MolFind: a software package enabling HPLC/MS-based identification of unknown chemical structures

Highly accurate and large-scale collision cross sections prediction with graph neural networks

The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS . Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures.

Exploring Glycosylated Soy Isoflavones Affinities toward G-tetrads as Studied by Survival Yield Method

Taking soy-based food supplements for menopausal symptoms by women may reduce the risk of cancer. Therefore, the interaction between nucleic acids (or their constituents) and ingredients of the supplements, e. g., isoflavone glucosides, on the molecular level, has been of interest with respect to cancer therapy. In this work, the interaction between isoflavone glucosides and G-tetrads, namely [4G+Na]⁺ ions (G stands for guanosine or deoxyguanosine), were analyzed by using electrospray ionization-collision induced dissociation-mass spectrometry (ESI-CID-MS) and survival yields method. The strength of isoflavone glucosides-[4G+Na]⁺ interaction in the gas phase was determined from E_com50 - the energy required to fragment 50 % of selected precursor ions. Glycitin-[4G+Na]⁺ interaction was found to be the strongest, and the interaction between isoflavone glucosides and guanosine tetrad was established to be stronger than that between isoflavone glucosides and deoxyguanosine tetrad.

MAD HATTER Correctly Annotates 98% of Small Molecule Tandem Mass Spectra Searching in PubChem

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics usually relies on mass spectrometry, a technology capable of detecting thousands of compounds in a biological sample. Metabolite annotation is executed using tandem mass spectrometry. Spectral library search is far from comprehensive, and numerous compounds remain unannotated. So-called in silico methods allow us to overcome the restrictions of spectral libraries, by searching in much larger molecular structure databases. Yet, after more than a decade of method development, in silico methods still do not reach the correct annotation rates that users would wish for. Here, we present a novel computational method called Mad Hatter for this task. Mad Hatter combines CSI:FingerID results with information from the searched structure database via a metascore. Compound information includes the melting point, and the number of words in the compound description starting with the letter ‘u’. We then show that Mad Hatter reaches a stunning 97.6% correct annotations when searching PubChem, one of the largest and most comprehensive molecular structure databases. Unfortunately, Mad Hatter is not a real method. Rather, we developed Mad Hatter solely for the purpose of demonstrating common issues in computational method development and evaluation. We explain what evaluation glitches were necessary for Mad Hatter to reach this annotation level, what is wrong with similar metascores in general, and why metascores may screw up not only method evaluations but also the analysis of biological experiments. This paper may serve as an example of problems in the development and evaluation of machine learning models for metabolite annotation.

Machine learning for identification of silylated derivatives from mass spectra

Motivation

Compound structure identification is using increasingly more sophisticated computational tools, among which machine learning tools are a recent addition that quickly gains in importance. These tools, of which the method titled Compound Structure Identification:Input Output Kernel Regression (CSI:IOKR) is an excellent example, have been used to elucidate compound structure from mass spectral (MS) data with significant accuracy, confidence and speed. They have, however, largely focused on data coming from liquid chromatography coupled to tandem mass spectrometry (LC–MS).

Gas chromatography coupled to mass spectrometry (GC–MS) is an alternative which offers several advantages as compared to LC–MS, including higher data reproducibility. Of special importance is the substantial compound coverage offered by GC–MS, further expanded by derivatization procedures, such as silylation, which can improve the volatility, thermal stability and chromatographic peak shape of semi-volatile analytes. Despite these advantages and the increasing size of compound databases and MS libraries, GC–MS data have not yet been used by machine learning approaches to compound structure identification.

Results

This study presents a successful application of the CSI:IOKR machine learning method for the identification of environmental contaminants from GC–MS spectra. We use CSI:IOKR as an alternative to exhaustive search of MS libraries, independent of instrumental platform and data processing software. We use a comprehensive dataset of GC–MS spectra of trimethylsilyl derivatives and their molecular structures, derived from a large commercially available MS library, to train a model that maps between spectra and molecular structures. We test the learned model on a different dataset of GC–MS spectra of trimethylsilyl derivatives of environmental contaminants, generated in-house and made publicly available. The results show that 37% (resp. 50%) of the tested compounds are correctly ranked among the top 10 (resp. 20) candidate compounds suggested by the model. Even though spectral comparisons with reference standards or de novo structural elucidations are neccessary to validate the predictions, machine learning provides efficient candidate prioritization and reduction of the time spent for compound annotation.

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Plants Metabolome Study: Emerging Tools and Techniques

Metabolomics is now considered a wide-ranging, sensitive and practical approach to acquire useful information on the composition of a metabolite pool present in any organism, including plants. Investigating metabolomic regulation in plants is essential to understand their adaptation, acclimation and defense responses to environmental stresses through the production of numerous metabolites. Moreover, metabolomics can be easily applied for the phenotyping of plants; and thus, it has great potential to be used in genome editing programs to develop superior next-generation crops. This review describes the recent analytical tools and techniques available to study plants metabolome, along with their significance of sample preparation using targeted and non-targeted methods. Advanced analytical tools, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography mass-spectroscopy (LC-MS), capillary electrophoresis-mass spectrometry (CE-MS), fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) matrix-assisted laser desorption/ionization (MALDI), ion mobility spectrometry (IMS) and nuclear magnetic resonance (NMR) have speed up precise metabolic profiling in plants. Further, we provide a complete overview of bioinformatics tools and plant metabolome database that can be utilized to advance our knowledge to plant biology.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

Challenges in Identifying the Dark Molecules of Life

Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.

Evaluation of an Artificial Neural Network Retention Index Model for Chemical Structure Identification in Nontargeted Metabolomics

Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) is a major analytical technique used for nontargeted identification of metabolites in biological fluids. Typically, in LC-ESI-MS/MS based database assisted structure elucidation pipelines, the exact mass of an unknown compound is used to mine a chemical structure database to acquire an initial set of possible candidates. Subsequent matching of the collision induced dissociation (CID) spectrum of the unknown to the CID spectra of candidate structures facilitates identification. However, this approach often fails because of the large numbers of potential candidates (i.e., false positives) for which CID spectra are not available. To overcome this problem, CID fragmentation predication programs have been developed, but these also have limited success if large numbers of isomers with similar CID spectra are present in the candidate set. In this study, we investigated the use of a retention index (RI) predictive model as an orthogonal method to help improve identification rates. The model was used to eliminate candidate structures whose predicted RI values differed significantly from the experimentally determined RI value of the unknown compound. We tested this approach using a set of ninety-one endogenous metabolites and four in silico CID fragmentation algorithms: CFM-ID, CSI:FingerID, Mass Frontier, and MetFrag. Candidate sets obtained from PubChem and the Human Metabolite Database (HMDB) were ranked with and without RI filtering followed by in silico spectral matching. Upon RI filtering, 12 of the ninety-one metabolites were eliminated from their respective candidate sets, i.e., were scored incorrectly as negatives. For the remaining seventy-nine compounds, we show that RI filtering eliminated an average of 58% from PubChem candidate sets. This resulted in an approximately 2-fold improvement in average rankings when using CFM-ID, Mass Frontier, and MetFrag. In addition, RI filtering slightly increased the occurrence of number one rankings for all 4 fragmentation algorithms. However, RI filtering did not significantly improve average rankings when HMDB was used as the candidate database, nor did it significantly improve average rankings when using CSI:FingerID. Overall, we show that the current RI model incorrectly eliminated more true positives (12) than were expected (4-5) on the basis of the filtering method. However, it slightly improved the number of correct first place rankings and improved overall average rankings when using CFM-ID, Mass Frontier, and MetFrag.

Development of a Reverse Phase HPLC Retention Index Model for Nontargeted Metabolomics Using Synthetic Compounds

The MolFind application has been developed as a nontargeted metabolomics chemometric tool to facilitate structure identification when HPLC biofluids analysis reveals a feature of interest. Here synthetic compounds are selected and measured to form the basis of a new, more accurate, HPLC retention index model for use with MolFind. We show that relatively inexpensive synthetic screening compounds with simple structures can be used to develop an artificial neural network model that is successful in making quality predictions for human metabolites. A total of 1955 compounds were obtained and measured for the model. A separate set of 202 human metabolites was used for independent validation. The new ANN model showed improved accuracy over previous models. The model, based on relatively simple compounds, was able to make quality predictions for complex compounds not similar to training data. Independent validation metabolites with feature combinations found in three or more training compounds were predicted with 97% sensitivity while metabolites with feature combinations found in less than three training compounds were predicted with >90% sensitivity. The study describes the method used to select synthetic compounds and new descriptors developed to encode the relationship between lipophilic molecular subgraphs and HPLC retention. Finally, we introduce the QRI (qualitative range of interest) modification of neural network backpropagation learning to generate models simultaneously based on quantitative and qualitative data.

Annotation: A Computational Solution for Streamlining Metabolomics Analysis

Metabolite identification is still considered an imposing bottleneck in liquid chromatography mass spectrometry (LC/MS) untargeted metabolomics. The identification workflow usually begins with detecting relevant LC/MS peaks via peak-picking algorithms and retrieving putative identities based on accurate mass searching. However, accurate mass search alone provides poor evidence for metabolite identification. For this reason, computational annotation is used to reveal the underlying metabolites monoisotopic masses, improving putative identification in addition to confirmation with tandem mass spectrometry. This review examines LC/MS data from a computational and analytical perspective, focusing on the occurrence of neutral losses and in-source fragments, to understand the challenges in computational annotation methodologies. Herein, we examine the state-of-the-art strategies for computational annotation including: (i) peak grouping or full scan (MS1) pseudo-spectra extraction, i.e., clustering all mass spectral signals stemming from each metabolite; (ii) annotation using ion adduction and mass distance among ion peaks; (iii) incorporation of biological knowledge such as biotransformations or pathways; (iv) tandem MS data; and (v) metabolite retention time calibration, usually achieved by prediction from molecular descriptors. Advantages and pitfalls of each of these strategies are discussed, as well as expected future trends in computational annotation.

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.

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.

compMS2Miner: An Automatable Metabolite Identification, Visualization, and Data-Sharing R Package for High-Resolution LC-MS Data Sets

A long-standing challenge of untargeted metabolomic profiling by ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is efficient transition from unknown mass spectral features to confident metabolite annotations. The compMS2Miner (Comprehensive MS2 Miner) package was developed in the R language to facilitate rapid, comprehensive feature annotation using a peak-picker-output and MS2 data files as inputs. The number of MS2 spectra that can be collected during a metabolomic profiling experiment far outweigh the amount of time required for pain-staking manual interpretation; therefore, a degree of software workflow autonomy is required for broad-scale metabolite annotation. CompMS2Miner integrates many useful tools in a single workflow for metabolite annotation and also provides a means to overview the MS2 data with a Web application GUI compMS2Explorer (Comprehensive MS2 Explorer) that also facilitates data-sharing and transparency. The automatable compMS2Miner workflow consists of the following steps: (i) matching unknown MS1 features to precursor MS2 scans, (ii) filtration of spectral noise (dynamic noise filter), (iii) generation of composite mass spectra by multiple similar spectrum signal summation and redundant/contaminant spectra removal, (iv) interpretation of possible fragment ion substructure using an internal database, (v) annotation of unknowns with chemical and spectral databases with prediction of mammalian biotransformation metabolites, wrapper functions for in silico fragmentation software, nearest neighbor chemical similarity scoring, random forest based retention time prediction, text-mining based false positive removal/true positive ranking, chemical taxonomic prediction and differential evolution based global annotation score optimization, and (vi) network graph visualizations, data curation, and sharing are made possible via the compMS2Explorer application. Metabolite identities and comments can also be recorded using an interactive table within compMS2Explorer. The utility of the package is illustrated with a data set of blood serum samples from 7 diet induced obese (DIO) and 7 nonobese (NO) C57BL/6J mice, which were also treated with an antibiotic (streptomycin) to knockdown the gut microbiota. The results of fully autonomous and objective usage of compMS2Miner are presented here. All automatically annotated spectra output by the workflow are provided in the Supporting Information and can alternatively be explored as publically available compMS2Explorer applications for both positive and negative modes ( https://wmbedmands.shinyapps.io/compMS2_mouseSera_POS and https://wmbedmands.shinyapps.io/compMS2_mouseSera_NEG ). The workflow provided rapid annotation of a diversity of endogenous and gut microbially derived metabolites affected by both diet and antibiotic treatment, which conformed to previously published reports. Composite spectra (n = 173) were autonomously matched to entries of the Massbank of North America (MoNA) spectral repository. These experimental and virtual (lipidBlast) spectra corresponded to 29 common endogenous compound classes (e.g., 51 lysophosphatidylcholines spectra) and were then used to calculate the ranking capability of 7 individual scoring metrics. It was found that an average of the 7 individual scoring metrics provided the most effective weighted average ranking ability of 3 for the MoNA matched spectra in spite of potential risk of false positive annotations emerging from automation. Minor structural differences such as relative carbon-carbon double bond positions were found in several cases to affect the correct rank of the MoNA annotated metabolite. The latest release and an example workflow is available in the package vignette ( https://github.com/WMBEdmands/compMS2Miner ) and a version of the published application is available on the shinyapps.io site ( https://wmbedmands.shinyapps.io/compMS2Example ).

Metabolomics and lipidomics using traveling-wave ion mobility mass spectrometry

Metabolomics and lipidomics aim to profile the wide range of metabolites and lipids that are present in biological samples. Recently, ion mobility spectrometry (IMS) has been used to support metabolomics and lipidomics applications to facilitate the separation and the identification of complex mixtures of analytes. IMS is a gas-phase electrophoretic technique that enables the separation of ions in the gas phase according to their charge, shape and size. Occurring within milliseconds, IMS separation is compatible with modern mass spectrometry (MS) operating with microsecond scan speeds. Thus, the time required for acquiring IMS data does not affect the overall run time of traditional liquid chromatography (LC)-MS-based metabolomics and lipidomics experiments. The addition of IMS to conventional LC-MS-based metabolomics and lipidomics workflows has been shown to enhance peak capacity, spectral clarity and fragmentation specificity. Moreover, by enabling determination of a collision cross-section (CCS) value-a parameter related to the shape of ions-IMS can improve the accuracy of metabolite identification. In this protocol, we describe how to integrate traveling-wave ion mobility spectrometry (TWIMS) into traditional LC-MS-based metabolomic and lipidomic workflows. In particular, we describe procedures for the following: tuning and calibrating a SYNAPT High-Definition MS (HDMS) System (Waters) specifically for metabolomics and lipidomics applications; extracting polar metabolites and lipids from brain samples; setting up appropriate chromatographic conditions; acquiring simultaneously m/z, retention time and CCS values for each analyte; processing and analyzing data using dedicated software solutions, such as Progenesis QI (Nonlinear Dynamics); and, finally, performing metabolite and lipid identification using CCS databases and TWIMS-derived fragmentation information.

Development of Database Assisted Structure Identification (DASI) Methods for Nontargeted Metabolomics

Metabolite structure identification remains a significant challenge in nontargeted metabolomics research. One commonly used strategy relies on searching biochemical databases using exact mass. However, this approach fails when the database does not contain the unknown metabolite (i.e., for unknown-unknowns). For these cases, constrained structure generation with combinatorial structure generators provides a potential option. Here we evaluated structure generation constraints based on the specification of: (1) substructures required (i.e., seed structures); (2) substructures not allowed; and (3) filters to remove incorrect structures. Our approach (database assisted structure identification, DASI) used predictive models in MolFind to find candidate structures with chemical and physical properties similar to the unknown. These candidates were then used for seed structure generation using eight different structure generation algorithms. One algorithm was able to generate correct seed structures for 21/39 test compounds. Eleven of these seed structures were large enough to constrain the combinatorial structure generator to fewer than 100,000 structures. In 35/39 cases, at least one algorithm was able to generate a correct seed structure. The DASI method has several limitations and will require further experimental validation and optimization. At present, it seems most useful for identifying the structure of unknown-unknowns with molecular weights <200 Da.

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.

Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data

BACKGROUND

Artificial Neural Networks (ANN) are extensively used to model 'omics' data. Different modeling methodologies and combinations of adjustable parameters influence model performance and complicate model optimization.

METHODOLOGY

We evaluated optimization of four ANN modeling parameters (learning rate annealing, stopping criteria, data split method, network architecture) using retention index (RI) data for 390 compounds. Models were assessed by independent validation (I-Val) using newly measured RI values for 1492 compounds.

CONCLUSION

The best model demonstrated an I-Val standard error of 55 RI units and was built using a Ward's clustering data split and a minimally nonlinear network architecture. Use of validation statistics for stopping and final model selection resulted in better independent validation performance than the use of test set statistics.

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.

Non-targeted screening approaches for contaminants and adulterants in food using liquid chromatography hyphenated to high resolution mass spectrometry

The majority of analytical methods for food safety monitor the presence of a specific compound or defined set of compounds. Non-targeted screening methods are complementary to these approaches by detecting and identifying unexpected compounds present in food matrices that may be harmful to public health. However, the development and implementation of generalized non-targeted screening workflows are particularly challenging, especially for food matrices due to inherent sample complexity and diversity and a large analyte concentration range. One approach that can be implemented is liquid chromatography coupled to high-resolution mass spectrometry, which serves to reduce this complexity and is capable of generating molecular formulae for compounds of interest. Current capabilities, strategies, and challenges will be reviewed for sample preparation, mass spectrometry, chromatography, and data processing workflows. Considerations to increase the accuracy and speed of identifying unknown molecular species will also be addressed, including suggestions for achieving sufficient data quality for non-targeted screening applications.

MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics

BACKGROUND

In spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography-mass spectrometry (LC-MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases.

DESCRIPTION

Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC-MS accurate mass data enabled the identity of an unknown peak to be confidently predicted.

CONCLUSIONS

MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures. Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.

PredRet: prediction of retention time by direct mapping between multiple chromatographic systems

Demands in research investigating small molecules by applying untargeted approaches have been a key motivator for the development of repositories for mass spectrometry spectra and automated tools to aid compound identification. Comparatively little attention has been afforded to using retention times (RTs) to distinguish compounds and for liquid chromatography there are currently no coordinated efforts to share and exploit RT information. We therefore present PredRet; the first tool that makes community sharing of RT information possible across laboratories and chromatographic systems (CSs). At http://predret.org , a database of RTs from different CSs is available and users can upload their own experimental RTs and download predicted RTs for compounds which they have not experimentally determined in their own experiments. For each possible pair of CSs in the database, the RTs are used to construct a projection model between the RTs in the two CSs. The number of compounds for which RTs can be predicted and the accuracy of the predictions are dependent upon the compound coverage overlap between the CSs used for construction of projection models. At the moment, it is possible to predict up to 400 RTs with a median error between 0.01 and 0.28 min depending on the CS and the median width of the prediction interval ranging from 0.08 to 1.86 min. By comparing experimental and predicted RTs, the user can thus prioritize which isomers to target for further characterization and potentially exclude some structures completely. As the database grows, the number and accuracy of predictions will increase.

Current status and prospects of computational resources for natural product dereplication: a review

Research in natural products has always enhanced drug discovery by providing new and unique chemical compounds. However, recently, drug discovery from natural products is slowed down by the increasing chance of re-isolating known compounds. Rapid identification of previously isolated compounds in an automated manner, called dereplication, steers researchers toward novel findings, thereby reducing the time and effort for identifying new drug leads. Dereplication identifies compounds by comparing processed experimental data with those of known compounds, and so, diverse computational resources such as databases and tools to process and compare compound data are necessary. Automating the dereplication process through the integration of computational resources has always been an aspired goal of natural product researchers. To increase the utilization of current computational resources for natural products, we first provide an overview of the dereplication process, and then list useful resources, categorizing into databases, methods and software tools and further explaining them from a dereplication perspective. Finally, we discuss the current challenges to automating dereplication and proposed solutions.

Evaluation of steroidomics by liquid chromatography hyphenated to mass spectrometry as a powerful analytical strategy for measuring human steroid perturbations

This review presents the evolution of steroid analytical techniques, including gas chromatography coupled to mass spectrometry (GC-MS), immunoassay (IA) and targeted liquid chromatography coupled to mass spectrometry (LC-MS), and it evaluates the potential of extended steroid profiles by a metabolomics-based approach, namely steroidomics. Steroids regulate essential biological functions including growth and reproduction, and perturbations of the steroid homeostasis can generate serious physiological issues; therefore, specific and sensitive methods have been developed to measure steroid concentrations. GC-MS measuring several steroids simultaneously was considered the first historical standard method for analysis. Steroids were then quantified by immunoassay, allowing a higher throughput; however, major drawbacks included the measurement of a single compound instead of a panel and cross-reactivity reactions. Targeted LC-MS methods with selected reaction monitoring (SRM) were then introduced for quantifying a small steroid subset without the problems of cross-reactivity. The next step was the integration of metabolomic approaches in the context of steroid analyses. As metabolomics tends to identify and quantify all the metabolites (i.e., the metabolome) in a specific system, appropriate strategies were proposed for discovering new biomarkers. Steroidomics, defined as the untargeted analysis of the steroid content in a sample, was implemented in several fields, including doping analysis, clinical studies, in vivo or in vitro toxicology assays, and more. This review discusses the current analytical methods for assessing steroid changes and compares them to steroidomics. Steroids, their pathways, their implications in diseases and the biological matrices in which they are analysed will first be described. Then, the different analytical strategies will be presented with a focus on their ability to obtain relevant information on the steroid pattern. The future technical requirements for improving steroid analysis will also be presented.

Metabolic pathway predictions for metabolomics: a molecular structure matching approach

Metabolic pathways are composed of a series of chemical reactions occurring within a cell. In each pathway, enzymes catalyze the conversion of substrates into structurally similar products. Thus, structural similarity provides a potential means for mapping newly identified biochemical compounds to known metabolic pathways. In this paper, we present TrackSM, a cheminformatics tool designed to associate a chemical compound to a known metabolic pathway based on molecular structure matching techniques. Validation experiments show that TrackSM is capable of associating 93% of tested structures to their correct KEGG pathway class and 88% to their correct individual KEGG pathway. This suggests that TrackSM may be a valuable tool to aid in associating previously unknown small molecules to known biochemical pathways and improve our ability to link metabolomics, proteomic, and genomic data sets. TrackSM is freely available at http://metabolomics.pharm.uconn.edu/?q=Software.html .

Ion mobility-derived collision cross section as an additional measure for lipid fingerprinting and identification

Despite recent advances in analytical and computational chemistry, lipid identification remains a significant challenge in lipidomics. Ion-mobility spectrometry provides an accurate measure of the molecules' rotationally averaged collision cross-section (CCS) in the gas phase and is thus related to ionic shape. Here, we investigate the use of CCS as a highly specific molecular descriptor for identifying lipids in biological samples. Using traveling wave ion mobility mass spectrometry (MS), we measured the CCS values of over 200 lipids within multiple chemical classes. CCS values derived from ion mobility were not affected by instrument settings or chromatographic conditions, and they were highly reproducible on instruments located in independent laboratories (interlaboratory RSD < 3% for 98% of molecules). CCS values were used as additional molecular descriptors to identify brain lipids using a variety of traditional lipidomic approaches. The addition of CCS improved the reproducibility of analysis in a liquid chromatography-MS workflow and maximized the separation of isobaric species and the signal-to-noise ratio in direct-MS analyses (e.g., "shotgun" lipidomics and MS imaging). These results indicate that adding CCS to databases and lipidomics workflows increases the specificity and selectivity of analysis, thus improving the confidence in lipid identification compared to traditional analytical approaches. The CCS/accurate-mass database described here is made publicly available.

Ion mobility derived collision cross sections to support metabolomics applications

Metabolomics is a rapidly evolving analytical approach in life and health sciences. The structural elucidation of the metabolites of interest remains a major analytical challenge in the metabolomics workflow. Here, we investigate the use of ion mobility as a tool to aid metabolite identification. Ion mobility allows for the measurement of the rotationally averaged collision cross-section (CCS), which gives information about the ionic shape of a molecule in the gas phase. We measured the CCSs of 125 common metabolites using traveling-wave ion mobility-mass spectrometry (TW-IM-MS). CCS measurements were highly reproducible on instruments located in three independent laboratories (RSD < 5% for 99%). We also determined the reproducibility of CCS measurements in various biological matrixes including urine, plasma, platelets, and red blood cells using ultra performance liquid chromatography (UPLC) coupled with TW-IM-MS. The mean RSD was < 2% for 97% of the CCS values, compared to 80% of retention times. Finally, as proof of concept, we used UPLC-TW-IM-MS to compare the cellular metabolome of epithelial and mesenchymal cells, an in vitro model used to study cancer development. Experimentally determined and computationally derived CCS values were used as orthogonal analytical parameters in combination with retention time and accurate mass information to confirm the identity of key metabolites potentially involved in cancer. Thus, our results indicate that adding CCS data to searchable databases and to routine metabolomics workflows will increase the identification confidence compared to traditional analytical approaches.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.

Identification of novel micropollutants in wastewater by a combination of suspect and nontarget screening

To detect site-specific, suspected and formerly unknown contaminants in a wastewater treatment plant effluent, we established a screening procedure based on liquid chromatography-high resolution mass spectrometry (LC-HRMS) with stepwise identification schemes. Based on automated substructure searches a list of 2160 suspected site-specific and documented water contaminants was reduced to those amenable to LC-HRMS. After searching chromatograms for exact masses of suspects, presumably false positive detections were stepwise excluded by retention time prediction, the evaluation of isotope patterns, ionization behavior, and HRMS/MS spectra. In nontarget analysis, peaks for identification were selected based on distinctive isotope patterns and intensity. The stepwise identification of nontarget compounds was automated by a plausibility check of molecular formulas using the Seven Golden Rules, an exclusion of compounds with presumably low commercial importance and an automated HRMS/MS evaluation. Six suspected and five nontarget chemicals were identified, of which two have not been previously reported as environmental pollutants.

In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics

Current methods of structure identification in mass-spectrometry-based nontargeted metabolomics rely on matching experimentally determined features of an unknown compound to those of candidate compounds contained in biochemical databases. A major limitation of this approach is the relatively small number of compounds currently included in these databases. If the correct structure is not present in a database, it cannot be identified, and if it cannot be identified, it cannot be included in a database. Thus, there is an urgent need to augment metabolomics databases with rationally designed biochemical structures using alternative means. Here we present the In Vivo/In Silico Metabolites Database (IIMDB), a database of in silico enzymatically synthesized metabolites, to partially address this problem. The database, which is available at http://metabolomics.pharm.uconn.edu/iimdb/, includes ~23,000 known compounds (mammalian metabolites, drugs, secondary plant metabolites, and glycerophospholipids) collected from existing biochemical databases plus more than 400,000 computationally generated human phase-I and phase-II metabolites of these known compounds. IIMDB features a user-friendly web interface and a programmer-friendly RESTful web service. Ninety-five percent of the computationally generated metabolites in IIMDB were not found in any existing database. However, 21,640 were identical to compounds already listed in PubChem, HMDB, KEGG, or HumanCyc. Furthermore, the vast majority of these in silico metabolites were scored as biological using BioSM, a software program that identifies biochemical structures in chemical structure space. These results suggest that in silico biochemical synthesis represents a viable approach for significantly augmenting biochemical databases for nontargeted metabolomics applications.

Human metabolomics: strategies to understand biology

Metabolomics provides a direct functional read-out of the physiological status of an organism and is in principle ideally suited to describe someone's health status. Whereas only a limited number of small metabolites are used in the clinics, in inborn errors of metabolism an extensive repertoire of metabolites are used as biomarkers. We discuss that the proper clinical phenotyping is crucial to find biomarkers and obtain biological insights for multifactorial diseases. This requires to study the phenotype dynamics including the concepts of homeostasis and allostasis, that is, the ability to adapt and cope with a challenge. We also elaborate that biology-driven metabolomics platforms (i.e. development of metabolomics technology driven by the need of studying and answering important biomedical questions) addressing clinically relevant pathways and at the same time providing absolute concentrations are key to allow discovery and validation of biomarkers across studies and labs. Following individuals over years will require high throughput metabolomics approaches, which are emerging for nuclear magnetic resonance spectroscopy and direct-infusion mass spectrometry, but should also include the biochemical networks needed for personalized health monitoring.

Chemical structure identification in metabolomics: computational modeling of experimental features

The identification of compounds in complex mixtures remains challenging despite recent advances in analytical techniques. At present, no single method can detect and quantify the vast array of compounds that might be of potential interest in metabolomics studies. High performance liquid chromatography/mass spectrometry (HPLC/MS) is often considered the analytical method of choice for analysis of biofluids. The positive identification of an unknown involves matching at least two orthogonal HPLC/MS measurements (exact mass, retention index, drift time etc.) against an authentic standard. However, due to the limited availability of authentic standards, an alternative approach involves matching known and measured features of the unknown compound with computationally predicted features for a set of candidate compounds downloaded from a chemical database. Computationally predicted features include retention index, ECOM₅₀ (energy required to decompose 50% of a selected precursor ion in a collision induced dissociation cell), drift time, whether the unknown compound is biological or synthetic and a collision induced dissociation (CID) spectrum. Computational predictions are used to filter the initial “bin” of candidate compounds. The final output is a ranked list of candidates that best match the known and measured features. In this mini review, we discuss cheminformatics methods underlying this database search-filter identification approach.

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.

BioSM: metabolomics tool for identifying endogenous mammalian biochemical structures in chemical structure space

The structural identification of unknown biochemical compounds in complex biofluids continues to be a major challenge in metabolomics research. Using LC/MS, there are currently two major options for solving this problem: searching small biochemical databases, which often do not contain the unknown of interest or searching large chemical databases which include large numbers of nonbiochemical compounds. Searching larger chemical databases (larger chemical space) increases the odds of identifying an unknown biochemical compound, but only if nonbiochemical structures can be eliminated from consideration. In this paper we present BioSM; a cheminformatics tool that uses known endogenous mammalian biochemical compounds (as scaffolds) and graph matching methods to identify endogenous mammalian biochemical structures in chemical structure space. The results of a comprehensive set of empirical experiments suggest that BioSM identifies endogenous mammalian biochemical structures with high accuracy. In a leave-one-out cross validation experiment, BioSM correctly predicted 95% of 1388 Kyoto Encyclopedia of Genes and Genomes (KEGG) compounds as endogenous mammalian biochemicals using 1565 scaffolds. Analysis of two additional biological data sets containing 2330 human metabolites (HMDB) and 2416 plant secondary metabolites (KEGG) resulted in biochemical annotations of 89% and 72% of the compounds, respectively. When a data set of 3895 drugs (DrugBank and USAN) was tested, 48% of these structures were predicted to be biochemical. However, when a set of synthetic chemical compounds (Chembridge and Chemsynthesis databases) were examined, only 29% of the 458,207 structures were predicted to be biochemical. Moreover, BioSM predicted that 34% of 883,199 randomly selected compounds from PubChem were biochemical. We then expanded the scaffold list to 3927 biochemical compounds and reevaluated the above data sets to determine whether scaffold number influenced model performance. Although there were significant improvements in model sensitivity and specificity using the larger scaffold list, the data set comparison results were very similar. These results suggest that additional biochemical scaffolds will not further improve our representation of biochemical structure space and that the model is reasonably robust. BioSM provides a qualitative (yes/no) and quantitative (ranking) method for endogenous mammalian biochemical annotation of chemical space and, thus, will be useful in the identification of unknown biochemical structures in metabolomics. BioSM is freely available at http://metabolomics.pharm.uconn.edu.