Fragmentation trees for the structural characterisation of metabolites

Advancing PROTAC Characterization: Structural Insights through Adducts and Multimodal Tandem-MS Strategies

Proteolysis targeting chimeras (PROTACs) are specialized molecules that bind to a target protein and a ubiquitin ligase to facilitate protein degradation. Despite their significance, native PROTACs have not undergone tandem mass spectrometry (MS) analysis. To address this gap, we conducted a pioneering investigation on the fragmentation patterns of two PROTACs in development, dBET1 and VZ185. Employing diverse cations (sodium, lithium, and silver) and multiple tandem-MS techniques, we enhanced their structural characterization. Notably, lithium cations facilitated comprehensive positive-mode coverage for dBET1, while negative polarity mode offered richer insights. Employing de novo structure determination on 2DMS data from degradation studies yielded crucial insights. In the case of VZ185, various charge states were observed, with [M + 2H]2+ revealing fewer moieties than [M + H]+ due to charge-related factors. Augmenting structural details through silver adducts suggested both charge-directed and charge-remote fragmentation. This comprehensive investigation identifies frequently dissociated bonds across multiple fragmentation techniques, pinpointing optimal approaches for elucidating PROTAC structures. The findings contribute to advancing our understanding of PROTACs, pivotal for their continued development as promising therapeutic agents.

Determining the parent and associated fragment formulae in mass spectrometry via the parent subformula graph

BACKGROUND

Identifying the molecular formula and fragmentation reactions of an unknown compound from its mass spectrum is crucial in areas such as natural product chemistry and metabolomics. We propose a method for identifying the correct candidate formula of an unidentified natural product from its mass spectrum. The method involves scoring the plausibility of parent candidate formulae based on a parent subformula graph (PSG), and two possible metrics relating to the number of edges in the PSG. This method is applicable to both electron-impact mass spectrometry (EI-MS) and tandem mass spectrometry (MS/MS) data. Additionally, this work introduces the two-dimensional fragmentation plot (2DFP) for visualizing PSGs.

RESULTS

Our results suggest that incorporating information regarding the edges of the PSG results in enhanced performance in correctly identifying parent formulae, in comparison to the more well-accepted "MS/MS score", on the 2016 Computational Assessment of Small Molecule Identification (CASMI 2016) data set (76.3 vs 58.9% correct formula identification) and the Research Centre for Toxic Compounds in the Environment (RECETOX) data set (66.2% vs 59.4% correct formula identification). In the extension of our method to identify the correct candidate formula from complex EI-MS data of semiochemicals, our method again performed better (correct formula appearing in the top 4 candidates in 20/23 vs 7/23 cases) than the MS/MS score, and enables the rapid identification of both the correct parent ion mass and the correct parent formula with minimal expert intervention.

CONCLUSION

Our method reliably identifies the correct parent formula even when the mass information is ambiguous. Furthermore, should parent formula identification be successful, the majority of associated fragment formulae can also be correctly identified. Our method can also identify the parent ion and its associated fragments in EI-MS spectra where the identity of the parent ion is unclear due to low quantities and overlapping compounds. Finally, our method does not inherently require empirical fitting of parameters or statistical learning, meaning it is easy to implement and extend upon.

SCIENTIFIC CONTRIBUTION

Developed, implemented and tested new metrics for assessing plausibility of candidate molecular formulae obtained from HR-MS data.

Xylan glucuronic acid side chains fix suberin-like aliphatic compounds to wood cell walls

Wood is the most important repository of assimilated carbon in the biosphere, in the form of large polymers (cellulose, hemicelluloses including glucuronoxylan, and lignin) that interactively form a composite, together with soluble extractives including phenolic and aliphatic compounds. Molecular interactions among these compounds are not fully understood. We have targeted the expression of a fungal α-glucuronidase to the wood cell wall of aspen (Populus tremula L. × tremuloides Michx.) and Arabidopsis (Arabidopsis thaliana (L.) Heynh), to decrease contents of the 4-O-methyl glucuronopyranose acid (mGlcA) substituent of xylan, to elucidate mGlcA's functions. The enzyme affected the content of aliphatic insoluble cell wall components having composition similar to suberin, which required mGlcA for binding to cell walls. Such suberin-like compounds have been previously identified in decayed wood, but here, we show their presence in healthy wood of both hardwood and softwood species. By contrast, γ-ester bonds between mGlcA and lignin were insensitive to cell wall-localized α-glucuronidase, supporting the intracellular formation of these bonds. These findings challenge the current view of the wood cell wall composition and reveal a novel function of mGlcA substituent of xylan in fastening of suberin-like compounds to cell wall. They also suggest an intracellular initiation of lignin-carbohydrate complex assembly.

Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)-based small molecule structure elucidation

RATIONALE

Structure elucidation of small molecules has been one of the cornerstone applications of mass spectrometry for decades. Despite the increasing availability of software tools, structure elucidation from tandem mass spectrometry (MS/MS) data remains a challenging task, leaving many spectra unidentified. However, as an increasing number of reference MS/MS spectra are being curated at a repository scale and shared on public servers, there is an exciting opportunity to develop powerful new deep learning (DL) models for automated structure elucidation.

ARCHITECTURES

Recent early-stage DL frameworks mostly follow a "two-step approach" that translates MS/MS spectra to database structures after first predicting molecular descriptors. The related architectures could suffer from: (1) computational complexity because of the separate training of descriptor-specific classifiers, (2) the high dimensional nature of mass spectral data and information loss due to data preprocessing, (3) low substructure coverage and class imbalance problem of predefined molecular fingerprints. Inspired by successful DL frameworks employed in drug discovery fields, we have conceptualized and designed hypothetical DL architectures to tackle the above issues. For (1), we recommend multitask learning to achieve better performance with fewer classifiers by grouping structurally related descriptors. For (2) and (3), we introduce feature engineering to extract condensed and higher-order information from spectra and structure data. For instance, encoding spectra with subtrees and pre-calculated spectral patterns add peak interactions to the model input. Encoding structures with graph convolutional networks incorporates connectivity within a molecule. The joint embedding of spectra and structures can enable simultaneous spectral library and molecular database search.

CONCLUSIONS

In principle, given enough training data, adapted DL architectures, optimal hyperparameters and computing power, DL frameworks can predict small molecule structures, completely or at least partially, from MS/MS spectra. However, their performance and general applicability should be fairly evaluated against classical machine learning frameworks.

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.

Extraction, Enrichment, and LC-MSn-Based Characterization of Phlorotannins and Related Phenolics from the Brown Seaweed, Ascophyllum nodosum

Phenolic components from the edible brown seaweed, Ascophyllum nodosum, have been associated with considerable antioxidant activity but also bioactivities related to human health. This study aims to select and identify the main phlorotannin components from this seaweed which have been previously associated with potential health benefits. Methods to enrich phenolic components then further select phlorotannin components from ethanolic extracts of Ascophyllum nodosum were applied. The composition and phenolic diversity of these extracts were defined using data dependent liquid chromatography mass spectroscopic (LC-MSn) techniques. A series of phlorotannin oligomers with apparent degree of polymerization (DP) from 10 to 31 were enriched by solid phase extraction and could be selected by fractionation on Sephadex LH-20. Evidence was also obtained for the presence of dibenzodioxin linked phlorotannins as well as sulphated phlorotannins and phenolic acids. As well as diversity in molecular size, there was evidence for potential isomers at each DP. MS2 fragmentation analyses strongly suggested that the phlorotannins contained ether linked phloroglucinol units and were most likely fucophlorethols and MS3 data suggested that the isomers may result from branching within the chain. Therefore, application of these LC-MSn techniques provided further information on the structural diversity of the phlorotannins from Ascophyllum, which could be correlated against their reported bioactivities and could be further applied to phlorotannins from different seaweed species.

Mass spectrometric analysis of purine de novo biosynthesis intermediates

Purines are essential molecules for all forms of life. In addition to constituting a backbone of DNA and RNA, purines play roles in many metabolic pathways, such as energy utilization, regulation of enzyme activity, and cell signaling. The supply of purines is provided by two pathways: the salvage pathway and de novo synthesis. Although purine de novo synthesis (PDNS) activity varies during the cell cycle, this pathway represents an important source of purines, especially for rapidly dividing cells. A method for the detailed study of PDNS is lacking for analytical reasons (sensitivity) and because of the commercial unavailability of the compounds. The aim was to fully describe the mass spectrometric fragmentation behavior of newly synthesized PDNS-related metabolites and develop an analytical method. Except for four initial ribotide PDNS intermediates that preferentially lost water or phosphate or cleaved the forming base of the purine ring, all the other metabolites studied cleaved the glycosidic bond in the first fragmentation stage. Fragmentation was possible in the third to sixth stages. A liquid chromatography-high-resolution mass spectrometric method was developed and applied in the analysis of CRISPR-Cas9 genome-edited HeLa cells deficient in the individual enzymatic steps of PDNS and the salvage pathway. The identities of the newly synthesized intermediates of PDNS were confirmed by comparing the fragmentation patterns of the synthesized metabolites with those produced by cells (formed under pathological conditions of known and theoretically possible defects of PDNS). The use of stable isotope incorporation allowed the confirmation of fragmentation mechanisms and provided data for future fluxomic experiments. This method may find uses in the diagnosis of PDNS disorders, the investigation of purinosome formation, cancer research, enzyme inhibition studies, and other applications.

Identification of Vitamin D3 Oxidation Products Using High-Resolution and Tandem Mass Spectrometry

In a successful fortification program, the stability of micronutrients added to the food is one of the most important factors. The added vitamin D3 is known to sometimes decline during storage of fortified milks, and oxidation through fatty acid lipoxidation could be suspected as the likely cause. Identification of vitamin D3 oxidation products (VDOPs) in natural foods is a challenge due to the low amount of their contents and their possible transformation to other compounds during analysis. The main objective of this study was to find a method to extract VDOPs in simulated whole milk powder and to identify these products using LTQ-ion trap, Q-Exactive Orbitrap and triple quadrupole mass spectrometry. The multistage mass spectrometry (MSⁿ) spectra can help to propose plausible schemes for unknown compounds and their fragmentations. With the growth of combinatorial libraries, mass spectrometry (MS) has become an important analytical technique because of its speed of analysis, sensitivity, and accuracy. This study was focused on identifying the fragmentation rules for some VDOPs by incorporating MS data with in silico calculated MS fragmentation pathways. Diels-Alder derivatization was used to enhance the sensitivity and selectivity for the VDOPs' identification. Finally, the confirmed PTAD-derivatized target compounds were separated and analyzed using ESI(+)-UHPLC-MS/MS in multiple reaction monitoring (MRM) mode. Graphical Abstract ᅟ.

Distinct Fragmentation Pathways of Anticancer Drugs Induced by Charge-Carrying Cations in the Gas Phase

With the growth of the pharmaceutical industry, structural elucidation of drugs and derivatives using tandem mass spectrometry (MS²) has become essential for drug development and pharmacokinetics studies because of its high sensitivity and low sample requirement. Thus, research seeking to understand fundamental relationships between fragmentation patterns and precursor ion structures in the gas phase has gained attention. In this study, we investigate the fragmentation of the widely used anticancer drugs, doxorubicin (DOX), vinblastine (VBL), and vinorelbine (VRL), complexed by a singly charged proton or alkali metal ion (Li⁺, Na⁺, K⁺) in the gas phase. The drug-cation complexes exhibit distinct fragmentation patterns in tandem mass spectra as a function of cation size. The trends in fragmentation patterns are explicable in terms of structures derived from ion mobility mass spectrometry (IM-MS) and theoretical calculations. Graphical Abstract ᅟ.

Proposed Confidence Scale and ID Score in the Identification of Known-Unknown Compounds Using High Resolution MS Data

High-resolution (HR) MS instruments recording HR-full scan allow analysts to go further beyond pre-acquisition choices. Untargeted acquisition can reveal unexpected compounds or concentrations and can be performed for preliminary diagnosis attempt. Then, revealed compounds will have to be identified for interpretations. Whereas the need of reference standards is mandatory to confirm identification, the diverse information collected from HRMS allows identifying unknown compounds with relatively high degree of confidence without reference standards injected in the same analytical sequence. However, there is a necessity to evaluate the degree of confidence in putative identifications, possibly before further targeted analyses. This is why a confidence scale and a score in the identification of (non-peptidic) known-unknown, defined as compounds with entries in database, is proposed for (LC-) HRMS data. The scale is based on two representative documents edited by the European Commission (2007/657/EC) and the Metabolomics Standard Initiative (MSI), in an attempt to build a bridge between the communities of metabolomics and screening labs. With this confidence scale, an identification (ID) score is determined as [a number, a letter, and a number] (e.g., 2D3), from the following three criteria: I, a General Identification Category (1, confirmed, 2, putatively identified, 3, annotated compounds/classes, and 4, unknown); II, a Chromatography Class based on the relative retention time (from the narrowest tolerance, A, to no chromatographic references, D); and III, an Identification Point Level (1, very high, 2, high, and 3, normal level) based on the number of identification points collected. Three putative identification examples of known-unknown will be presented. Graphical Abstract ᅟ.

Omics Approaches To Probe Microbiota and Drug Metabolism Interactions

The drug metabolism field has long recognized the beneficial and sometimes deleterious influence of microbiota in the absorption, distribution, metabolism, and excretion of drugs. Early pioneering work with the sulfanilamide precursor prontosil pointed toward the necessity not only to better understand the metabolic capabilities of the microbiota but also, importantly, to identify the specific microbiota involved in the generation and metabolism of drugs. However, technological limitations important for cataloging the microbiota community as well as for understanding and/or predicting their metabolic capabilities hindered progress. Current advances including mass spectrometry-based metabolite profiling as well as culture-independent sequence-based identification and functional analysis of microbiota have begun to shed light on microbial metabolism. In this review, case studies will be presented to highlight key aspects (e.g., microbiota identification, metabolic function and prediction, metabolite identification, and profiling) that have helped to clarify how the microbiota might impact or be impacted by drug metabolism. Lastly, a perspective of the future of this field is presented that takes into account what important knowledge is lacking and how to tackle these problems.

Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics

Metabolomics, along with other "omics" approaches, is rapidly becoming one of the major approaches aimed at understanding the organization and dynamics of metabolic networks. Mass spectrometry is often a technique of choice for metabolomics studies due to its high sensitivity, reproducibility and wide dynamic range. High resolution mass spectrometry (HRMS) is a widely practiced technique in analytical and bioanalytical sciences. It offers exceptionally high resolution and the highest degree of structural confirmation. Many metabolomics studies have been conducted using HRMS over the past decade. In this review, we will explore the latest developments in Fourier transform mass spectrometry (FTMS) and Orbitrap based metabolomics technology, its advantages and drawbacks for using in metabolomics and lipidomics studies, and development of novel approaches for processing HRMS data.

UV-activated multilayer nanomatrix provides one-step tunable carbohydrate structural characterization in MALDI-MS

The structure-specific fragmentation of gas-phase ions in tandem mass spectrometry among other techniques provides an efficient analytical method for confirming unknown analytes or for elucidating chemical structures. Using concentration-dependent UV-absorbing matrix-functionalized magnetic nanoparticles and matrix-assisted laser desorption-ionization mass spectrometry (MALDI MS), we developed a single-step pseudo-MS/MS approach for tunable ionization and fragmentation to facilitate structure determination. Without chemical derivatization, we have demonstrated that this approach successfully distinguished isomeric sets of di-, tri- and tetrasaccharides. Low concentration of nanomatrix provided an enhanced signal for accurate mass determination of the intact molecular ions of analytes present in the sample. In contrast, high concentration of nanomatrix induced extensive and unique fragmentation, including high-energy facile bond breakage (A- and X-type cross-ring cleavages), which facilitated the linkage and sequence characterization of oligosaccharides without conventional tandem mass spectrometric instrumentation. The practicality of this approach for complex sample analysis was evaluated by an oligosaccharide mixture, wherein molecular ions are unambiguously observed and signature product ions are distinguishable enough for molecular identification and isomer differentiation by this simple tunable approach. By probing the roles of the multilayer nanomatrix components: matrix (energy absorption), silane-coating (energy pooling and dissipation) and core Fe3O4 (fragmentation), a plausible energy transfer mechanism was proposed based on a computational study and photoelectron experiments. The differentiation of tri- and tetra-oligosaccharide shown in this study not only demonstrated the first step toward glycan characterization by nanoparticle-assisted MALDI-MS, but also shed some insight on the nanoparticle-mediated energy transfer dynamics behind our approach.

Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations.

Enhanced acylcarnitine annotation in high-resolution mass spectrometry data: fragmentation analysis for the classification and annotation of acylcarnitines

Metabolite annotation and identification are primary challenges in untargeted metabolomics experiments. Rigorous workflows for reliable annotation of mass features with chemical structures or compound classes are needed to enhance the power of untargeted mass spectrometry. High-resolution mass spectrometry considerably improves the confidence in assigning elemental formulas to mass features in comparison to nominal mass spectrometry, and embedding of fragmentation methods enables more reliable metabolite annotations and facilitates metabolite classification. However, the analysis of mass fragmentation spectra can be a time-consuming step and requires expert knowledge. This study demonstrates how characteristic fragmentations, specific to compound classes, can be used to systematically analyze their presence in complex biological extracts like urine that have undergone untargeted mass spectrometry combined with data dependent or targeted fragmentation. Human urine extracts were analyzed using normal phase liquid chromatography (hydrophilic interaction chromatography) coupled to an Ion Trap-Orbitrap hybrid instrument. Subsequently, mass chromatograms and collision-induced dissociation and higher-energy collisional dissociation (HCD) fragments were annotated using the freely available MAGMa software¹. Acylcarnitines play a central role in energy metabolism by transporting fatty acids into the mitochondrial matrix. By filtering on a combination of a mass fragment and neutral loss designed based on the MAGMa fragment annotations, we were able to classify and annotate 50 acylcarnitines in human urine extracts, based on high-resolution mass spectrometry HCD fragmentation spectra at different energies for all of them. Of these annotated acylcarnitines, 31 are not described in HMDB yet and for only 4 annotated acylcarnitines the fragmentation spectra could be matched to reference spectra. Therefore, we conclude that the use of mass fragmentation filters within the context of untargeted metabolomics experiments is a valuable tool to enhance the annotation of small metabolites.

High resolution mass spectrometry based techniques at the crossroads of metabolic pathways

The metabolome is the set of small molecular mass compounds found in biological media, and metabolomics, which refers to as the analysis of metabolome in a given biological condition, deals with the large scale detection and quantification of metabolites in biological media. It is a data driven and multidisciplinary approach combining analytical chemistry for data acquisition, and biostatistics, informatics and biochemistry for mining and interpretation of these data. Since the middle of the 2000s, high resolution mass spectrometry is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. As the field of HRMS is quickly and permanently evolving, the aim of this work is to review its use in different aspects of metabolomics, including data acquisition, metabolite annotation, identification and quantification. At last, we would like to show that, thanks to their versatility, HRMS instruments are the most appropriate to achieve optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics.

CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra

CFM-ID is a web server supporting three tasks associated with the interpretation of tandem mass spectra (MS/MS) for the purpose of automated metabolite identification: annotation of the peaks in a spectrum for a known chemical structure; prediction of spectra for a given chemical structure and putative metabolite identification—a predicted ranking of possible candidate structures for a target spectrum. The algorithms used for these tasks are based on Competitive Fragmentation Modeling (CFM), a recently introduced probabilistic generative model for the MS/MS fragmentation process that uses machine learning techniques to learn its parameters from data. These algorithms have been extensively tested on multiple datasets and have been shown to out-perform existing methods such as MetFrag and FingerId. This web server provides a simple interface for using these algorithms and a graphical display of the resulting annotations, spectra and structures. CFM-ID is made freely available at http://cfmid.wishartlab.com.

Mass spectrometry of natural products: current, emerging and future technologies

Although mass spectrometry is a century old technology, we are entering into an exciting time for the analysis of molecular information directly from complex biological systems. In this Highlight, we feature emerging mass spectrometric methods and tools used by the natural product community and give a perspective of future directions where the mass spectrometry field is migrating towards over the next decade.

After the feature presentation: technologies bridging untargeted metabolomics and biology

Liquid chromatography/mass spectrometry-based untargeted metabolomics is now an established experimental approach that is being broadly applied by many laboratories worldwide. Interpreting untargeted metabolomic data, however, remains a challenge and limits the translation of results into biologically relevant conclusions. Here we review emerging technologies that can be applied after untargeted profiling to extend biological interpretation of metabolomic data. These technologies include advances in bioinformatic software that enable identification of isotopes and adducts, comprehensive pathway mapping, deconvolution of MS(2) data, and tracking of isotopically labeled compounds. There are also opportunities to gain additional biological insight by complementing the metabolomic analysis of homogenized samples with recently developed technologies for metabolite imaging of intact tissues. To maximize the value of these emerging technologies, a unified workflow is discussed that builds on the traditional untargeted metabolomic pipeline. Particularly when integrated together, the combination of the advances highlighted in this review helps transform lists of masses and fold changes characteristic of untargeted profiling results into structures, absolute concentrations, pathway fluxes, and localization patterns that are typically needed to understand biology.

Annotation of the human serum metabolome by coupling three liquid chromatography methods to high-resolution mass spectrometry

This work aims at evaluating the relevance and versatility of liquid chromatography coupled to high resolution mass spectrometry (LC/HRMS) for performing a qualitative and comprehensive study of the human serum metabolome. To this end, three different chromatographic systems based on a reversed phase (RP), hydrophilic interaction chromatography (HILIC) and a pentafluorophenylpropyl (PFPP) stationary phase were used, with detection in both positive and negative electrospray modes. LC/HRMS platforms were first assessed for their ability to detect, retain and separate 657 metabolite standards representative of the chemical families occurring in biological fluids. More than 75% were efficiently retained in either one LC-condition and less than 5% were exclusively retained by the RP column. These three LC/HRMS systems were then evaluated for their coverage of serum metabolome. The combination of RP, HILIC and PFPP based LC/HRMS methods resulted in the annotation of about 1328 features in the negative ionization mode, and 1358 in the positive ionization mode on the basis of their accurate mass and precise retention time in at least one chromatographic condition. Less than 12% of these annotations were shared by the three LC systems, which highlights their complementarity. HILIC column ensured the greatest metabolome coverage in the negative ionization mode, whereas PFPP column was the most effective in the positive ionization mode. Altogether, 192 annotations were confirmed using our spectral database and 74 others by performing MS/MS experiments. This resulted in the formal or putative identification of 266 metabolites, among which 59 are reported for the first time in human serum.

Molecular networking as a dereplication strategy

A major goal in natural product discovery programs is to rapidly dereplicate known entities from complex biological extracts. We demonstrate here that molecular networking, an approach that organizes MS/MS data based on chemical similarity, is a powerful complement to traditional dereplication strategies. Successful dereplication with molecular networks requires MS/MS spectra of the natural product mixture along with MS/MS spectra of known standards, synthetic compounds, or well-characterized organisms, preferably organized into robust databases. This approach can accommodate different ionization platforms, enabling cross correlations of MS/MS data from ambient ionization, direct infusion, and LC-based methods. Molecular networking not only dereplicates known molecules from complex mixtures, it also captures related analogues, a challenge for many other dereplication strategies. To illustrate its utility as a dereplication tool, we apply mass spectrometry-based molecular networking to a diverse array of marine and terrestrial microbial samples, illustrating the dereplication of 58 molecules including analogues.

A branched biosynthetic pathway is involved in production of roquefortine and related compounds in Penicillium chrysogenum

Profiling and structural elucidation of secondary metabolites produced by the filamentous fungus Penicillium chrysogenum and derived deletion strains were used to identify the various metabolites and enzymatic steps belonging to the roquefortine/meleagrin pathway. Major abundant metabolites of this pathway were identified as histidyltryptophanyldiketopiperazine (HTD), dehydrohistidyltryptophanyldi-ketopiperazine (DHTD), roquefortine D, roquefortine C, glandicoline A, glandicoline B and meleagrin. Specific genes could be assigned to each enzymatic reaction step. The nonribosomal peptide synthetase RoqA accepts L-histidine and L-tryptophan as substrates leading to the production of the diketopiperazine HTD. DHTD, previously suggested to be a degradation product of roquefortine C, was found to be derived from HTD involving the cytochrome P450 oxidoreductase RoqR. The dimethylallyltryptophan synthetase RoqD prenylates both HTD and DHTD yielding directly the products roquefortine D and roquefortine C without the synthesis of a previously suggested intermediate and the involvement of RoqM. This leads to a branch in the otherwise linear pathway. Roquefortine C is subsequently converted into glandicoline B with glandicoline A as intermediates, involving two monooxygenases (RoqM and RoqO) which were mixed up in an earlier attempt to elucidate the biosynthetic pathway. Eventually, meleagrin is produced from glandicoline B involving a methyltransferase (RoqN). It is concluded that roquefortine C and meleagrin are derived from a branched biosynthetic pathway.

Electronic structure and gas-phase chemistry of protonated α- and β-quinonoid compounds: a mass spectrometry and computational study

RATIONALE

The use of quinonoid compounds against tropical diseases and as antitumor agents has prompted the search for new naturally occurring and synthetic derivatives. Among these quinonoid compounds, lapachol and its isomers (α- and β-lapachone) serve as models for the synthesis of new compounds with biological activity, and the use of electrospray ionization tandem mass spectrometry (ESI-MS/MS) analysis as a tool to elucidate and characterize these products has furnished important information about these compounds.

METHODS

ESI-MS/MS analysis under collision-induced dissociation conditions was used to describe the fragmentation mechanisms for protonated 1,4-naphthoquinone, 1,2-naphthoquinone, α-lapachone, and β-lapachone. The B3LYP/6-31+G(d,p) model was used to obtain proton affinities, gas-phase basicities, and molecular electrostatic potential maps, thus indicating the probable protonation sites. Fragmentation pathways were suggested on the basis of the relative enthalpies of the product ions.

RESULTS

The ESI-MS signals of the cationized molecules of ortho quinonoid compounds were more intense than those of the protonated molecule. Formation of the major product ions with m/z 187 from protonated α- and β-lapachone has been attributed to a retro-Diels-Alder (RDA) reaction.

CONCLUSIONS

MS/MS studies on lapachol isomers (α- and β-lapachone) will facilitate the interpretation of the liquid chromatography (LC)-MS/MS analysis of new metabolites. MS/MS data on the 1,4-naphthoquinone, 1,2-naphthoquinone, α-lapachone and β-lapachone core will help characterize new derivatives from in vitro/in vivo metabolism studies in complex matrices. The product ions revealed the major fragmentation mechanisms and these ions will serve as diagnostic ions to identify each studied compound.

Differentiating isobaric steroid hormone metabolites using multi-stage tandem mass spectrometry

Steroid hormones and their metabolites are currently undergoing clinical trials as potential therapeutics for traumatic brain injury (TBI). To support this work, it is necessary to develop improved procedures for differentiating isobaric species in this compound class. Equilin sulfate (E-S), estrone sulfate (E1-S), 17α-dihydroequilin sulfate (ADHE-S), and 17β-dihydroequilin sulfate (BDHE-S) are primary constituents in hormone replacement therapies, such as Premarin, which are among pharmaceuticals being investigated for TBI treatment. The latter three compounds are isomers and can be difficult to differentiate in trace analytical determinations. In this work, a systematic study of the fragmentation of ADHE-S, BDHE-S, E1-S, and E-S under different stages of higher order tandem mass spectrometry (MS(n)) and variation of collision energy, allowed optimization of conditions for distinguishing the isomeric structures. For epimeric variants (e.g., ADHE-S versus BDHE-S; α- versus β-stereoisomerization in the C-17 position), differentiation was achieved at MS(4) and fragmentation was demonstrated through MS(5). Computational analysis was performed to further explore differences in the fragmentation pathways due to changes in stereochemistry.

Computational mass spectrometry for small molecules

: The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline.

A rough guide to metabolite identification using high resolution liquid chromatography mass spectrometry in metabolomic profiling in metazoans

Compound identification in mass spectrometry based metabolomics can be a problem but sometimes the problem seems to be presented in an over complicated way. The current review focuses on metazoans where the range of metabolites is more restricted than for example in plants. The focus is on liquid chromatography with high resolution mass spectrometry where it is proposed that most of the problems in compound identification relate to structural isomers rather than to isobaric compounds. Thus many of the problems faced relate to separation of isomers, which is usually required even if fragmentation is used to support structural identification. Many papers report the use of MS/MS or MS² as an adjunct to the identification of known metabolites but there a few examples in metabolomics studies of metazoans of complete structure elucidation of novel metabolites or metabolites where no authentic standards are available for comparison.