Facilitate Compound Identification Research Articles

Predicting Collision Cross-Section Values for Small Molecules through Chemical Class-Based Multimodal Graph Attention Network.

Libraries of collision cross-section (CCS) values have the potential to facilitate compound identification in metabolomics. Although computational methods provide an opportunity to increase library size rapidly, accurate prediction of CCS values remains challenging due to the structural diversity of small molecules. Here, we developed a machine learning (ML) model that integrates graph attention networks and multimodal molecular representations to predict CCS values on the basis of chemical class. Our approach, referred to as MGAT-CCS, had superior performance in comparison to other ML models in CCS prediction. MGAT-CCS achieved a median relative error of 0.47%/1.14% (positive/negative mode) and 1.40%/1.63% (positive/negative mode) for lipids and metabolites, respectively. When MGAT-CCS was applied to real-world metabolomics data, it reduced the number of false metabolite candidates by roughly 25% across multiple sample types ranging from plasma and urine to cells. To facilitate its application, we developed a user-friendly stand-alone web server for MGAT-CCS that is freely available at https://mgat-ccs-web.onrender.com. This work represents a step forward in predicting CCS values and can potentially facilitate the identification of small molecules when using ion mobility spectrometry coupled with mass spectrometry.

AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification.

Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS1 and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS1 spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS1 and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.

Developing high through put screening strategies to identify the next generation of antimicrobials

Area of expertise: My research interests focus on identifying novel compounds that can disrupt key phenotypes associated with antibiotic resistance such as biofilm formation and motility in the pathogens Acinetobacter baumannii and Pseudomonas aeruginosa. To this end we have developed a range of screens and assays to facilitate compound identification and mechanism of action profiling in these pathogens. These include: Antibiotic Resistance Inhibitor Screens, Biofilm Inhibition Screens, Biofilm Disruption Screens, Signalling Pathway Specific Biosensor Screens , Mechanism of Action Assays, Resistance Development Screens, Antibiotic Resistance Inhibitor Screens, In vivo biofilm and pathogenicity models to validate lead compounds and perform toxicity profiling. Future goals: Future goals include being part of a collaborative multidisciplinary drug discovery network that has access to diverse and bespoke extract/compound collections and the expertise to identify the bioactive leads, characterise their mechanism of action and ultimately take them forward to clinical trials. Further to this the goal is to target national and international funding opportunities that will facilitate taking active compounds right through the drug discovery pipeline from the bench to the bedside. I am hoping to identify collaborators across all aspects of drugs discovery, particularly collaborators with: Access to compound or extract collections, Expertise in compound identification ,Clinical trial expertise.

High resolution mass spectrometry-based non-target screening can support regulatory environmental monitoring and chemicals management

Non-target screening (NTS) including suspect screening with high resolution mass spectrometry has already shown its feasibility in detecting and identifying emerging contaminants, which subsequently triggered exposure mitigating measures. NTS has a large potential for tasks such as effective evaluation of regulations for safe marketing of substances and products, prioritization of substances for monitoring programmes and assessment of environmental quality. To achieve this, a further development of NTS methodology is required, including: (i) harmonized protocols and quality requirements, (ii) infrastructures for efficient data management, data evaluation and data sharing and (iii) sufficient resources and appropriately trained personnel in the research and regulatory communities in Europe. Recommendations for achieving these three requirements are outlined in the following discussion paper. In particular, in order to facilitate compound identification it is recommended that the relevant information for interpretation of mass spectra, as well as about the compounds usage and production tonnages, should be made accessible to the scientific community (via open-access databases). For many purposes, NTS should be implemented in combination with effect-based methods to focus on toxic chemicals.

Discovering Chemicals of Emerging Arctic Concern: Application of New Analytical Approaches to Human Biomonitoring

Different analytical strategies are used to discover chemicals of emerging concern in the Arctic. While traditional targeted analyses allow for the identification and quantification of chemicals with a priori knowledge of their presence, semi-targeted and untargeted analyses also permit samples obtained in the framework of human biomonitoring studies to be screened for the presence of unknown or unsuspected pollutants. We recently applied these different analytical strategies to human biomonitoring studies conducted in various regions of the Arctic. Chemical families targeted in plasma samples include polychlorinated biphenyls and chlorinated pesticides, polychlorinated dibenzo-p-dioxins and dibenzofurans as well as perfluorinated compounds. Targeted interrogation of the non polar purified extracts revealed the presence of chlorobenzenes, polycyclic aromatic hydrocarbons, polychlorinated naphthalenes, polychlorinated terphenylenes, short-chain chlorinated paraffins and natural halogenated compounds. Through untargeted analyses of extracts, thousands of entities are detected. Chemometric methods such as Kendrick mass defect plot and isotopic pattern detection can be used to attribute unknowns to the proper chemical family and facilitate compound identification. These innovative strategies will help identifying chemicals of emerging Arctic concern that should be included in future biomonitoring studies and considered for inclusion under the Stockholm Convention.

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

The mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can yield erroneous results if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flight MS. In this method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases for highly saturated species and dynamic range increased by 1–2 orders of magnitude for peptides in a blood serum sample.

Prediction Models of Retention Indices for Increased Confidence in Structural Elucidation during Complex Matrix Analysis: Application to Gas Chromatography Coupled with High-Resolution Mass Spectrometry.

Monitoring of volatile and semivolatile compounds was performed using gas chromatography (GC) coupled to high-resolution electron ionization mass spectrometry, using both headspace and liquid injection modes. A total of 560 reference compounds, including 8 odd n-alkanes, were analyzed and experimental linear retention indices (LRI) were determined. These reference compounds were randomly split into training (n = 401) and test (n = 151) sets. LRI for all 552 reference compounds were also calculated based upon computational Quantitative Structure-Property Relationship (QSPR) models, using two independent approaches RapidMiner (coupled to Dragon) and ACD/ChromGenius software. Correlation coefficients for experimental versus predicted LRI values calculated for both training and test set compounds were calculated at 0.966 and 0.949 for RapidMiner and at 0.977 and 0.976 for ACD/ChromGenius, respectively. In addition, the cross-validation correlation was calculated at 0.96 from RapidMiner and the residual standard error value obtained from ACD/ChromGenius was 53.635. These models were then used to predict LRI values for several thousand compounds reported present in tobacco and tobacco-related fractions, plus a range of specific flavor compounds. It was demonstrated that using the mean of the LRI values predicted by RapidMiner and ACD/ChromGenius, in combination with accurate mass data, could enhance the confidence level for compound identification from the analysis of complex matrixes, particularly when the two predicted LRI values for a compound were in close agreement. Application of this LRI modeling approach to matrixes with unknown composition has already enabled the confirmation of 23 postulated compounds, demonstrating its ability to facilitate compound identification in an analytical workflow. The goal is to reduce the list of putative candidates to a reasonable relevant number that can be obtained and measured for confirmation.

Using mass defect plots as a discovery tool to identify novel fluoropolymer thermal decomposition products

Fire events involving halogenated materials, such as plastics and electronics, produce complex mixtures that include unidentified toxic and environmentally persistent contaminants. Ultrahigh-resolution mass spectrometry and mass defect filtering can facilitate compound identification within these complex mixtures. In this study, thermal decomposition products of polychlorotrifluoroethylene (PCTFE, [-CClF-CF2 -]n), a common commercial polymer, were analyzed by Fourier transform ion cyclotron resonance mass spectrometry. Using the mass defect plot as a guide, novel PCTFE thermal decomposition products were identified, including 29 perhalogenated carboxylic acid (PXCA, X = Cl,F) congener classes and 21 chlorine/fluorine substituted polycyclic aromatic hydrocarbon (X-PAH, X = Cl,F) congener classes. This study showcases the complexity of fluoropolymer thermal decomposition and the potential of mass defect filtering to characterize complex environmental samples.

UPLC-MS-ELSD-PDA as a powerful dereplication tool to facilitate compound identification from small-molecule natural product libraries.

The generation of natural product libraries containing column fractions, each with only a few small molecules, using a high-throughput, automated fractionation system, has made it possible to implement an improved dereplication strategy for selection and prioritization of leads in a natural product discovery program. Analysis of databased UPLC-MS-ELSD-PDA information of three leads from a biological screen employing the ependymoma cell line EphB2-EPD generated details on the possible structures of active compounds present. The procedure allows the rapid identification of known compounds and guides the isolation of unknown compounds of interest. Three previously known flavanone-type compounds, homoeriodictyol (1), hesperetin (2), and sterubin (3), were identified in a selected fraction derived from the leaves of Eriodictyon angustifolium. The lignan compound deoxypodophyllotoxin (8) was confirmed to be an active constituent in two lead fractions derived from the bark and leaves of Thuja occidentalis. In addition, two new but inactive labdane-type diterpenoids with an uncommon triol side chain were also identified as coexisting with deoxypodophyllotoxin in a lead fraction from the bark of T. occidentalis. Both diterpenoids were isolated in acetylated form, and their structures were determined as 14S,15-diacetoxy-13R-hydroxylabd-8(17)-en-19-oic acid (9) and 14R,15-diacetoxy-13S-hydroxylabd-8(17)-en-19-oic acid (10), respectively, by spectroscopic data interpretation and X-ray crystallography. This work demonstrates that a UPLC-MS-ELSD-PDA database produced during fractionation may be used as a powerful dereplication tool to facilitate compound identification from chromatographically tractable small-molecule natural product libraries.

DrugBank 4.0: shedding new light on drug metabolism

DrugBank (http://www.drugbank.ca) is a comprehensive online database containing extensive biochemical and pharmacological information about drugs, their mechanisms and their targets. Since it was first described in 2006, DrugBank has rapidly evolved, both in response to user requests and in response to changing trends in drug research and development. Previous versions of DrugBank have been widely used to facilitate drug and in silico drug target discovery. The latest update, DrugBank 4.0, has been further expanded to contain data on drug metabolism, absorption, distribution, metabolism, excretion and toxicity (ADMET) and other kinds of quantitative structure activity relationships (QSAR) information. These enhancements are intended to facilitate research in xenobiotic metabolism (both prediction and characterization), pharmacokinetics, pharmacodynamics and drug design/discovery. For this release, >1200 drug metabolites (including their structures, names, activity, abundance and other detailed data) have been added along with >1300 drug metabolism reactions (including metabolizing enzymes and reaction types) and dozens of drug metabolism pathways. Another 30 predicted or measured ADMET parameters have been added to each DrugCard, bringing the average number of quantitative ADMET values for Food and Drug Administration-approved drugs close to 40. Referential nuclear magnetic resonance and MS spectra have been added for almost 400 drugs as well as spectral and mass matching tools to facilitate compound identification. This expanded collection of drug information is complemented by a number of new or improved search tools, including one that provides a simple analyses of drug–target, –enzyme and –transporter associations to provide insight on drug–drug interactions.

A view from above: cloud plots to visualize global metabolomic data.

Global metabolomics describes the comprehensive analysis of small molecules in a biological system without bias. With mass spectrometry-based methods, global metabolomic data sets typically comprise thousands of peaks, each of which is associated with a mass-to-charge ratio, retention time, fold change, p-value, and relative intensity. Although several visualization schemes have been used for metabolomic data, most commonly used representations exclude important data dimensions and therefore limit interpretation of global data sets. Given that metabolite identification through tandem mass spectrometry data acquisition is a time-limiting step of the untargeted metabolomic workflow, simultaneous visualization of these parameters from large sets of data could facilitate compound identification and data interpretation. Here, we present such a visualization scheme of global metabolomic data using a so-called "cloud plot" to represent multidimensional data from septic mice. While much attention has been dedicated to lipid compounds as potential biomarkers for sepsis, the cloud plot shows that alterations in hydrophilic metabolites may provide an early signature of the disease prior to the onset of clinical symptoms. The cloud plot is an effective representation of global mass spectrometry-based metabolomic data, and we describe how to extract it as standard output from our XCMS metabolomic software.

MeRy-B: a web knowledgebase for the storage, visualization, analysis and annotation of plant NMR metabolomic profiles.

BackgroundImprovements in the techniques for metabolomics analyses and growing interest in metabolomic approaches are resulting in the generation of increasing numbers of metabolomic profiles. Platforms are required for profile management, as a function of experimental design, and for metabolite identification, to facilitate the mining of the corresponding data. Various databases have been created, including organism-specific knowledgebases and analytical technique-specific spectral databases. However, there is currently no platform meeting the requirements for both profile management and metabolite identification for nuclear magnetic resonance (NMR) experiments.DescriptionMeRy-B, the first platform for plant 1H-NMR metabolomic profiles, is designed (i) to provide a knowledgebase of curated plant profiles and metabolites obtained by NMR, together with the corresponding experimental and analytical metadata, (ii) for queries and visualization of the data, (iii) to discriminate between profiles with spectrum visualization tools and statistical analysis, (iv) to facilitate compound identification. It contains lists of plant metabolites and unknown compounds, with information about experimental conditions, the factors studied and metabolite concentrations for several plant species, compiled from more than one thousand annotated NMR profiles for various organs or tissues.ConclusionMeRy-B manages all the data generated by NMR-based plant metabolomics experiments, from description of the biological source to identification of the metabolites and determinations of their concentrations. It is the first database allowing the display and overlay of NMR metabolomic profiles selected through queries on data or metadata. MeRy-B is available from http://www.cbib.u-bordeaux2.fr/MERYB/index.php.

MetaboAnalyst: a web server for metabolomic data analysis and interpretation

Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.ca