. Synthesis of eudistidine C (1) confirmed the proposed structure and provided material for further biological characterization. Treatment of 2 with various nitrogen heterocycles and electron-rich arenes provided a series of analogues (5-10) of eudistidine C. Chiral-phase HPLC resolution of epimeric eudistidine C provided (+)-(R)-eudistidine C (1a) and (-)-(S)-eudistidine C (1b). The absolute configuration of these enantiomers was assigned by ECD analysis. (-)-(S)-Eudistidine C (1b) modestly inhibited interaction between the protein binding domains of HIF-1α and p300. Compounds 1, 2, and 6-10 exhibited significant antimalarial activity against Plasmodium falciparum.

"Ask Ernö": a self-learning tool for assignment and prediction of nuclear magnetic resonance spectra

BACKGROUND

We present "Ask Ernö", a self-learning system for the automatic analysis of NMR spectra, consisting of integrated chemical shift assignment and prediction tools. The output of the automatic assignment component initializes and improves a database of assigned protons that is used by the chemical shift predictor. In turn, the predictions provided by the latter facilitate improvement of the assignment process. Iteration on these steps allows Ask Ernö to improve its ability to assign and predict spectra without any prior knowledge or assistance from human experts.

RESULTS

This concept was tested by training such a system with a dataset of 2341 molecules and their (1)H-NMR spectra, and evaluating the accuracy of chemical shift predictions on a test set of 298 partially assigned molecules (2007 assigned protons). After 10 iterations, Ask Ernö was able to decrease its prediction error by 17 %, reaching an average error of 0.265 ppm. Over 60 % of the test chemical shifts were predicted within 0.2 ppm, while only 5 % still presented a prediction error of more than 1 ppm.

CONCLUSIONS

Ask Ernö introduces an innovative approach to automatic NMR analysis that constantly learns and improves when provided with new data. Furthermore, it completely avoids the need for manually assigned spectra. This system has the potential to be turned into a fully autonomous tool able to compete with the best alternatives currently available.Graphical abstractSelf-learning loop. Any progress in the prediction (forward problem) will improve the assignment ability (reverse problem) and vice versa.

Exploiting the Complementarity between Dereplication and Computer-Assisted Structure Elucidation for the Chemical Profiling of Natural Cosmetic Ingredients: Tephrosia purpurea as a Case Study

The aqueous-ethanolic extract of Tephrosia purpurea seeds is currently exploited in the cosmetic industry as a natural ingredient of skin lotions. The aim of this study was to chemically characterize this ingredient by combining centrifugal partition extraction (CPE) as a fractionation tool with two complementary identification approaches involving dereplication and computer-assisted structure elucidation. Following two rapid fractionations of the crude extract (2 g), seven major compounds namely, caffeic acid, quercetin-3-O-rutinoside, ethyl galactoside, ciceritol, stachyose, saccharose, and citric acid, were unambiguously identified within the CPE-generated simplified mixtures by a recently developed (13)C NMR-based dereplication method. The structures of four additional compounds, patuletin-3-O-rutinoside, kaempferol-3-O-rutinoside, guaiacylglycerol 8-vanillic acid ether, and 2-methyl-2-glucopyranosyloxypropanoic acid, were automatically elucidated by using the Logic for Structure Determination program based on the interpretation of 2D NMR (HSQC, HMBC, and COSY) connectivity data. As more than 80% of the crude extract mass was characterized without need for tedious and labor-intensive multistep purification procedures, the identification tools involved in this work constitute a promising strategy for an efficient and time-saving chemical profiling of natural extracts.