Optimized experimental workflow for tandem mass spectrometry molecular networking in metabolomics

Feature-Based Molecular Network for New Psychoactive Substance Identification: The Case of Synthetic Cannabinoids in a Seized e-Liquid and Biological Samples

The comprehensive detection of new psychoactive substances, including synthetic cannabinoids along with their associated metabolites in biological samples, remains an analytical challenge. To detect these chemicals, untargeted approaches using appropriate bioinformatic tools such as molecular networks are useful, albeit it necessitates as a prerequisite the identification of a node of interest within the cluster. To illustrate it, we reported in this study the identification of synthetic cannabinoids and some of their metabolites in seized e-liquid, urine, and hair collected from an 18-year-old poisoned patient hospitalized for neuropsychiatric disorders. A comprehensive analysis of the seized e-liquid was performed using gas chromatography coupled with electron ionization mass spectrometry, 1H NMR, and liquid chromatography coupled with high resolution tandem mass spectrometry combined with data processing based on molecular network strategy. It allowed researchers to detect in the e-liquid known synthetic cannabinoids including MDMB-4en-PINACA, EDMB-4en-PINACA, MMB-4en-PINACA, and MDMB-5F-PICA. Compounds corresponding to transesterification of MDMB-4en-PINACA with pentenol, glycerol, and propylene glycol were also identified. Regarding the urine sample of the patient, metabolites of MDMB-4en-PINACA were detected, including MDMB-4en-PINACA butanoic acid, dihydroxylated MDMB-4en-PINACA butanoic acid, and glucurono-conjugated MDMB-4en-PINACA butanoic acid. Hair analysis of the patient allowed the detection of MDMB-4en-PINACA and MDMB-5F-PICA in the two investigated hair segments. This untargeted analysis of seized materials and biological samples demonstrates the utility of the molecular network strategy in identifying closely related compounds and metabolites of synthetic cannabinoids. It also emphasizes the need for developing strategies to anchor molecular networks, especially for new psychoactive substances.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Metabolomics as a Prospective Tool for Soybean (Glycine max) Crop Improvement

Global demand for soybean and its products has stimulated research into the production of novel genotypes with higher yields, greater drought and disease tolerance, and shorter growth times. Genetic research may be the most effective way to continue developing high-performing cultivars with desirable agronomic features and improved nutritional content and seed performance. Metabolomics, which predicts the metabolic marker for plant performance under stressful conditions, is rapidly gaining interest in plant breeding and has emerged as a powerful tool for driving crop improvement. The development of increasingly sensitive, automated, and high-throughput analytical technologies, paired with improved bioinformatics and other omics techniques, has paved the way for wide characterization of genetic characteristics for crop improvement. The combination of chromatography (liquid and gas-based) with mass spectrometry has also proven to be an indisputable efficient platform for metabolomic studies, notably plant metabolic fingerprinting investigations. Nevertheless, there has been significant progress in the use of nuclear magnetic resonance (NMR), capillary electrophoresis, and Fourier-transform infrared spectroscopy (FTIR), each with its own set of benefits and drawbacks. Furthermore, utilizing multivariate analysis, principal components analysis (PCA), discriminant analysis, and projection to latent structures (PLS), it is possible to identify and differentiate various groups. The researched soybean varieties may be correctly classified by using the PCA and PLS multivariate analyses. As metabolomics is an effective method for evaluating and selecting wild specimens with desirable features for the breeding of improved new cultivars, plant breeders can benefit from the identification of metabolite biomarkers and key metabolic pathways to develop new genotypes with value-added features.

Mapping Molecular Networks within Clitoria ternatea Linn. against LPS-Induced Neuroinflammation in Microglial Cells, with Molecular Docking and In Vivo Toxicity Assessment in Zebrafish

Clitoria ternatea Linn. (CT), or butterfly pea, is an Ayurvedic plant traditionally used as a brain tonic. Recently, it was reported to be of use in treating central nervous system (CNS) disorders, i.e., as an antistress treatment and antidepressant. In the present study, we report a detailed phytochemical profile of the ethyl acetate fraction of the flower of CT (CTF_EA) with significant neuroprotective and anti-neuroinflammatory properties in both LPS-activated BV-2 and SK-N-SH cells. Concurrently, the molecular network (MN) derived from the CTF_EA metabolome allows putative identification of flavonol 3-O-glycosides, hydrocinnamic acids, and primary metabolites. Molecular docking studies suggest that CTF_EA preferentially targets iNOS, resulting in a decrease in nitric oxide (NO). Furthermore, no toxic effects on normal embryonic development, blood vessel formation, and apoptosis are observed when CTF_EA is tested for in vivo toxicity in zebrafish models. The overall preliminary results suggest the anti-neuroinflammatory and neuroprotective effects of CT and provide scientific support for the efficacy of this medicinal plant at local and traditional levels. However, studies on the targeted isolation of bioactive metabolites, in-depth pharmacological efficacy, and safety in mammalian models are urgently needed to expand our understanding of this plant before it is developed into a promising therapeutic agent for brain-related diseases.

Optimization of LC-MS2 Data Acquisition Parameters for Molecular Networking Applied to Marine Natural Products

Since the introduction of the online open-source GNPS, molecular networking has quickly become a widely applied tool in the field of natural products chemistry, with applications from dereplication, genome mining, metabolomics, and visualization of chemical space. Studies have shown that data dependent acquisition (DDA) parameters affect molecular network topology but are limited in the number of parameters studied. With an aim to optimize LC-MS² parameters for integrating GNPS-based molecular networking into our marine natural products workflow, a design of experiment (DOE) was used to screen the significance of the effect that eleven parameters have on both Classical Molecular Networking workflow (CLMN) and the new Feature-Based Molecular Networking workflow (FBMN). Our results indicate that four parameters (concentration, run duration, collision energy and number of precursors per cycle) are the most significant data acquisition parameters affecting the network topology. While concentration and the LC duration were found to be the two most important factors to optimize for CLMN, the number of precursors per cycle and collision energy were also very important factors to optimize for FBMN.

Enhanced detection and annotation of small molecules in metabolomics using molecular-network-oriented parameter optimization

Metabolomics, especially the large-scale untargeted metabolomics, generates massive amounts of data on a regular basis, which often needs to be filtered, screened, analyzed and annotated via a variety of approaches. Data-dependent-acquisition (DDA) mode including inclusion and exclusion rules for tandem mass spectrometry (MS) is routinely used to perform such analyses. While the parameters of data acquisition are important in these processes, there is a lack of systematic studies on these parameters that can be used in data collection to generate metabolic features for molecular-network (MN) analysis on the Global Natural Products Social Molecular Networking (GNPS) platform. To explore the key parameters that impact the formation and quality of MNs, several data-acquisition parameters for metabolomic studies were proposed in this study. The influences of MS1 resolution, normalized collision energy (NCE), intensity threshold, and exclusion time on GNPS analyses were demonstrated. Moreover, an optimization workflow dedicated to Thermo Scientific QE Hybrid Orbitrap instruments is described, and a comparison of phytochemical contents from two forms of black raspberry extract was performed based on the GNPS MN results. Overall, we expect this study to provide additional thoughts on developing a natural-product-analysis workflow using the GNPS network, and to shed some light on future analyses that utilize similar instrumental setups.

Molecular-networking-guided discovery of species-specific markers for discriminating five medicinal Paris herbs

BACKGROUND

Paridis Rhizoma (PR) is a famous traditional herbal medicine. Apart from two officially recorded species, viz. Paris polyphylla Smith var. yunnanensis (Franch.) Hand. - Mazz. (PPY) and P. polyphylla Smith var. chinensis (Franch.) Hara (PPC), there are still many other species used as folk medicine. It is necessary to understand the metabolic differences among Paris species.

PURPOSE

To establish a strategy that can discover species-specific steroidal saponin markers to distinguish closely-related Paris herbs for quality and safety control.

METHODS

A new strategy of molecular-networking-guided discovery of species-specific markers was proposed. Firstly, the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was applied to obtain the MS and MS/MS data of all samples. Then, molecular networking (MN) was created using MS/MS data to prescreen the steroidal saponins for subsequent analysis. Next, the principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA) models were established to discover potential markers. Finally, the verification, identification and distribution of chemical markers were performed.

RESULTS

A total of 126 steroidal saponins were screened out from five species using MN. Five species were classified successfully by OPLS-DA model, and 18 species-specific markers were discovered combining the variable importance in the projection (VIP) value, P value (one-way ANOVA) and their relative abundance. These markers could predict the species of Paris herbs correctly.

CONCLUSION

These results revealed that this new strategy could be an efficient way for chemical discrimination of medicinal herbs with close genetic relationship.

Molecular networking as a novel approach to unravel toxin diversity of four strains of the dominant Dinophysis species from French coastal waters

Some species of the genus Dinophysis contain Diarrhetic shellfish Poisoning (DSP) toxins and are the main threat to shellfish farming in Europe including France. Dinophysis species are known to produce two families of bioactive lipophilic toxins: (i) okadaic acid (OA) and their analogues dinophysistoxins (DTXs) and (ii) pectenotoxins (PTXs). Only six toxins (OA, DTX1, DTX2, DTX3, PTX1 and PTX2) regulated by the European Union Legislation (EC No. 15/2011; 3) are routinely monitored using targeted chemical analysis by liquid chromatography coupled to mass spectrometry (LC-MS/MS) while toxic species of Dinophysis produce many other analogues. To tentatively identify unknown toxin analogues, a recent approach (Molecular Networking, MN) was used based on fragmentation data obtained by untargeted high resolution mass spectrometry (HRMS). An optimization of the data-dependent LC-HRMS/MS acquisition conditions was conducted to obtain more informative networks. The MN was applied to provide an overview of the chemical diversity of four strains belonging to three major Dinophysis species isolated from French coastal waters (D. acuta, D. caudata and the "D. acuminata complex" species D. acuminata and D. sacculus). This approach highlighted species-specific chemical patterns and also that Dinophysis chemical diversity is largely unexplored. Using MN allowed to identify directly known toxins and their relationship between species of Dinophysis, leading to the discovery of five new putative PTX analogues.

Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.

Lipid Annotation by Combination of UHPLC-HRMS (MS), Molecular Networking, and Retention Time Prediction: Application to a Lipidomic Study of In Vitro Models of Dry Eye Disease

Annotation of lipids in untargeted lipidomic analysis remains challenging and a systematic approach needs to be developed to organize important datasets with the help of bioinformatic tools. For this purpose, we combined tandem mass spectrometry-based molecular networking with retention time (t_R) prediction to annotate phospholipid and sphingolipid species. Sixty-five standard compounds were used to establish the fragmentation rules of each lipid class studied and to define the parameters governing their chromatographic behavior. Molecular networks (MNs) were generated through the GNPS platform using a lipid standards mixture and applied to lipidomic study of an in vitro model of dry eye disease, i.e., human corneal epithelial (HCE) cells exposed to hyperosmolarity (HO). These MNs led to the annotation of more than 150 unique phospholipid and sphingolipid species in the HCE cells. This annotation was reinforced by comparing theoretical to experimental t_R values. This lipidomic study highlighted changes in 54 lipids following HO exposure of corneal cells, some of them being involved in inflammatory responses. The MN approach coupled to t_R prediction thus appears as a suitable and robust tool for the discovery of lipids involved in relevant biological processes.

Mass spectrometry-based metabolomics approach in the isolation of bioactive natural products

Metabolomics is a powerful tool in the analysis and identification of metabolites responsible for biological properties. Regarding natural product chemistry, it constitutes a potential strategy to streamline the classic and laborious process of isolating natural products, which often involves the re-isolation and identification of known compounds. In this contribution, we establish a mass spectrometry-based metabolomics strategy to discover compounds with larvicidal activity against Aedes aegypti. We analyse the Brazilian plant Annona crassiflora using different platforms to annotate the active compounds in different extracts/fractions of various plant parts. The MetaboAnalyst and GNPS platforms, which consider LC-MS and LC-MS/MS data, respectively, were chosen to identify compounds that differentiate active and inactive samples. Bio-guided isolation was subsequently performed to confirm compound activity. Results proved the capacity of metabolomics to predict metabolite differences between active and inactive samples using LC-MS and LC-MS/MS data. Moreover, we discuss the limitations, possibilities, and strategies to have a broad view of vast data.

Unraveling Asian Soybean Rust metabolomics using mass spectrometry and Molecular Networking approach

Asian Soybean Rust (ASR), caused by the biotrophic fungus Phakopsora pachyrhizi, is a devastating disease with an estimated crop yield loss of up to 90%. Yet, there is a nerf of information on the metabolic response of soybean plants to the pathogen Untargeted metabolomics and Global Natural Products Social Molecular Networking platform approach was used to explore soybean metabolome modulation to P. pachyrhizi infection. Soybean plants susceptible to ASR was inoculated with P. pachyrhizi spore suspension and non-inoculated plants were used as controls. Leaves from both groups were collected 14 days post-inoculation and extracted using different extractor solvent mixtures. The extracts were analyzed on an ultra-high performance liquid chromatography system coupled to high-definition electrospray ionization-mass spectrometry. There was a significant production of defense secondary metabolites (phenylpropanoids, terpenoids and flavonoids) when P. pachyrhizi infected soybean plants, such as putatively identified liquiritigenin, coumestrol, formononetin, pisatin, medicarpin, biochanin A, glyoceollidin I, glyoceollidin II, glyoceollin I, glyoceolidin II, glyoceolidin III, glyoceolidin IV, glyoceolidin VI. Primary metabolites (amino acids, peptides and lipids) also were putatively identified. This is the first report using untargeted metabolomics and GNPS-Molecular Networking approach to explore ASR in soybean plants. Our data provide insights into the potential role of some metabolites in the plant resistance to ASR, which could result in the development of resistant genotypes of soybean to P. pachyrhizi, and effective and specific products against the pathogen.

Current status and contemporary approaches to the discovery of antitumor agents from higher plants

Higher plant constituents have afforded clinically available anticancer drugs. These include both chemically unmodified small molecules and their synthetic derivatives currently used or those in clinical trials as antineoplastic agents, and an updated summary is provided. In addition, botanical dietary supplements, exemplified by mangosteen and noni constituents, are also covered as potential cancer chemotherapeutic agents. Approaches to metabolite purification, rapid dereplication, and biological evaluation including analytical hyphenated techniques, molecular networking, and advanced cellular and animal models are discussed. Further, enhanced and targeted drug delivery systems for phytochemicals, including micelles, nanoparticles and antibody drug conjugates (ADCs) are described herein.

Natural products targeting strategies involving molecular networking: different manners, one goal

Covering: up to 2019Landmark advances in bioinformatics tools have recently enhanced the field of natural products research, putting today's natural product chemists in the enviable position of being able to perform the efficient targeting/discovery of previously undescribed molecules by expediting the prioritization of the isolation workflow. Among these advances, MS/MS molecular networking has appeared as a promising approach to dereplicate complex natural product mixtures, leading to a real revolution in the "art of natural product isolation" by accelerating the pace of research of this field. This review illustrates through selected cornerstone studies the new thinking in natural product isolation by drawing a parallel between the different underlying philosophies behind the use of molecular networking in targeting natural products.

MS-Based Molecular Networking of Designer Drugs as an Approach for the Detection of Unknown Derivatives for Forensic and Doping Applications: A Case of NBOMe Derivatives

The NBOMe family is a group of new psychoactive substances (NPSs). In this study, the fragmentation patterns of NBOMe derivatives were analyzed using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF/MS). The MS/MS spectral data was used to establish a molecular networking map for NBOMe derivatives. The fragmentation patterns of nine NBOMe derivatives were interpreted on the basis of their product ion spectral data. NBOMe derivatives generally showed similar product ion spectral patterns; among them, the halogen-substituted methoxybenzyl ethanamine type derivatives showed a characteristic product ion of a radical cation. Molecular network analysis of the MS/MS data revealed that all NBOMe derivatives formed one integrated networking cluster that discriminated them from other types of NPSs. NBOMe derivatives were spiked into human urine and identified by connection to the NBOMe database network. Furthermore, the NBOMe compounds that were not registered in the database were also recognized as an NBOMe-related substance by molecular networking. These results demonstrate the potential of using molecular networking-based screening methods for designer drugs, and the proposed method would be useful in forensic or doping analysis.

Extending compound identification for molecular network using the LipidXplorer database independent method: A proof of concept using glycoalkaloids from Solanum pseudoquina A. St.-Hil

INTRODUCTION

Molecular networks are now established as the method of choice for tandem mass spectrometry dereplication and similarity-based structure elucidation. Node identification can be used to start the propagation of the structure elucidation of unknown compounds progressively.

OBJECTIVE

To demonstrate the capabilities of using the LipidXplorer data results along with molecular networking to identify nodes and aid sequential structure elucidation of unknown compounds.

MATERIAL AND METHODS

Molecular fragmentation query language (MFQL) files were written to identify glycoalkaloids based on known structures described for Solanum species. A dataset generated from liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis of Solanum pseudoquina sample were submitted to dereplication on both LipidXplorer software and Global Natural Products Social Molecular Network (GNPS) online system. The resulting attribute table from GNPS calculations was merged with the LipidXplorer results and this merged file was used for network visualisation in Cytoscape. Nodes in the molecular network were labelled using the LipidXplorer identifiers, thus assisting the structure elucidation of unidentified compounds.

RESULTS

The combination of the LipidXplorer glycoalkaloids list and GNPS analysis was used in Cytoscape to label nodes in the molecular network. The analysis of the network using these labelled starting points triggered the structure elucidation of closely related nodes leading to the identification of 30 compounds using the LipidXplorer output and four purified and structure elucidated compounds, including a new glycoalkaloids identified as 3-O-(β-d-xylopyranosyl)-(20R,25S)-22,26-epimino-16-acetyl-cholesta-5,22(N)-diene.

CONCLUSION

A significant compound identification completely based on molecular formula and fragmentation queries was achieved. This new and effective approach could help researches to expand the identification rate of compounds in dereplication studies using molecular networks.

Antiviral Compounds from Codiaeum peltatum Targeted by a Multi-informative Molecular Networks Approach

From a set of 292 Euphorbiaceae extracts, the use of a molecular networking (MN)-based prioritization approach highlighted three clusters (MN1-3) depicting ions from the bark extract of Codiaeum peltatum. Based on their putative antiviral potential and structural novelty, the MS-guided purification of compounds present in MN1 and MN2 afforded two new daphnane-type diterpenoid orthoesters (DDO), codiapeltines A (1) and B (2), the new actephilols B (3) and C (4), and four known 1,4-dioxane-fused phenanthrene dimers (5-8). The structures of the new compounds were elucidated by NMR spectroscopic data analysis, and the absolute configurations of compounds 1 and 2 were deduced by comparison of experimental and calculated ECD spectra. Codiapeltine B (2) is the first daphnane bearing a 9,11,13-orthoester moiety, establishing a new major structural class of DDO. Compounds 1-8 and four recently reported monoterpenyl quinolones (9-12) detected in MN3 were investigated for their selective activities against chikungunya virus replication and their antipolymerase activities against the NS5 proteins of dengue and zika viruses. Compounds 3-8 exhibited strong inhibitory activities on both dengue and zika NS5 in primary assays, but extensive biological analyses indicated that only actephilol B (3) displayed a specific interaction with the NS5 targets.

Metabolomics-Driven Discovery of Meroterpenoids from a Mussel-Derived Penicillium ubiquetum

Penicillium ubiquetum MMS330 isolated from the blue mussel Mytilus edulis collected on the Loire estuary in France was here investigated. As very few secondary metabolites have been documented for this species, its metabolome was studied following the OSMAC approach to enhance as many biosynthetic pathways as possible. Interestingly, HPLC-HRMS based hierarchical clustering analysis together with MS/MS molecular networking highlighted the selective overproduction of some structurally related compounds when the culture was performed on seawater CYA (Czapek Yeast extract Agar) medium. Mass-guided purification from large scale cultivation on this medium led to the isolation of nine meroterpenoids including two new analogues, 22-deoxyminiolutelide A (1) and 4-hydroxy-22-deoxyminiolutelide B (2), together with seven known compounds (3-9). The structures of 1 and 2 were elucidated on the basis of HR-ESIMS and NMR spectroscopic data analysis. Furthermore, NMR signals of 22-deoxyminiolutelide B (3) were reassigned.

MetGem Software for the Generation of Molecular Networks Based on the t-SNE Algorithm

Molecular networking (MN) is becoming a standard bioinformatics tool in the metabolomic community. Its paradigm is based on the observation that compounds with a high degree of chemical similarity share comparable MS² fragmentation pathways. To afford a clear separation between MS² spectral clusters, only the most relevant similarity scores are selected using dedicated filtering steps requiring time-consuming parameter optimization. Depending on the filtering values selected, some scores are arbitrarily deleted and a part of the information is ignored. The problem of creating a reliable representation of MS² spectra data sets can be solved using algorithms developed for dimensionality reduction and pattern recognition purposes, such as t-distributed stochastic neighbor embedding (t-SNE). This multivariate embedding method pays particular attention to local details by using nonlinear outputs to represent the entire data space. To overcome the limitations inherent to the GNPS workflow and the networking architecture, we developed MetGem. Our software allows the parallel investigation of two complementary representations of the raw data set, one based on a classic GNPS-style MN and another based on the t-SNE algorithm. The t-SNE graph preserves the interactions between related groups of spectra, while the MN output allows an unambiguous separation of clusters. Additionally, almost all parameters can be tuned in real time, and new networks can be generated within a few seconds for small data sets. With the development of this unified interface ( https://metgem.github.io ), we fulfilled the need for a dedicated, user-friendly, local software for MS² comparison and spectral network generation.

Ribeiro FW, Cervi G, C. Correra T. Evaluation of Common Theoretical Methods for Predicting Infrared Multiphotonic Dissociation Vibrational Spectra of Intramolecular Hydrogen-Bonded Ions

Infrared photodissociation analyses are supported by theoretical calculations that allow a trustworthy interpretation of experimental spectra of gaseous ions. B3LYP calculations are the most prominent method used to model IR spectra, as detailed in our bibliographic survey. However, this and other commonly used methods are known to provide inaccurate energy values and geometries, especially when it comes to long-range interactions, such as intramolecular H-bonds, which show increased anharmonicity. Therefore, we evaluated some of the most commonly used density functional theory methods (B3LYP, CAM-B3LYP, and M06-2X) and basis sets (6-31+G(d,p), 6-311++G(d,p), 6-311++G(3df,2pd), aug-cc-pVDZ, and aug-cc-pVTZ), including anharmonicity and dispersion corrections. The results were compared to MP2 calculations and to experimental high-frequency (2000-4000 cm-1) IR multiphotonic dissociation (IRMPD) spectra of two protonated model molecules containing intramolecular hydrogen bonds: biotin and tryptophan. M06-2X/6-31+G(d,p) was shown to be the most cost-effective level of theory, whereas CAM-B3LYP was the most efficient method to describe the van der Waals interactions. The use of the dispersion correction D3, proposed by Grimme, improved the description of O-H vibrations involved in H-bonding but worsened the description of N-H stretches. Anharmonic calculations were shown to be extremely expensive when compared to other approaches. The efficiencies of well-established scaling factors (SFs) in opposition to sample-dependent SFs were also discussed and the use of fitted SFs were shown to be the most cost-effective approach to predict IRMPD spectra. M06-2X/6-31+G(d,p) and CAM-B3LYP/aug-cc-pVDZ were also tested against the fingerprint region. Our results suggest that these methods can also be used for analysis in this lower frequency range and should be regarded as the methods of choice for cost-effective IRMPD simulations rather than the ubiquitous B3LYP method, especially when further molecular properties are needed.

Propagating annotations of molecular networks using in silico fragmentation

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.

Bioactive Natural Products Prioritization Using Massive Multi-informational Molecular Networks

Natural products represent an inexhaustible source of novel therapeutic agents. Their complex and constrained three-dimensional structures endow these molecules with exceptional biological properties, thereby giving them a major role in drug discovery programs. However, the search for new bioactive metabolites is hampered by the chemical complexity of the biological matrices in which they are found. The purification of single constituents from such matrices requires such a significant amount of work that it should be ideally performed only on molecules of high potential value (i.e., chemical novelty and biological activity). Recent bioinformatics approaches based on mass spectrometry metabolite profiling methods are beginning to address the complex task of compound identification within complex mixtures. However, in parallel to these developments, methods providing information on the bioactivity potential of natural products prior to their isolation are still lacking and are of key interest to target the isolation of valuable natural products only. In the present investigation, we propose an integrated analysis strategy for bioactive natural products prioritization. Our approach uses massive molecular networks embedding various informational layers (bioactivity and taxonomical data) to highlight potentially bioactive scaffolds within the chemical diversity of crude extracts collections. We exemplify this workflow by targeting the isolation of predicted active and nonactive metabolites from two botanical sources (Bocquillonia nervosa and Neoguillauminia cleopatra) against two biological targets (Wnt signaling pathway and chikungunya virus replication). Eventually, the detection and isolation processes of a daphnane diterpene orthoester and four 12-deoxyphorbols inhibiting the Wnt signaling pathway and exhibiting potent antiviral activities against the CHIKV virus are detailed. Combined with efficient metabolite annotation tools, this bioactive natural products prioritization pipeline proves to be efficient. Implementation of this approach in drug discovery programs based on natural extract screening should speed up and rationalize the isolation of bioactive natural products.