Confidence in chemical identifications is a function of the experimental information available [2]. For each comparison group (n = 6), the estimated median value was significantly greater than 1 (Wilcoxon signed-rank test; p < 0.0001 in all cases), reflecting higher CFM-ID scores when CEexperimental = CEin silico. and run it locally. 2014;30(12):157–64. For optimal results, your experimental data should match the For example, in a hypothetical study, MS2 spectra could be matched to both the reference and in silico libraries. EPA’s DSSTox database: history of development of a curated chemistry resource supporting computational toxicology research. Even though we provide specialized Using prepared mixtures of ToxCast chemicals to evaluate non-targeted analysis (NTA) method performance. In a previous analysis of the ENTACT mixtures, initial substance identification was performed without the use of individual reference standards. 2005;14(8):1847–50. Typical Tandem MS in space instruments include QqQ, QTOF, and hybrid ion trap/FTMS, etc. S2 (see ESM). The formation of molecular ions. Specifically, the lowest FPR is expected for a given TPR when using a quotient-based cutoff and formula filtering. Thus, FPRs are generally expected to be lower, on average, using quotient cutoffs, but more consistent using percentile cutoffs. For a more user friendly experience, we created this web server. Number of “pass” compounds within the top 20 CFM-ID hits using approach 3 vs. approach 1 at CE = 10, 20, or 40 V (b). Reference MS2 spectra were contained in Agilent Personal Compound Database and Library (PCDL) format. Blazenovic I, Kind T, Ji J, Fiehn O. The set of “pass” substances, spanning all ten mixtures, was the basis for all analyses in the current study. Find out more. Once samples are ionized (by ESI, MALDI, EI, etc.) Hufsky F, Scheubert K, Bocker S. Computational mass spectrometry for small-molecule fragmentation. Djoumbou-Feunang Y, Pon A, Karu N, Zheng JM, Li C, Arndt D, et al. Approach 3 tended to yield the best overall results and was therefore the basis for performance evaluations regarding TPR and FPR. J Cheminformatics. Typical Tandem-in-Time MS/MS instruments include ion trap and FT-ICR MS. Peptides and oligosaccharides (including glycolipids) follow different systems of nomenclature for their fragment ions. Using the global curves, the percentile value and quotient value that would result in a minimum TPR of 0.90 were determined. You can install MS²PIP on your machine by following our extended install instructions found on the Six Agilent PCDLs were used in this analysis as the source of reference MS2 data for matching; the composite of these PCDLs included 11,324 unique compounds with reference MS2 spectra. On the download page we also provide an interactive CAS (850) 644-0311 | This email address is being protected from spambots. Kind T, Tsugawa H, Cajka T, Ma Y, Lai ZJ, Mehta SS, et al. 4a). addyc2b20d6fa05204a614aca6cdea379ece = addyc2b20d6fa05204a614aca6cdea379ece + 'magnet' + '.' + 'fsu' + '.' + 'edu'; The confidence in eventual chemical identifications depends, in part, on the experimental HRMS data available for analysis. If you Our previously reported results for the ENTACT mixtures were based on matching feature data to mass lists, formula lists, and reference MS2 libraries (highlighted in blue) [23]. It is noteworthy that certain substances were included in multiple mixtures as part of the ENTACT design to help evaluate method reproducibility [21, 23]. The purpose of the current article is to describe the incorporation of CFM-ID predicted spectra into the existing EPA workflow, and to evaluate overall method performance using the ENTACT mixture data. Degroeve, S., & Martens, L. (2013). The resulting database of CFM-ID predicted spectra is hereafter referred to as the “CFM-ID database” [27]. Other candidate compounds which are above the cutoff value are considered potential FPs, and those below the cutoff value are considered true negatives (TN). Thus, results of our formula-based analysis represent a “best case scenario” and yield the smallest expected FPRs. Correspondence to It further lists the confidence levels associated with each type of match (right). Amide. MS2PIP is a tool to predict MS2 signal peak intensities from peptide sequences. Processing of MGF files was performed to improve data formatting and to de-duplicate MS2 spectra. Metabolites. In silico libraries can be generated at a much more rapid pace, on both known and predicted structures (e.g., those of expected metabolites and transformation products) within a given database. To achieve these goals, MS2 spectra for “pass” compounds were scored against their respective CFM-ID spectra at all three CE levels. For an NTA workflow where the compounds are unknown, the recommended practice is to acquire experimental MS2 data at all three CE levels in order to capture suitable spectra on the widest range of compounds. Each combination of experimental spectrum vs. CFM-ID predicted spectrum generates a unique score via the dot-product algorithm, designated by a unique letter assignment. In the following table, we list all MS² acquisition information and peptide properties for The spectrum with the highest signal was considered most representative of the chemical feature for spectral matching purposes. var prefix = 'ma' + 'il' + 'to'; Examples of cutoff filtering of CFM-ID results are shown in Fig. The generated product ions are detected by time-of-flight (TOF) mass spectrometry. These lists of expected masses were then searched (with a 10-ppm accuracy window) against MS2 precursor ion lists to identify “pass” substances for which MS2 data were acquired. document.getElementById('cloakc2b20d6fa05204a614aca6cdea379ece').innerHTML = ''; NTA methods can benefit from these reference data to the extent that they have been previously acquired and stored in a usable format. Once MS2 spectra were processed, the Python script searched the CFM-ID database for all candidate compounds (as identified by MS-Ready DTXCID) within a 10-ppm mass window of each MS2 spectrum precursor mass, considering only [M+H]+ and [M-H]- ion species for positive and negative modes, respectively. In approach 3, scores are summed across all three CEin silico levels, and then across all three CEexperimental levels. All matches were manually reviewed to increase confidence in compound identifications. Experimental MS2 data for ENTACT mixture compounds were collected and CFM-ID spectra predicted at three CE levels (10, 20, and 40 V). Present address: Agilent Technologies Inc., Santa Clara, CA, 95051, USA, Present address: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365, Esch-sur-Alzette, Luxembourg, Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. 2017;9. Figure 5 shows a comparison of de-duplicated “pass” compounds (n = 377) that were correctly identified by PCDL reference library matching (n = 199) vs. CFM-ID database matching (with formula filtering, n = 188). .peprec, .csv, .tsv and .txt. 2013;3(3):517–38. Electron capture dissociation and electron transfer dissociation mainly produce c and z ions while preserving post-translational modifications (PTMs). Initial results (vide infra), however, are provided without de-duplication to preserve statistics specific to each individual ENTACT mixture. J Cheminform. Considering only the highest matching compounds will limit the number of FPs, but at a greater risk of missing a TP. Identifying small molecules via high resolution mass spectrometry: communicating confidence. used to calculate m/z values. Linking in silico MS/MS spectra with chemistry data to improve identification of unknowns. Accurate mass and isotope pattern data may enable chemical characterization at the molecular formula level, whereas tandem fragmentation data (i.e., MS/MS or MS2 spectra) may enable characterization at the structure level [2]. This workflow outlines the main components of data acquisition and processing (left), as well as database generation and matching (center). PubChem 2019 update: improved access to chemical data. Compound information from each of the six PCDLs was exported using Agilent PCDL Manager software. Resulting predicted spectra were then linked with MS-Ready structure metadata such as DTXCID, molecular formula, and monoisotopic mass. EPA’s DSSTox database is freely available to the public via the Dashboard (https://comptox.epa.gov/dashboard) [24]. S6). proteins will then be written in the Comment field of the MSP file. Future investigations will aim to incorporate these various data streams into a unified workflow, and to optimize filtering criteria for maximum TPRs and minimum FPRs. Alex Chao or Jon R. Sobus. Other classes of compounds, i.e. As expected, results were markedly better, regardless of the scoring approach, when implementing formula filtering as part of candidate ranking (Table 3). to discover fragmentation rules and eventual predictive models for MS2 spectra. The fragmentation in the C-ring may occur in all subclasses of flavonoids, and the RDA mechanism may follow pathways a, b and/or c , according to the structural subclass. Hufsky F, Bocker S. Mining molecular structure databases: identification of small molecules based on fragmentation mass spectrometry data. For more information please contact Amy McKenna, Manager, ICR User Program. Article This list of compounds was filtered for those containing at least one MS2 spectrum, and then batch searched by CAS number on the Dashboard to retrieve a DTXSID for each compound in the PCDLs. Thus, for a given precursor mass, the spectrum with the highest sum intensity of ions was retained for analysis.