Computing Fragmentation Trees from Tandem Mass Spectrometry Data

ModiFinder: Tandem Mass Spectral Alignment Enables Structural Modification Site Localization

Untargeted tandem mass spectrometry (MS/MS) has become a high-throughput method to measure small molecules in complex samples. One key goal is the transformation of these MS/MS spectra into chemical structures. Computational techniques such as MS/MS library search have enabled the reidentification of known compounds. Analog library search and molecular networking extend this identification to unknown compounds. While there have been advancements in metrics for the similarity of MS/MS spectra of structurally similar compounds, there is still a lack of automated methods to provide site specific information about structural modifications. Here we introduce ModiFinder which leverages the alignment of peaks in MS/MS spectra between structurally related known and unknown small molecules. Specifically, ModiFinder focuses on shifted MS/MS fragment peaks in the MS/MS alignment. These shifted peaks putatively represent substructures of the known molecule that contain the site of the modification. ModiFinder synthesizes this information together and scores the likelihood for each atom in the known molecule to be the modification site. We demonstrate in this manuscript how ModiFinder can effectively localize modifications which extends the capabilities of MS/MS analog searching and molecular networking to accelerate the discovery of novel compounds.

Tandem Mass Spectrometry across Platforms

PubChem serves as a comprehensive repository, housing over 100 million unique chemical structures representing the breadth of our chemical knowledge across numerous fields including metabolism, pharmaceuticals, toxicology, cosmetics, agriculture, and many more. Rapid identification of these small molecules increasingly relies on electrospray ionization (ESI) paired with tandem mass spectrometry (MS/MS), particularly by comparison to genuine standard MS/MS data sets. Despite its widespread application, achieving consistency in MS/MS data across various analytical platforms remains an unaddressed concern. This study evaluated MS/MS data derived from one hundred molecular standards utilizing instruments from five manufacturers, inclusive of quadrupole time-of-flight (QTOF) and quadrupole orbitrap "exactive" (QE) mass spectrometers by Agilent (QTOF), Bruker (QTOF), SCIEX (QTOF), Waters (QTOF), and Thermo QE. We assessed fragment ion variations at multiple collisional energies (0, 10, 20, and 40 eV) using the cosine scoring algorithm for comparisons and the number of fragments observed. A parallel visual analysis of the MS/MS spectra across instruments was conducted, consistent with a standard procedure that is used to circumvent the still prevalent issue of mischaracterizations as shown for dimethyl sphingosine and C20 sphingosine. Our analysis revealed a notable consistency in MS/MS data and identifications, with fragment ions' m/z values exhibiting the highest concordance between instrument platforms at 20 eV, the other collisional energies (0, 10, and 40 eV) were significantly lower. While moving toward a standardized ESI MS/MS protocol is required for dependable molecular characterization, our results also underscore the continued importance of corroborating MS/MS data against standards to ensure accurate identifications. Our findings suggest that ESI MS/MS manufacturers, akin to the established norms for gas chromatography mass spectrometry instruments, should standardize the collision energy at 20 eV across different instrument platforms.

BRCA1/TP53 tumor proteins inhibited by novel analogues of curcumin - Insight from computational modelling, dynamic simulation and experimental validation

The current study aimed to design novel curcumin analogue inhibitors with antiproliferative and antitumor activity towards BRCA1 and TP53 tumor proteins and to study their therapeutic potential by computer-aided molecular designing and experimental investigations. Four curcumin analogues were computationally designed and their drug-likeness and pharmacokinetic properties were predicted. The binding of these analogues against six protein targets belonging to BRCA1 and TP53 tumor proteins were modelled by molecular docking and their binding energies were compared with that of curcumin and the standard drug cyclophosphamide and its validated target. The stabilities of selected docked complexes were confirmed by molecular dynamic simulation (MDS) and MMGBSA calculations. The best-docked analogue was chemically synthesized, characterized, and used for in vitro cytotoxic screening using DLA, EAC, and C127I cell lines. In vivo antitumor studies were carried out in Swiss Albino Mice. The study revealed that the designed analogues satisfied drug-likeness and pharmacokinetic properties and demonstrated better binding affinity to the selected targets than curcumin. Among the analogues, NLH demonstrated significant interaction with the BRCA1-BRCT-c domain (TG3; binding energy -8.3 kcal/mol) when compared to the interaction of curcumin (binding energy -6.19 kcal) and cyclophosphamide (binding energy -3.8 kcal/mol) and its usual substrate (TG7). The MDS and MM/GBSA studies revealed that the binding free energy of the NLH-TG3 complex (-61.24 kcal/mol) was better when compared to that of the cyclophosphamide-TG7 complex (-21.67 kcal/mol). In vitro, cytotoxic studies showed that NLH demonstrated significant antiproliferative activities against tumor cell lines. The in vivo study depicted NLH possesses the potential for tumor inhibition. Thus, the newly synthesized curcumin analogue is probably used to develop novel therapeutic agents against breast cancer.

Activity labelled molecular networking fuels the antioxidation active molecules profile of Ginger

Ginger has been used as consumed food spice and folk medicine in daily life for thousands of years in various regions of the world. Considerable antioxidation is one of the major activities for Ginger to exhibit health-promoting effects. In this study, a bioinformatic workflow was developed to generate activity labelled molecular networking (ALMN) to fuel the antioxidation active molecules profile of Ginger. In ALMN, antioxidation activity data, which was defined as correlation (r and p value) between the relative abundance of a molecule in fractions and the activity level of each fraction, was labelled to feature-based molecular network to profile out antioxidation active molecules visually. Fragmentation tree was further computed as a complementary way to conduct high confidence structure annotations of antioxidation active molecules. Consequently, 48 molecules were prioritized as antioxidation active molecules from 11,720 metabolite molecules of Ginger in a systematical way.

Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective

The interpretation of spectral data, including mass, nuclear magnetic resonance, infrared, and ultraviolet-visible spectra, is critical for obtaining molecular structural information. The development of advanced sensing technology has multiplied the amount of available spectral data. Chemical experts must use basic principles corresponding to the spectral information generated by molecular fragments and functional groups. This is a time-consuming process that requires a solid professional knowledge base. In recent years, the rapid development of computer science and its applications in cheminformatics and the emergence of computer-aided expert systems have greatly reduced the difficulty in analyzing large quantities of data. For expert systems, however, the problem-solving strategy must be known in advance or extracted by human experts and translated into algorithms. Gratifyingly, the development of artificial intelligence (AI) methods has shown great promise for solving such problems. Traditional algorithms, including the latest neural network algorithms, have shown great potential for both extracting useful information and processing massive quantities of data. This Perspective highlights recent innovations covering all of the emerging AI-based spectral interpretation techniques. In addition, the main limitations and current obstacles are presented, and the corresponding directions for further research are proposed. Moreover, this Perspective gives the authors' personal outlook on the development and future applications of spectral interpretation.

Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics

The ability to reliably identify small molecules (e.g., metabolites) is key toward driving scientific advancement in metabolomics. Gas chromatography-mass spectrometry (GC-MS) is an analytic method that may be applied to facilitate this process. The typical GC-MS identification workflow involves quantifying the similarity of an observed sample spectrum and other features (e.g., retention index) to that of several references, noting the compound of the best-matching reference spectrum as the identified metabolite. While a deluge of similarity metrics exist, none quantify the error rate of generated identifications, thereby presenting an unknown risk of false identification or discovery. To quantify this unknown risk, we propose a model-based framework for estimating the false discovery rate (FDR) among a set of identifications. Extending a traditional mixture modeling framework, our method incorporates both similarity score and experimental information in estimating the FDR. We apply these models to identification lists derived from across 548 samples of varying complexity and sample type (e.g., fungal species, standard mixtures, etc.), comparing their performance to that of the traditional Gaussian mixture model (GMM). Through simulation, we additionally assess the impact of reference library size on the accuracy of FDR estimates. In comparing the best performing model extensions to the GMM, our results indicate relative decreases in median absolute estimation error (MAE) ranging from 12% to 70%, based on comparisons of the median MAEs across all hit-lists. Results indicate that these relative performance improvements generally hold despite library size; however FDR estimation error typically worsens as the set of reference compounds diminishes.

BUDDY: molecular formula discovery via bottom-up MS/MS interrogation

A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY ( https://github.com/HuanLab/BUDDY ).

Analysis of a Broad Range of Carbonyl Metabolites in Exhaled Breath by UHPLC-MS

Analysis of volatile organic compounds (VOCs) in exhaled breath (EB) has shown great potential for disease detection including lung cancer, infectious respiratory diseases, and chronic obstructive pulmonary disease. Although many breath sample collection and analytical methods have been developed for breath analysis, analysis of metabolic VOCs in exhaled breath is still a challenge for clinical application. Many carbonyl compounds in exhaled breath are related to the metabolic processes of diseases. This work reports a method of ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-MS) for the analysis of a broad range of carbonyl metabolites in exhaled breath. Carbonyl compounds in the exhaled breath were captured by a fabricated silicon microreactor with a micropillar array coated with 2-(aminooxy)ethyl-N,N,N-trimethylammonium (ATM) triflate. A total of six subgroups consisting of saturated aldehydes and ketones, hydroxy-aldehydes, and hydroxy-ketones, unsaturated 2-alkenals, and 4-hydroxy-2-alkenals were identified in the exhaled breath. The combination of a silicon microreactor for the selective capture of carbonyl compounds with UHPLC-MS analysis may provide a quantitative method for the analysis of carbonyls to identify disease markers in exhaled breath.

Discovering New Natural Products Using Metabolomics-Based Approaches

The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.

Concepts and applications of chemical fingerprint for hit and lead screening

Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches. In this review, we summarize and classify common and state-of-the-art fingerprints into eight different types (dictionary based, circular, topological, pharmacophore, protein-ligand interaction, shape based, reinforced, and multi). We also highlight applications of fingerprints in early drug research and development (R&D). Thus, this review provides a guide for the selection of appropriate fingerprints of compounds (or ligand-protein complexes) for use in drug R&D.

Computed Mass-Fragmentation Energy Profiles of Some Acetalized Monosaccharides for Identification in Mass Spectrometry

Our study found that quantum calculations can differentiate fragmentation energies into isomeric structures with asymmetric carbon atoms, such as those of acetalized monosaccharides. It was justified by the good results that have been published in recent years on the discrimination of structural isomers and diastereomers by correlating the calculated mass energy fragmentation profiles with their mass spectra. Based on the quantitative structure–fragmentation relationship (QSFR), this technique compares the intensities of primary ions from the experimental spectrum using the mass energy profiles calculated for the candidate structures. Maximum fit is obtained for the true structure. For a preliminary assessment of the accuracy of the identification of some di-O-isopropylidene monosaccharide diastereomers, we used fragmentation enthalpies (ΔfH) and Gibbs energies (ΔfG) as the energetic descriptors of fragmentation. Four quantum chemical methods were used: RM1, PM7, DFT ΔfH and DFT ΔfG. The mass energy database shows that the differences between the profiles of the isomeric candidate structures could be large enough to be distinguished from each other. This database allows the optimization of energy descriptors and quantum computing methods that can ensure the correct identification of these isomers.

PyFragMS-A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways

Dissociation induced by the accumulation of internal energy via collisions of ions with neutral molecules is one of the most important fragmentation techniques in mass spectrometry (MS), and the identification of small singly charged molecules is based mainly on the consideration of the fragmentation spectrum. Many research studies have been dedicated to the creation of databases of experimentally measured tandem mass spectrometry (MS/MS) spectra (such as MzCloud, Metlin, etc.) and developing software for predicting MS/MS fragments in silico from the molecular structure (such as MetFrag, CFM-ID, CSI:FingerID, etc.). However, the fragmentation mechanisms and pathways are still not fully understood. One of the limiting obstacles is that protomers (positive ions protonated at different sites) produce different fragmentation spectra, and these spectra overlap in the case of the presence of different protomers. Here, we are proposing to use a combination of two powerful approaches: computing fragmentation trees that carry information of all consecutive fragmentations and consideration of the MS/MS data of isotopically labeled compounds. We have created PyFragMS-a web tool consisting of a database of annotated MS/MS spectra of isotopically labeled molecules (after H/D and/or ¹⁶O/¹⁸O exchange) and a collection of instruments for computing fragmentation trees for an arbitrary molecule. Using PyFragMS, we investigated how the site of protonation influences the fragmentation pathway for small molecules. Also, PyFragMS offers capabilities for performing database search when MS/MS data of the isotopically labeled compounds are taken into account.

Comprehensive degradation profiling and influence of different oxidizing reagents on tinoridine hydrochloride: Structural characterization of its degradation products using HPLC and HRMS

RATIONALE

Stress testing on tinoridine hydrochloride was carried out using a multidimensional approach. This included different conditions: hydrolytic (acidic, alkaline, and neutral conditions), different oxidative reagents, thermal, photolytic conditions, HPLC method development, and structural elucidation using high-resolution mass spectrometry (HRMS). It provides the basis for quality control of tinoridine hydrochloride and its derivatives during storage conditions.

METHODS

The tinoridine hydrochloride was subjected to a variety of stress conditions. A gradient reversed-phase HPLC method was developed on a X-Bridge C18 column (250 × 4.6 mm, 5 μm) to separate all the degradation products (DPs). HRMS studies have been performed to elucidate the structure of DPs.

RESULTS

HPLC-PDA study revealed that significant degradation products were formed in hydrolytic, AIBN (radical initiator at 40°C), thermal, and solid-state photolight stress conditions, but the drug was stable under oxidative conditions (H₂ O_2, Fenton's reagent at room temperature and ferric chloride at 40°C). The structure of degradation products was elucidated, and mechanism of their formation was explained.

CONCLUSION

Stress study was successfully carried out as per ICH Q1A (R2) guideline on tinoridine hydrochloride. A total of six new degradation products were characterized, DP 2 and DP 6 formed by the effect of co-solvent. This study provides the scientifically sound basis for quality monitoring and storage conditions of tinoridine hydrochloride.

A novel workflow utilizing open-source software tools in the environmental fate studies: The example of imatinib biotransformation

The aim of this study is to utilize novel and powerful workflows with publicly available tools to efficiently process data and facilitate rapid acquisition of knowledge on environmental fate studies. Taking imatinib (IMA) as an example, we developed an efficient workflow to describe IMA biodegradation with activated sludge (AS) from wastewater treatment plants (WWTP). IMA is a cytostatic pharmaceutical; a selective tyrosine kinase inhibitor used to treat chronic myeloid leukemia. Its reported ecotoxic, endocrine and genotoxic effects imply high risk for aquatic wildlife and human health, however its fate in the environment is not yet well known. The study was conducted in a batch biotransformation setup, at two AS concentration levels and in presence and absence of carbon source. Degradation profiles and formation of IMA transformation products (TPs) were investigated using UHPLC-QqOrbitrap-MS/MS which showed that IMA is readily biodegradable. TPs were determined using multivariate statistical analysis. Eight TPs were determined and tentatively identified, six of them for first time. Hydrolysis of amide bond, oxidation, demethylation, deamination, acetylation and succinylation are proposed as major biodegradation pathways. TP235, the product of amide bond hydrolysis, was detected and quantified in actual wastewaters, at levels around 1 ng/L. This calls for more studies on the environmental fate of IMA in order to properly asses the environmental risk and hazard associated to IMA and its TPs.

Next-Generation Mass Spectrometry Metabolomics Revives the Functional Analysis of Plant Metabolic Diversity

The remarkable diversity of specialized metabolites produced by plants has inspired several decades of research and nucleated a long list of theories to guide empirical ecological studies. However, analytical constraints and the lack of untargeted processing workflows have long precluded comprehensive metabolite profiling and, consequently, the collection of the critical currencies to test theory predictions for the ecological functions of plant metabolic diversity. Developments in mass spectrometry (MS) metabolomics have revolutionized the large-scale inventory and annotation of chemicals from biospecimens. Hence, the next generation of MS metabolomics propelled by new bioinformatics developments provides a long-awaited framework to revisit metabolism-centered ecological questions, much like the advances in next-generation sequencing of the last two decades impacted all research horizons in genomics. Here, we review advances in plant (computational) metabolomics to foster hypothesis formulation from complex metabolome data. Additionally, we reflect on how next-generation metabolomics could reinvigorate the testing of long-standing theories on plant metabolic diversity.

A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics

Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.

Maize specialized metabolome networks reveal organ-preferential mixed glycosides

Despite the scientific and economic importance of maize, little is known about its specialized metabolism. Here, five maize organs were profiled using different reversed-phase liquid chromatography-mass spectrometry methods. The resulting spectral metadata, combined with candidate substrate-product pair (CSPP) networks, allowed the structural characterization of 427 of the 5,420 profiled compounds, including phenylpropanoids, flavonoids, benzoxazinoids, and auxin-related compounds, among others. Only 75 of the 427 compounds were already described in maize. Analysis of the CSPP networks showed that phenylpropanoids are present in all organs, whereas other metabolic classes are rather organ-enriched. Frequently occurring CSPP mass differences often corresponded with glycosyl- and acyltransferase reactions. The interplay of glycosylations and acylations yields a wide variety of mixed glycosides, bearing substructures corresponding to the different biochemical classes. For example, in the tassel, many phenylpropanoid and flavonoid-bearing glycosides also contain auxin-derived moieties. The characterized compounds and mass differences are an important step forward in metabolic pathway discovery and systems biology research. The spectral metadata of the 5,420 compounds is publicly available (DynLib spectral database, https://bioit3.irc.ugent.be/dynlib/).

ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra

Motivation

Metabolite identification is an important task in metabolomics to enhance the knowledge of biological systems. There have been a number of machine learning-based methods proposed for this task, which predict a chemical structure of a given spectrum through an intermediate (chemical structure) representation called molecular fingerprints. They usually have two steps: (i) predicting fingerprints from spectra; (ii) searching chemical compounds (in database) corresponding to the predicted fingerprints. Fingerprints are feature vectors, which are usually very large to cover all possible substructures and chemical properties, and therefore heavily redundant, in the sense of having many molecular (sub)structures irrelevant to the task, causing limited predictive performance and slow prediction.

Results

We propose ADAPTIVE, which has two parts: learning two mappings (i) from structures to molecular vectors and (ii) from spectra to molecular vectors. The first part learns molecular vectors for metabolites from given data, to be consistent with both spectra and chemical structures of metabolites. In more detail, molecular vectors are generated by a model, being parameterized by a message passing neural network, and parameters are estimated by maximizing the correlation between molecular vectors and the corresponding spectra in terms of Hilbert-Schmidt Independence Criterion. Molecular vectors generated by this model are compact and importantly adaptive (specific) to both given data and task of metabolite identification. The second part uses input output kernel regression (IOKR), the current cutting-edge method of metabolite identification. We empirically confirmed the effectiveness of ADAPTIVE by using a benchmark data, where ADAPTIVE outperformed the original IOKR in both predictive performance and computational efficiency.

Availability and implementation

The code will be accessed through http://www.bic.kyoto-u.ac.jp/pathway/tools/ADAPTIVE after the acceptance of this article.

Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics

Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer a candidate set, a listing of putative chemical identities to be used during annotation, through an extended metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMF aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMF was applied to untargeted LC-MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMF are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxyphenyllactate, a metabolite predicted by EMMF that has not been previously documented as part of the CHO cell metabolic model.

Graph theory-based reaction pathway searches and DFT calculations for the mechanism studies of free radical-initiated peptide sequencing mass spectrometry (FRIPS MS): a model gas-phase reaction of GGR tri-peptide

Graph theory-based reaction pathway searches (ACE-Reaction program) and density functional theory calculations were performed to shed light on the mechanisms for the production of [a_n + H]⁺, x_n⁺, y_n⁺, z_n⁺, and [y_n + 2H]⁺ fragments formed in free radical-initiated peptide sequencing (FRIPS) mass spectrometry measurements of a small model system of glycine-glycine-arginine (GGR). In particular, the graph theory-based searches, which are rarely applied to gas-phase reaction studies, allowed us to investigate reaction mechanisms in an exhaustive manner without resorting to chemical intuition. As expected, radical-driven reaction pathways were favorable over charge-driven reaction pathways in terms of kinetics and thermodynamics. Charge- and radical-driven pathways for the formation of [y_n + 2H]⁺ fragments were carefully compared, and it was revealed that the [y_n + 2H]⁺ fragments observed in our FRIPS MS spectra originated from the radical-driven pathway, which is in contrast to the general expectation. The acquired understanding of the FRIPS fragmentation mechanism is expected to aid in the interpretation of FRIPS MS spectra. It should be emphasized that graph theory-based searches are powerful and effective methods for studying reaction mechanisms, including gas-phase reactions in mass spectrometry.

De Novo Molecular Formula Annotation and Structure Elucidation Using SIRIUS 4

SIRIUS 4 is the best-in-class computational tool for metabolite identification from high-resolution tandem mass spectrometry data. It offers de novo molecular formula annotation with outstanding accuracy. When searching fragmentation spectra in a structure database, it reaches over 70% correct identifications. A predicted fingerprint, which indicates the presence or absence of thousands of molecular properties, helps to deduce information about the compound of interest even if it is not contained in any structure database. Here, we present best practices and describe how to leverage the full potential of SIRIUS 4, how to incorporate it into your own workflow, and how it adds value to the analysis of mass spectrometry data beyond spectral library search.

Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches

MOTIVATION

Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Understanding biochemical characteristics of the metabolites is an essential and significant part of metabolomics to enlarge the knowledge of biological systems. It is also the key to the development of many applications and areas such as biotechnology, biomedicine or pharmaceuticals. However, the identification of the metabolites remains a challenging task in metabolomics with a huge number of potentially interesting but unknown metabolites. The standard method for identifying metabolites is based on the mass spectrometry (MS) preceded by a separation technique. Over many decades, many techniques with different approaches have been proposed for MS-based metabolite identification task, which can be divided into the following four groups: mass spectra database, in silico fragmentation, fragmentation tree and machine learning. In this review paper, we thoroughly survey currently available tools for metabolite identification with the focus on in silico fragmentation, and machine learning-based approaches. We also give an intensive discussion on advanced machine learning methods, which can lead to further improvement on this task.

SIMPLE: Sparse Interaction Model over Peaks of moLEcules for fast, interpretable metabolite identification from tandem mass spectra

Motivation

Recent success in metabolite identification from tandem mass spectra has been led by machine learning, which has two stages: mapping mass spectra to molecular fingerprint vectors and then retrieving candidate molecules from the database. In the first stage, i.e. fingerprint prediction, spectrum peaks are features and considering their interactions would be reasonable for more accurate identification of unknown metabolites. Existing approaches of fingerprint prediction are based on only individual peaks in the spectra, without explicitly considering the peak interactions. Also the current cutting-edge method is based on kernels, which are computationally heavy and difficult to interpret.

Results

We propose two learning models that allow to incorporate peak interactions for fingerprint prediction. First, we extend the state-of-the-art kernel learning method by developing kernels for peak interactions to combine with kernels for peaks through multiple kernel learning (MKL). Second, we formulate a sparse interaction model for metabolite peaks, which we call SIMPLE, which is computationally light and interpretable for fingerprint prediction. The formulation of SIMPLE is convex and guarantees global optimization, for which we develop an alternating direction method of multipliers (ADMM) algorithm. Experiments using the MassBank dataset show that both models achieved comparative prediction accuracy with the current top-performance kernel method. Furthermore SIMPLE clearly revealed individual peaks and peak interactions which contribute to enhancing the performance of fingerprint prediction.

Availability and implementation

The code will be accessed through http://mamitsukalab.org/tools/SIMPLE/.

Calculation of Electron Ionization Mass Spectra with Semiempirical GFNn-xTB Methods

In this work, we have tested two different extended tight-binding methods in the framework of the quantum chemistry electron ionization mass spectrometry (QCEIMS) program to calculate electron ionization mass spectra. The QCEIMS approach provides reasonable, first-principles computed spectra, which can be directly compared to experiment. Furthermore, it provides detailed insight into the reaction mechanisms of mass spectrometry experiments. It sheds light upon the complicated fragmentation procedures of bond breakage and structural rearrangements that are difficult to derive otherwise. The required accuracy and computational demands for successful reproduction of a mass spectrum in relation to the underlying quantum chemical method are discussed. To validate the new GFN2-xTB approach, we conduct simulations for 15 organic, transition-metal, and main-group inorganic systems. Major fragmentation patterns are analyzed, and the entire calculated spectra are directly compared to experimental data taken from the literature. We discuss the computational costs and the robustness (outliers) of several calculation protocols presented. Overall, the new, theoretically more sophisticated semiempirical method GFN2-xTB performs well and robustly for a wide range of organic, inorganic, and organometallic systems.

From genomics to metabolomics, moving toward an integrated strategy for the discovery of fungal secondary metabolites

Fungal secondary metabolites are defined by bioactive properties that ensure adaptation of the fungus to its environment. Although some of these natural products are promising sources of new lead compounds especially for the pharmaceutical industry, others pose risks to human and animal health. The identification of secondary metabolites is critical to assessing both the utility and risks of these compounds. Since fungi present biological specificities different from other microorganisms, this review covers the different strategies specifically used in fungal studies to perform this critical identification. Strategies focused on the direct detection of the secondary metabolites are firstly reported. Particularly, advances in high-throughput untargeted metabolomics have led to the generation of large datasets whose exploitation and interpretation generally require bioinformatics tools. Then, the genome-based methods used to study the entire fungal metabolic potential are reported. Transcriptomic and proteomic tools used in the discovery of fungal secondary metabolites are presented as links between genomic methods and metabolomic experiments. Finally, the influence of the culture environment on the synthesis of secondary metabolites by fungi is highlighted as a major factor to consider in research on fungal secondary metabolites. Through this review, we seek to emphasize that the discovery of natural products should integrate all of these valuable tools. Attention is also drawn to emerging technologies that will certainly revolutionize fungal research and to the use of computational tools that are necessary but whose results should be interpreted carefully.

The Structure and Function of Major Plant Metabolite Modifications

Plants produce a myriad of structurally and functionally diverse metabolites that play many different roles in plant growth and development and in plant response to continually changing environmental conditions as well as abiotic and biotic stresses. This metabolic diversity is, to a large extent, due to chemical modification of the basic skeletons of metabolites. Here, we review the major known plant metabolite modifications and summarize the progress that has been achieved and the challenges we are facing in the field. We focus on discussing both technical and functional aspects in studying the influences that various modifications have on biosynthesis, degradation, transport, and storage of metabolites, as well as their bioactivity and toxicity. Finally, we discuss some emerging insights into the evolution of metabolic pathways and metabolite functionality.

Stress-induced modification of indole alkaloids:Phytomodificines as a new category of specialized metabolites

This study focuses on the elucidation of the stress-induced reverse changes of major indole alkaloids in Vinca minor, primarily on the postulated conversion of vincamine and vincadifformine to yield 9-methoxyvincamine, minovincine, and minovincinine, respectively. By applying the P450 enzyme inhibitors, naproxen and resveratrol, it was shown that the oxidative reaction involved in the postulated conversion of vincamine and vincadifformine is catalyzed by cytochrome P450 enzymes. In combination with the identification of 9-hydroxyvincamine as a postulated intermediate, this result confirms that the observed stress-induced reverse changes in the alkaloid pattern are caused by modifications of the alkaloids which regularly accumulate in the healthy Vinca minor plants. Up to now, just two main types of defense compounds are distinguished: phytoalexins, which are synthesized de novo from primary metabolites and phytoanticipins, which are constitutively present in plants - either intrinsically active or are activated after cell death by hydrolysis or oxidation of the precursors. In contrast, the results presented in this paper demonstrate that indole alkaloids, representing typical phytoanticipins, are just slightly modified in response to a stress-related elicitation. Accordingly, these modified alkaloids neither represent classical phytoalexins (being synthesized de novo), nor can they be classified as phytoanticipins, since modification does not occur postmortem. Consequently, we propose a new category for these modified alkaloids that we call phytomodificines.

ChemFrag: Chemically meaningful annotation of fragment ion mass spectra

Identification and structural determination of small molecules by mass spectrometry is an important step in chemistry and biochemistry. However, the chemically realistic annotation of a fragment ion spectrum can be a difficult challenge. We developed ChemFrag, for the detection of fragmentation pathways and the annotation of fragment ions with chemically reasonable structures. ChemFrag combines a quantum chemical with a rule-based approach. For different doping substances as test instances, ChemFrag correctly annotates fragment ions. In most cases, the predicted fragments are chemically more realistic than those from purely combinatorial approaches, or approaches based on machine learning. The annotation generated by ChemFrag often coincides with spectra that have been manually annotated by experts. This is a major advance in peak annotation and allows a more precise automatic interpretation of mass spectra.

Structural Characterization of Acidic Compounds in Pyrolysis Liquids Using Collision-Induced Dissociation and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

In this study, a novel approach to characterize and identify acidic oil compounds utilizing the fragmentational behavior of their corresponding precursor ions is presented. Precursor ions of seven analyzed pyrolysis oils that were generated from pyrolysis educts of different origins and degrees of coalification were produced by electrospray ionization in the negative ion mode (ESI(-)). Following a fragmentation of all ions in the ion cloud by collision-induced dissociation (CID), the precursor and product ions were subsequently detected by ultrahigh resolving Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). The ESI(-)-CID data sets were evaluated by applying either a targeted classification or untargeted clustering approach. In the case of the targeted classification, 10% of the ionized precursor ions of the analyzed pyrolysis liquid samples could be classified into one of 11 compound classes utilizing theoretical fragmentation pathways of these classes. In contrast, theoretical fragmentation pathways were not necessary for the untargeted clustering approach, making it the more transmittable method. Results from both approaches were verified by analyzing standard compounds of known structure. The analysis and data evaluation methods presented in this work can be used to characterize complex organic mixtures, such as pyrolysis oils, and their compounds in-depth on a structural level.

Dereplication of microbial metabolites through database search of mass spectra

Natural products have traditionally been rich sources for drug discovery. In order to clear the road toward the discovery of unknown natural products, biologists need dereplication strategies that identify known ones. Here we report DEREPLICATOR+, an algorithm that improves on the previous approaches for identifying peptidic natural products, and extends them for identification of polyketides, terpenes, benzenoids, alkaloids, flavonoids, and other classes of natural products. We show that DEREPLICATOR+ can search all spectra in the recently launched Global Natural Products Social molecular network and identify an order of magnitude more natural products than previous dereplication efforts. We further demonstrate that DEREPLICATOR+ enables cross-validation of genome-mining and peptidogenomics/glycogenomics results.

Seeking universal detectors for analytical characterizations

It is highly desirable to have a universal detector that can detect all types of compounds and give a uniform response regardless of the physiochemical properties of the compounds. With such a universal detector, all components in a sample can be accurately quantified without the need for individual standards. This is especially needed for the characterization of unknowns and for non-targeted analysis, or for samples that have no isolated standards available for each component. Over the years, much effort has been put into seeking a universal detection technology. In this review, we discuss the commonly used detectors for analytical characterization, including UV, RI, ELSD, CAD, CLND, FID, VUV, MS, NMR, and hyphenated detection, with the focuses on the "universal" features of these detectors regarding the types of molecules they can detect and the uniformity of responses.

A tutorial in small molecule identification via electrospray ionization-mass spectrometry: The practical art of structural elucidation

The identification of unknown molecules has been one of the cornerstone applications of mass spectrometry for decades. This tutorial reviews the basics of the interpretation of electrospray ionization-based MS and MS/MS spectra in order to identify small-molecule analytes (typically below 2000 Da). Most of what is discussed in this tutorial also applies to other atmospheric pressure ionization methods like atmospheric pressure chemical/photoionization. We focus primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds, rather than describing strategies for large-scale identification in complex samples. We critically discuss topics like the detection of protonated and deprotonated ions ([M + H]+ and [M - H]- ) as well as other adduct ions, the determination of the molecular formula, and provide some basic rules on the interpretation of product ion spectra. Our tutorial focuses primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds (eg, contaminants in chemical production, pharmacological alteration of drugs), rather than describing strategies for large-scale identification in complex samples. This tutorial also discusses strategies to obtain useful orthogonal information (UV/Vis, H/D exchange, chemical derivatization, etc) and offers an overview of the different informatics tools and approaches that can be used for structural elucidation of small molecules. It is primarily intended for beginning mass spectrometrists and researchers from other mass spectrometry sub-disciplines that want to get acquainted with structural elucidation are interested in some practical tips and tricks.

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.

Recent (2000-2015) developments in the analysis of minor unknown natural products based on characteristic fragment information using LC-MS

Liquid chromatography-Mass Spectrometry (LC-MS) has been widely used in natural product analysis. Global detection and identification of nontargeted components are desirable in natural product research, for example, in quality control of Chinese herbal medicine. Nontargeted components analysis continues to expand to exciting life science application domains such as metabonomics. With this background, the present review summarizes recent developments in the analysis of minor unknown natural products using LC-MS and mainly focuses on the determination of the molecular formulae, selection of precursor ions, and characteristic fragmentation patterns of the known compounds. This review consists of three parts. Firstly, the methods used to determine unique molecular formula of unknown compounds such as accurate mass measurements, MSⁿ spectra, or relative isotopic abundance information, are introduced. Secondly, the methods improving signal-to-noise ratio of MS/MS spectra by manual-MS/MS or workflow targeting-only signals were elucidated; pure precursor ions can be selected by changing the precursor ion isolated window. Lastly, characteristic fragmentation patterns such as Retro-Diels-Alder (RDA), McLafferty rearrangements, "internal residue loss," and so on, occurring in the molecular ions of natural products are summarized. Classical application of characteristic fragmentation patterns in identifying unknown compounds in extracts and relevant fragmentation mechanisms are presented (RDA reactions occurring readily in the molecular ions of flavanones or isoflavanones, McLafferty-type fragmentation reactions of some natural products such as epipolythiodioxopiperazines; fragmentation by "internal residue loss" possibly involving ion-neutral complex intermediates). © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:202-216, 2018.

Significance estimation for large scale metabolomics annotations by spectral matching

The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from -92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science.

Mining molecular structure databases: Identification of small molecules based on fragmentation mass spectrometry data

Mass spectrometry (MS) is a key technology for the analysis of small molecules. For the identification and structural elucidation of novel molecules, new approaches beyond straightforward spectral comparison are required. In this review, we will cover computational methods that help with the identification of small molecules by analyzing fragmentation MS data. We focus on the four main approaches to mine a database of metabolite structures, that is rule-based fragmentation spectrum prediction, combinatorial fragmentation, competitive fragmentation modeling, and molecular fingerprint prediction. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:624-633, 2017.

Quantum chemical calculation of electron ionization mass spectra for general organic and inorganic molecules

We introduce a fully stand-alone version of the Quantum Chemistry Electron Ionization Mass Spectra (QCEIMS) program [S. Grimme, Angew. Chem. Int. Ed., 2013, 52, 6306] allowing efficient simulations for molecules composed of elements with atomic numbers up to Z = 86. The recently developed extended tight-binding semi-empirical method GFN-xTB has been combined with QCEIMS, thereby eliminating dependencies on third-party electronic structure software. Furthermore, for reasonable calculations of ionization potentials, as required by the method, a second tight-binding variant, IPEA-xTB, is introduced here. This novel combination of methods allows the automatic, fast and reasonably accurate computation of electron ionization mass spectra for structurally different molecules across the periodic table. In order to validate and inspect the transferability of the method, we perform large-scale simulations for some representative organic, organometallic, and main-group inorganic systems. Theoretical spectra for 23 molecules are compared directly to experimental data taken from standard databases. For the first time, realistic quantum chemistry based EI-MS for organometallic systems like ferrocene or copper(ii)acetylacetonate are presented. Compared to previously used semiempirical methods, GFN-xTB is faster, more robust, and yields overall higher quality spectra. The partially analysed theoretical reaction and fragmentation mechanisms are chemically reasonable and reveal in unprecedented detail the extreme complexity of high energy gas phase ion chemistry including complicated rearrangement reactions prior to dissociation.

Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.

Effect-directed analysis supporting monitoring of aquatic environments--An in-depth overview

Aquatic environments are often contaminated with complex mixtures of chemicals that may pose a risk to ecosystems and human health. This contamination cannot be addressed with target analysis alone but tools are required to reduce this complexity and identify those chemicals that might cause adverse effects. Effect-directed analysis (EDA) is designed to meet this challenge and faces increasing interest in water and sediment quality monitoring. Thus, the present paper summarizes current experience with the EDA approach and the tools required, and provides practical advice on their application. The paper highlights the need for proper problem formulation and gives general advice for study design. As the EDA approach is directed by toxicity, basic principles for the selection of bioassays are given as well as a comprehensive compilation of appropriate assays, including their strengths and weaknesses. A specific focus is given to strategies for sampling, extraction and bioassay dosing since they strongly impact prioritization of toxicants in EDA. Reduction of sample complexity mainly relies on fractionation procedures, which are discussed in this paper, including quality assurance and quality control. Automated combinations of fractionation, biotesting and chemical analysis using so-called hyphenated tools can enhance the throughput and might reduce the risk of artifacts in laboratory work. The key to determining the chemical structures causing effects is analytical toxicant identification. The latest approaches, tools, software and databases for target-, suspect and non-target screening as well as unknown identification are discussed together with analytical and toxicological confirmation approaches. A better understanding of optimal use and combination of EDA tools will help to design efficient and successful toxicant identification studies in the context of quality monitoring in multiply stressed environments.

Fragmentation trees reloaded

BACKGROUND

Untargeted metabolomics commonly uses liquid chromatography mass spectrometry to measure abundances of metabolites; subsequent tandem mass spectrometry is used to derive information about individual compounds. One of the bottlenecks in this experimental setup is the interpretation of fragmentation spectra to accurately and efficiently identify compounds. Fragmentation trees have become a powerful tool for the interpretation of tandem mass spectrometry data of small molecules. These trees are determined from the data using combinatorial optimization, and aim at explaining the experimental data via fragmentation cascades. Fragmentation tree computation does not require spectral or structural databases. To obtain biochemically meaningful trees, one needs an elaborate optimization function (scoring).

RESULTS

We present a new scoring for computing fragmentation trees, transforming the combinatorial optimization into a Maximum A Posteriori estimator. We demonstrate the superiority of the new scoring for two tasks: both for the de novo identification of molecular formulas of unknown compounds, and for searching a database for structurally similar compounds, our method SIRIUS 3, performs significantly better than the previous version of our method, as well as other methods for this task.

CONCLUSION

SIRIUS 3 can be a part of an untargeted metabolomics workflow, allowing researchers to investigate unknowns using automated computational methods.Graphical abstractWe present a new scoring for computing fragmentation trees from tandem mass spectrometry data based on Bayesian statistics. The best scoring fragmentation tree most likely explains the molecular formula of the measured parent ion.