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      Supporting non-target identification by adding hydrogen deuterium exchange MS/MS capabilities to MetFrag

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          Abstract

          Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is increasingly popular for the non-targeted exploration of complex samples, where tandem mass spectrometry (MS/MS) is used to characterize the structure of unknown compounds. However, mass spectra do not always contain sufficient information to unequivocally identify the correct structure. This study investigated how much additional information can be gained using hydrogen deuterium exchange (HDX) experiments. The exchange of “easily exchangeable” hydrogen atoms (connected to heteroatoms), with predominantly [M+D] + ions in positive mode and [M-D] in negative mode was observed. To enable high-throughput processing, new scoring terms were incorporated into the in silico fragmenter MetFrag. These were initially developed on small datasets and then tested on 762 compounds of environmental interest. Pairs of spectra (normal and deuterated) were found for 593 of these substances (506 positive mode, 155 negative mode spectra). The new scoring terms resulted in 29 additional correct identifications (78 vs 49) for positive mode and an increase in top 10 rankings from 80 to 106 in negative mode. Compounds with dual functionality (polar head group, long apolar tail) exhibited dramatic retention time (RT) shifts of up to several minutes, compared with an average 0.04 min RT shift. For a smaller dataset of 80 metabolites, top 10 rankings improved from 13 to 24 (positive mode, 57 spectra) and from 14 to 31 (negative mode, 63 spectra) when including HDX information. The results of standard measurements were confirmed using targets and tentatively identified surfactant species in an environmental sample collected from the river Danube near Novi Sad (Serbia). The changes to MetFrag have been integrated into the command line version available at http://c-ruttkies.github.io/MetFrag and all resulting spectra and compounds are available in online resources and in the Electronic Supplementary Material (ESM).

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          The online version of this article (10.1007/s00216-019-01885-0) contains supplementary material, which is available to authorized users.

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          The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo-and Bioinformatics

          The Chemistry Development Kit (CDK) is a freely available open-source Java library for Structural Chemo-and Bioinformatics. Its architecture and capabilities as well as the development as an open-source project by a team of international collaborators from academic and industrial institutions is described. The CDK provides methods for many common tasks in molecular informatics, including 2D and 3D rendering of chemical structures, I/O routines, SMILES parsing and generation, ring searches, isomorphism checking, structure diagram generation, etc. Application scenarios as well as access information for interested users and potential contributors are given.
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            MetFrag relaunched: incorporating strategies beyond in silico fragmentation

            Background The in silico fragmenter MetFrag, launched in 2010, was one of the first approaches combining compound database searching and fragmentation prediction for small molecule identification from tandem mass spectrometry data. Since then many new approaches have evolved, as has MetFrag itself. This article details the latest developments to MetFrag and its use in small molecule identification since the original publication. Results MetFrag has gone through algorithmic and scoring refinements. New features include the retrieval of reference, data source and patent information via ChemSpider and PubChem web services, as well as InChIKey filtering to reduce candidate redundancy due to stereoisomerism. Candidates can be filtered or scored differently based on criteria like occurence of certain elements and/or substructures prior to fragmentation, or presence in so-called “suspect lists”. Retention time information can now be calculated either within MetFrag with a sufficient amount of user-provided retention times, or incorporated separately as “user-defined scores” to be included in candidate ranking. The changes to MetFrag were evaluated on the original dataset as well as a dataset of 473 merged high resolution tandem mass spectra (HR-MS/MS) and compared with another open source in silico fragmenter, CFM-ID. Using HR-MS/MS information only, MetFrag2.2 and CFM-ID had 30 and 43 Top 1 ranks, respectively, using PubChem as a database. Including reference and retention information in MetFrag2.2 improved this to 420 and 336 Top 1 ranks with ChemSpider and PubChem (89 and 71 %), respectively, and even up to 343 Top 1 ranks (PubChem) when combining with CFM-ID. The optimal parameters and weights were verified using three additional datasets of 824 merged HR-MS/MS spectra in total. Further examples are given to demonstrate flexibility of the enhanced features. Conclusions In many cases additional information is available from the experimental context to add to small molecule identification, which is especially useful where the mass spectrum alone is not sufficient for candidate selection from a large number of candidates. The results achieved with MetFrag2.2 clearly show the benefit of considering this additional information. The new functions greatly enhance the chance of identification success and have been incorporated into a command line interface in a flexible way designed to be integrated into high throughput workflows. Feedback on the command line version of MetFrag2.2 available at http://c-ruttkies.github.io/MetFrag/ is welcome. Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0115-9) contains supplementary material, which is available to authorized users.
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              In silico fragmentation for computer assisted identification of metabolite mass spectra

              Background Mass spectrometry has become the analytical method of choice in metabolomics research. The identification of unknown compounds is the main bottleneck. In addition to the precursor mass, tandem MS spectra carry informative fragment peaks, but the coverage of spectral libraries of measured reference compounds are far from covering the complete chemical space. Compound libraries such as PubChem or KEGG describe a larger number of compounds, which can be used to compare their in silico fragmentation with spectra of unknown metabolites. Results We created the MetFrag suite to obtain a candidate list from compound libraries based on the precursor mass, subsequently ranked by the agreement between measured and in silico fragments. In the evaluation MetFrag was able to rank most of the correct compounds within the top 3 candidates returned by an exact mass query in KEGG. Compared to a previously published study, MetFrag obtained better results than the commercial MassFrontier software. Especially for large compound libraries, the candidates with a good score show a high structural similarity or just different stereochemistry, a subsequent clustering based on chemical distances reduces this redundancy. The in silico fragmentation requires less than a second to process a molecule, and MetFrag performs a search in KEGG or PubChem on average within 30 to 300 seconds, respectively, on an average desktop PC. Conclusions We presented a method that is able to identify small molecules from tandem MS measurements, even without spectral reference data or a large set of fragmentation rules. With today's massive general purpose compound libraries we obtain dozens of very similar candidates, which still allows a confident estimate of the correct compound class. Our tool MetFrag improves the identification of unknown substances from tandem MS spectra and delivers better results than comparable commercial software. MetFrag is available through a web application, web services and as java library. The web frontend allows the end-user to analyse single spectra and browse the results, whereas the web service and console application are aimed to perform batch searches and evaluation.
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                Author and article information

                Contributors
                emma.schymanski@uni.lu
                martin.krauss@ufz.de
                Journal
                Anal Bioanal Chem
                Anal Bioanal Chem
                Analytical and Bioanalytical Chemistry
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1618-2642
                1618-2650
                17 June 2019
                17 June 2019
                2019
                : 411
                : 19
                : 4683-4700
                Affiliations
                [1 ]ISNI 0000 0004 0493 728X, GRID grid.425084.f, Department of Stress and Developmental Biology, , Leibniz Institute of Plant Biochemistry, ; Weinberg 3, 06120 Halle, Germany
                [2 ]ISNI 0000 0001 2295 9843, GRID grid.16008.3f, Luxembourg Centre for Systems Biomedicine (LCSB), , University of Luxembourg, ; 6 avenue du Swing, 4367 Belvaux, Luxembourg
                [3 ]ISNI 0000 0001 1551 0562, GRID grid.418656.8, Eawag: Swiss Federal Institute of Aquatic Science and Technology, ; Überlandstrasse 133, 8600 Dübendorf, Switzerland
                [4 ]Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092 Zürich, Switzerland
                [5 ]iDiv - German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany
                [6 ]ISNI 0000 0001 2146 2763, GRID grid.418698.a, National Centre for Computational Toxicity (NCCT), , United States Environmental Protection Agency, ; Research Triangle Park, NC 27711 USA
                [7 ]ISNI 0000 0004 0492 3830, GRID grid.7492.8, Helmholtz Centre for Environmental Research – UFZ, ; Permoserstr. 15, 04318 Leipzig, Germany
                Author notes

                Published in the topical collection Young Investigators in (Bio-)Analytical Chemistry with guest editors Erin Baker, Kerstin Leopold, Francesco Ricci, and Wei Wang.

                Author information
                http://orcid.org/0000-0001-6868-8145
                Article
                1885
                10.1007/s00216-019-01885-0
                6611743
                31209548
                2c62de32-3e02-4561-9d7a-1c132aacf5cc
                © The Author(s) 2019

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 25 January 2019
                : 8 April 2019
                : 30 April 2019
                Funding
                Funded by: Luxembourg National Research Fund (FNR)
                Award ID: 12341006
                Funded by: FundRef http://dx.doi.org/10.13039/501100001656, Helmholtz-Gemeinschaft;
                Award ID: CITEPro
                Funded by: FundRef http://dx.doi.org/10.13039/501100001664, Leibniz-Gemeinschaft;
                Award ID: Institutional funding
                Funded by: FundRef http://dx.doi.org/10.13039/100011102, Seventh Framework Programme;
                Award ID: 603437
                Funded by: FundRef http://dx.doi.org/10.13039/100010661, Horizon 2020 Framework Programme;
                Award ID: EC654241
                Categories
                Paper in Forefront
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2019

                Analytical chemistry
                compound identification,in silico fragmentation,hydrogen deuterium exchange,high-resolution mass spectrometry,structure elucidation,metabolomics

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