EI-MS, Structure

Article pubs.acs.org/ac

Consensus Structure Elucidation Combining GC/EI-MS, Structure Generation, and Calculated Properties Emma L. Schymanski,*,†,‡ Christine M. J. Gallampois,§ Martin Krauss,† Markus Meringer,∥ Steffen Neumann,⊥ Tobias Schulze,† Sebastian Wolf,⊥ and Werner Brack† †

UFZ - Helmholtz Centre for Environmental Research, Department of Effect-Directed Analysis, Permoserstrasse 15, D-04318 Leipzig, Germany § Department of Clinical and Experimental Medicine, Faculty of Health Science, Linköping University, SE-581 83 Linköping, Sweden ∥ DLR - German Aerospace Centre, Remote Sensing Technology Institute, Münchner Strasse 20, D-82234 Oberpfaffenhofen-Wessling, Germany ⊥ IPB - Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, D-06120 Halle (Saale), Germany S Supporting Information *

ABSTRACT: This article explores consensus structure elucidation on the basis of GC/EI-MS, structure generation, and calculated properties for unknown compounds. Candidate structures were generated using the molecular formula and substructure information obtained from GC/EI-MS spectra. Calculated properties were then used to score candidates according to a consensus approach, rather than filtering or exclusion. Two mass spectral match calculations (MOLGEN-MS and MetFrag), retention behavior (Lee retention index/boiling point correlation, NIST Kovat’s retention index), octanol−water partitioning behavior (log Kow), and finally steric energy calculations were used to select candidates. A simple consensus scoring function was developed and tested on two unknown spectra detected in a mutagenic subfraction of a water sample from the Elbe River using GC/EI-MS. The top candidates proposed using the consensus scoring technique were purchased and confirmed analytically using GC/EI-MS and LC/MS/MS. Although the compounds identified were not responsible for the sample mutagenicity, the structure-generation-based identification for GC/EI-MS using calculated properties and consensus scoring was demonstrated to be applicable to real-world unknowns and suggests that the development of a similar strategy for multidimensional highresolution MS could improve the outcomes of environmental and metabolomics studies.

T

consistent fragmentation patterns. However, mass spectral databases contain only a relatively small fraction of molecules known to exist (around 670 000 unique spectra combined in the latest versions of the NIST5 and Wiley6 databases). This is only a small portion of the estimated 8 600 000 compounds available commercially7 and over 25 million compound entries in the online databases PubChem8 and ChemSpider,9 as well as many other compounds potentially present in the environment, including natural products, byproducts, metabolites, and transformation products.10 As a result, toxicant identification and confirmation in EDA cannot rely solely on the use of databases, but often requires a tiered approach, where information from each tier contributes to lines of evidence rather than a distinct yes/no decision.1 The challenge in EDA, therefore, is to deduce as much information as possible from the separation, bioassay, and analysis to assist in the identification of compounds.

he identification of unknown compounds in complex environmental samples is a challenging and timeconsuming task, requiring prioritization of efforts and streamlined data processing. Effect-directed analysis (EDA) can be used to reduce the number of “relevant” unknowns within a complex sample, by isolating those compounds that are (potentially) toxicologically relevant rather than attempting to identify all compounds present in complex environmental mixtures.1 EDA combines biological tests (biotests) and physicochemical fractionation procedures to reduce sample complexity and identify the fractions showing toxic effects for further investigation. This process is repeated until (ideally) only a few compounds are present that might contribute to the fraction toxicity in chemical analysis results.2 Despite fractionation efforts, the sample amount and purity often restrict the choice of chemical analysis to coupled chromatographic systems with mass spectrometric detection providing information for identification of components.3 Gas chromatography coupled with electron-ionization mass spectrometry (GC/EI-MS) is a common starting point for EDA for a number of reasons.4 The mass spectra are well-suited to identification efforts, being relatively reproducible with © 2012 American Chemical Society

Received: December 27, 2011 Accepted: February 24, 2012 Published: February 24, 2012 3287

dx.doi.org/10.1021/ac203471y | Anal. Chem. 2012, 84, 3287−3295

Analytical Chemistry

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MATERIALS AND METHODS Analytical Method. GC/EI-MS analysis was conducted on selected fractions obtained using semipreparative liquid chromatography during an EDA of a river water sample collected using a blue rayon passive sampler. The Ames fluctuation test24b was used to assess the mutagenicity of the sample and the resulting fractions. Further details on the EDA method are given elsewhere.25 Details on the GC/MS and LC/MS/MS methods are provided in the Supporting Information. Retention indices (KRI and LRI) were calculated according to the standard equations.12 GC/EI-MS Processing. AMDIS26 was used to deconvolute the GC/MS spectra and identify peaks in the sample by comparing the fractionated sample with the respective fractionated blank extract of blue rayon. Deconvolution settings were medium. MSP files27 for the selected deconvoluted spectra were saved from AMDIS and searched using the NIST library search. The NIST substructure information was saved for automatic upload into MOLGEN-MS.28 MOLGEN-MS was used to perform spectral interpretation, including the NIST substructure information, and then generate the possible structures matching the mass spectral data.11,29 Molecular formulas were calculated based on composition estimates from the substructural information and consideration of the ring and double bond (RDB) count. Incorporation of MetFrag. The mass spectral prediction program MetFrag17 was extended to predict fragments for GC/ EI-MS spectra. In addition to calculating fragments for [M + H]+ and [M − H]− ions, it is now possible to start from [M]+ and [M]− ions. In “M” mode, the peak-matching algorithm adds or removes the mass of one electron (0.00054858 Da) to the fragment to match the measured peak. MetFrag “M+” mode was evaluated for candidate selection using GC/EI-MS spectral matching, following the method used for MOLGEN-MS.15 Briefly, 100 spectra with up to 10 000 mathematically possible candidates were used to assess the ability of MetFrag to distinguish the correct structure from the incorrect structures. The MetFrag parameters were Mode 0 positive (settings for M+•) with mzabs = 0.5 and mzppm = 0 to cater to nominal masses. The tree depth was set to 2 (default) or 3. Three main values were used to assess the performance of MetFrag for GC/EI-MS. The MetFrag score (S) was calculated for each structure and scaled relative to other candidates as follows

Until recently, few options were available for the rapid identification of mass spectra outside the domain of mass spectral databases. Although it is surely difficult to surpass expert knowledge when interpreting mass spectra, such analysis is time-consuming, and experts are unfortunately not always available to identify the many unknown peaks still present in the chromatograms of many environmental samples. As an alternative, an automated strategy combining EI-MS interpretation, structure generation, and calculated compound properties was presented recently, 11 to assist in the identification of compounds without relying on a matching database spectrum. The calculated properties incorporate values typically available during EDA investigations, including the Lee retention index (LRI) and Kovat’s retention index (KRI) from GC/MS measurements,12 as well as the octanol−water partitioning coefficients (log Kow) of the fraction, which can be estimated during reverse-phase high-performance liquid chromatographic (RP-HPLC) separation of samples.13 The strategy was tested on 29 C12H10O2 isomers and reduced the structures remaining in consideration from over 1.5 × 109 structures based on the molecular formula alone to below 6 structures in 10 cases and less than 35 structures in all but 3 cases.11 This study used a filtering system, similar to that used by Hill et al.,14 to progressively eliminate candidates from consideration. The disadvantage of this approach is that one problem in any given calculation could lead to the exclusion of candidates that might otherwise be plausible. The energy criteria used was also a first-order calculation and was in need of more detailed investigation, as it was calculated on a randomly selected set of 1000 spectra that included both very small and very large molecules.15 Herein, we propose a consensus scoring system to overcome the difficulty with multiple selection criteria and instead use the weight of evidence, that is, the largest number of criteria satisfied, to identify our compounds of interest. This approach was used successfully, for example, by Bentzien et al.16 to improve the specificity and sensitivity of their classification by combining three mutagenicity predictions, compared with just one predictive technique alone. Thus, we also included more calculations than those outlined by Schymanksi et al.11 We extended the program MetFrag17 to cover spectral prediction for GC/EI-MS spectra and validated this prediction as an additional mass spectral match criterion. Through MetFrag, we included the Chemistry Development Kit (CDK) implementations of XlogP and AlogP18 to calculate additional log Kow values for candidate structures. The NIST KRI prediction, recently used by Kumari et al.,19 was added to the LRI/BP correlation to have two retention-based criteria. We also extended the energy calculations to include Open Babel Obenergy,20 MOPAC,21 and ChemAxon22 and furthermore used a selection of these programs to calculate more detailed energy distributions. We then explored the concept of molecule plausibility following structure generation, comparing the energies for all possible structures of a given molecular formula with the energies of those molecules with the same formula in PubChem.8 We then applied these ideas to identify real unknown spectra. The spectra were chosen from mutagenic fractions isolated during the EDA of river water collected from Pardubice (Czech Republic) using the passive sampler blue rayon23 and tested using the Ames II fluctuation test.24

Si =

1 1 wi − ei max(w) 2max(e)

where ei =

wi = 1 |Fi|

∑

I f 0.6mf 3

and

f ∈ Fi

∑ ∑ f ∈ Fi b ∈ Bf

BDEb (1)

The score Si of candidate i was calculated for all fragments Fi that explain peaks in the measured spectrum, based on the weighted peak count wi [scaled to be between 0 and 1 by max(w)] and the sum of fragment bond dissociation energies (BDEs) represented by ei [scaled to be between 0 and 0.5 by max(e)]. Further details are given in Wolf et al.17 Second, we used the mass spectral match value (MV, shown in eq 2) to compare the individual MetFrag predictions with 3288



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Figure 1. Flowchart of structure generation on the basis of a GC/EI-MS spectrum followed by candidate selection.

candidates with the same score, equals 0 if the correct structure has the best score (S, MV), and equals 1 if the correct structure has the worst score. Conformational Energy Criteria. We investigated the assumption that energy calculations will allow the less plausible molecules resulting from structure generation to be penalized. We included five programs for the derivation of steric energy criteria for candidate selection. These were ChemBio3D,32 MOLGEN-QSPR,33 Obenergy,20 ChemAxon,22a and MOPAC7 for Linux.21,34 Preliminary assessment of the programs was performed on structures taken from 1000 randomly selected NIST mass spectra11 and the original 698 molecules from the MMFF94 validation set35 (hypervalent representation), which provided data points with experimentally determined energy values. These calculations were used to form a first-order existence criterion through the calculation of percentiles11 and to determine which programs and settings were more suitable for the calculation of more detailed energy distributions. More details are provided in the Supporting Information. Further, we created a large data set to test the plausibility of molecules. MOLGEN was used to generate all possible molecules for 132 molecular formulas, taken from existing data sets11,15b,29 as the background generated data set. A list of the formulas is given in the Supporting Information (Table S-

the MVs calculated for MOLGEN-MS and two other programs, Mass Frontier30 and ACD MS Fragmenter,31 during a previous validation study.15b MV = 1 −

∑m [I(m) − x(m)I(m)]2 ∑m [I(m)]2

(2)

Here, m is the mass-to-charge (m/z) ratio of the fragment, I(m) is the intensity of the experimental mass spectral peak at m (scaled by the base peak intensity to lie between 0 and 1), and x(m) indicates the presence/absence of predicted fragments such that x(m) = 0 if there is no predicted fragment for m and x(m) = 1 if there is a predicted fragment. More details are given in Kerber et al.15a Finally, the relative ranking position (RRP) was used to determine the position of the “correct” candidate with respect to all other candidates, as follows RRP =

1 ⎛⎜ BC − WC ⎞⎟ 1+ 2⎝ TC − 1 ⎠

(3)

Here, BC represents the number of “better” candidates [i.e., those with a higher score (S or MV)], WC is the number of “worse” candidates (i.e., with a lower score), and TC is the total number of candidates. As such, the RRP is equal for all 3289



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Table 1. Average Match Values (MV) or MetFrag Scores (S) for All Structures and for the Correct Structures and Average RRPs (for the Correct Structure) for 100 and 27 Spectraa,b average MV MetFrag, TD2 (MV) MetFrag, TD2 (score) MetFrag, TD3 (MV) MetFrag, TD3 (score) MassFrontier, 3 step MassFrontier, 5 step MOLGEN-MS ACD, 3 step ACD, 5 step MassFrontier, 3 step, library MassFrontier, 5 step, library

correct MV

correct RRP

100 spectra

27 spectra

100 spectra

27 spectra

100 spectra

27 spectra

0.4011 0.3740 0.7391 0.5616 0.2725 0.3963 0.2463 − − − −

0.3917 0.3626 0.6632 0.4751 0.2769 0.3066 0.2328 0.7672 0.8084 0.4428 0.5305

0.4958 0.4617 0.7476 0.5764 0.4622 0.5581 0.4317 − − − −

0.4184 0.4215 0.6730 0.5118 0.3811 0.4325 0.3537 0.8131 0.8331 0.5111 0.6157

0.4127 0.3587 0.5074 0.4806 0.2685 0.3527 0.2734 − − − −

0.5084 0.4302 0.4528 0.4510 0.3754 0.3926 0.3519 0.5197 0.5349 0.3887 0.3815

a TD = tree depth (MetFrag); “3 step” and “5 step” indicate the numbers of fragmentation steps (MassFrontier, ACD). bResults for ACD, MassFrontier, and MOLGEN-MS taken from Schymanski et al.15b

into one value, as using them separately provided more sensitive results.

2). All chemicals in PubChem snapshot (November 2010) with these formulas were then retrieved as the foreground existing data set. The energy values were calculated for all molecules in both sets to compare the distribution of energies for existing versus generated molecules. Energy calculations were performed for a subset of those above (Obenergy with force fields Ghemical, MMFF94, and UFF and MOPAC with force field UFF); see the Supporting Information for more details. Candidate Scoring. The workflow from EI-MS spectrum to candidate selection on the basis of calculated properties is shown in Figure 1. To calculate the score, the MOLGEN-MS match value and MetFrag score for each candidate were used as is, as both values already ranged between 0 (poor match) and 1 (best match). Octanol−water partitioning coefficient (log Kow) data were calculated for each candidate using EPISuite36 and CDK XlogP and AlogP.18b,c The former is group-theory-based, whereas the latter are atomic-based. Candidates with a predicted log Kow value within the calculated fraction range ±1 were assigned a value of 1; those outside, a value of 0. Boiling-point (BP) data were calculated using EPISuite to enable use of the LRI/BP correlation.37 Structures within the predicted BP range, namely, (LRI − 31) to (LRI + 71) °C, were assigned a value of 1; those outside, a value of 0. The NIST KRI prediction, developed by Stein et al.38 and used by Kumari et al.19 for metabolite identification, provided an additional retention match criterion. We adopted a more conservative error margin than Kumari et al.,19 choosing the 95% confidence interval for diverse functional groups (382 units, as this was applicable to both unknowns here) and an experimental error of 3.4.11 Thus, candidates within the range of the experimental KRI ± 355.4 were assigned a score of 1; those outside, a score of 0. The energy values for different programs were assigned a value of 1 if the value was below the 90th percentile for that program (calculated on different data sets; see the Results section) and 0 if the value was above this cutoff. All of these values were combined to give a consensus score (CS), shown in eq 4. This score scales the partitioning, retention, and energy values according to the number of values available before summing these and dividing by 5 to give a score between 0 and 1. We did not combine the MV and score

⎡ ∑ log K ow ∑ RI ∑E ⎤ + + CS = ⎢MV + S + ⎥ /5 n(log K ow ) n(RI) n(E) ⎦ ⎣ (4)

In eq 4, MV is the MOLGEN-MS match value, and S is the MetFrag score. ∑log Kow is the sum of the partitioning criteria results and is divided by the number of log Kow criteria used. Similarly, RI refers to the retention index criteria results, and E to the steric energy results, also divided by the corresponding numbers of criteria.

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RESULTS Evaluation of MetFrag for GC/MS. MetFrag was expanded to apply to GC/MS fragmentation and evaluated using the same 100 spectra as used in previous studies.15 The calculations were run locally in batch mode. The average MetFrag score (S) and match value (MV) for all molecules and the correct molecule compared with other programs and settings15b are reported in Table 1, along with the RRPs for the correct molecule. Data are presented for all 100 spectra (with up to 10 000 isomers) and for 27 spectra (with fewer than 200 isomers), to compare with the results from ACD and Mass Frontier with library fragmentation. Conformational Energy. Full details of the energy calculations are given in the Supporting Information. Calculations on the 1698-molecule data set for ChemBio3D and MOLGEN-QSPR yielded 90th-percentile values of 130.9 and 388.5 kcal/mol, respectively. The remaining values are listed in Table S-3 (Supporting Information). We selected Obenergy and MOPAC to perform more detailed calculations, as they are freely available, cover several different force fields, and had the lowest error rates. Calculations with the 22381-molecule PubChem data set yielded 90th-percentile values of 24.11, 207.74, 52.08 and 307.74 kcal/mol for the MOPAC UFF force field and Obenergy force fields Ghemical, MMFF94 and UFF, respectively. (Details on the force fields are given in the Supporting Information.) These energies are lower than the equivalent values from the MOLGEN data set: 138.45, 414.23, 110.64, and 566.85 kcal/mol, in the same order. The large differences in energy values between the MOLGEN (gen3290



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identified as the top candidate, showing the strength of consensus scoring: Whereas 71 other candidates have higher MVs and 12 other candidates have higher MetFrag scores, structure 56 is the only one to satisfy the energy criteria for all programs, as well as the retention and partitioning behavior. This is not particularly surprising, considering the other top candidates in Chart 2. On the basis of this result, structure 56 (phthalic anhydride) was considered most likely to be the unknown. Confirmation of Unknown 1 (19.2 min). LC/MS/MS analysis [atmospheric-pressure chemical ionization (APCI) positive] of the mutagenic subfraction revealed a small peak of m/z [M + H]+ = 149.0229 at retention time 3.04 min, corresponding with the formula of the candidates in Chart 2 (C8H4O3, calculated [M + H]+ = 149.0233, error 2.7 ppm, monoisotopic mass =148.0160), which was not detected in any blanks. The corresponding analytical standard of the tentatively identified phthalic anhydride was purchased (Sigma Aldrich) and measured using the same GC/MS method. Phthalic anhydride was detected at 18.9843 min. The NIST match value5 between unknown and standard was 921, with a reverse match value also equal to 921 (of maximum 1000), indicating a very good match. The KRI and LRI were calculated as 1320.2 and 224.0, respectively, compared with the unknown's values of 1320 and 224.7. Confirmation LC/MS/MS analysis of phthalic anhydride revealed a peak at 3.06 min of mass 149.0226 (4.7 ppm), confirming the presence of phthalic anhydride in the sample using two analytical techniques. Tentative Identification of Unknown 2. The database search results for unknown 2 (25.4 min) are shown in Table S8 (Supporting Information). Either of the first two compounds could match the unknown on MS and retention data, whereas the larger molecules farther down in the list have much larger KRIs. The LRI calculated for unknown 2 was 251.0, giving an inclusion range of BP = 220.0−322.0 °C. The KRI inclusion range was 1472 ± 385.4, that is, 1086.6−1857.4. The NIST and MOLGEN-MS substructure information and the resulting compatible molecular formulas calculated by MOLGEN-MS are presented in Chart 3.

erated) and PubChem data sets (see Supporting Information, Table S-4, for more values) shows that the implementation of an energy criterion in candidate selection is valid for generated structures and is likely to help significantly in reducing the number of very sterically hindered candidates that can result from structure generation of very unsaturated molecules. Unknown Spectra. We isolated two unknown spectra, at 19.2 and 25.4 min, from a mutagenic subfraction of a sample collected from Pardubice, Czech Republic.25 The log Kow range of the compounds in this subfraction, from −0.15 to 1.35, was determined during RP-HPLC fractionation on a polymeric C18 column using a linear regression with seven standards.25 Tentative Identification of Unknown 1 (19.2 min). The database search for the first unknown is shown in Table S-6 (Supporting Information). The first two spectra and the corresponding NIST probabilities are very similar. The LRI calculated for unknown 1 was 224.7, leading to a possible BP inclusion range of 193.7−295.7 °C. The experimental KRI, 1320, resulted in an inclusion range of 934.6−1705 units. Although this range is very large, using the median error range (e.g., as in Kumari et al.19) would eliminate all database matches from consideration. The NIST and MOLGEN-MS substructure information and the resulting compatible molecular formulas calculated by MOLGEN-MS are presented in Chart 1. Chart 1. NIST and MOLGEN-MS Substructure Information and Possible Formulas for Unknown 1a

a

RDB = ring and double bond count.

The three formulas were used with the substructure information to generate 137 possible structures using MOLGEN-MS. Thus, we applied the idea of consensus candidate selection to select the final candidates, using eq 4, resulting in a CS ranging between 0.295 and 0.913. The ranges of all values calculated are given in Table S-7 (Supporting Information), and the top four candidates according to the consensus score are shown in Chart 2. Structure 56 is clearly

Chart 3. NIST and MOLGEN-MS Substructure Information and Possible Formulas for Unknown 2a

Chart 2. Spectral Match and Consensus Score Data for the Top Four Candidates for Unknown 1a

a

RDB = ring and double bond count.

The two formulas were used with the substructure information to generate 561 possible structures using MOLGEN-MS, all with formula C8H5NO2. The procedure used for unknown 1 was followed here; the criteria for the consensus scores are presented in Table S-9 (Supporting Information), and the top four candidates are shown in Chart 4. In general, the range of predicted data for this set of generated structures is much lower than in the previous example. Although 15 structures were within the BP/LRI range, all of these structures had very high energies. Possible reasons

a

MV = match value, MetFrag S = MetFrag score, CS = consensus score. 3291



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Schymanski et al.11 In the following subsections, we discuss the contributions of the different steps to the workflow. MetFrag for GC/MS. The extension of MetFrag to predict fragmentation for GC/MS data provides a vital, freely accessible, and open-source contribution to the identification of unknown GC-EI/MS spectra, using either database searching (KEGG, PubChem, or ChemSpider) or structure-data files (SDFs), generated, for example, by MOLGEN, as described here. The results reported in the preceding section show that, although the overall candidate ranking is not quite as good as that obtained with Mass Frontier and MOLGEN-MS (RRP ≈ 0.27 compared with RRP = 0.36 for the default MetFrag settings), it is better than that obtained with ACD MS Fragmenter. More importantly, the scoring function used in MetFrag improved the ranking significantly over the match value calculated on the MetFrag fragments (RRP = 0.36 for the score versus 0.41 based on MV, which considers only the fragments produced and not the energy used to produce them). Whereas we calculated these values for the worst case here, separating generated structures for an unknown, the typical application of MetFrag, that is, ranking candidates based on a database formula search (more appropriate for nominal mass data than a mass search), is generally more successful, although success depends greatly on the number and type of database entries. With the two unknowns, a MetFrag ChemSpider search by formula had the correct unknown structure ranked equal first of 12 for unknown 1 and third of 105 for unknown 2 based on spectral match alone. Using the SDFs generated for the unknowns, MetFrag ranked the correct candidates in 14th place in both cases, of 137 (unknown 1) and 561 (unknown 2) candidates. As demonstrated herein, these rankings can be improved significantly when taking calculated properties of the structures into account, with the correct structures ranked in first place using consensus scoring. Calculated Properties. The use of calculated properties has again been shown to be vital for improved candidate selection. Here, we discuss the individual criteria in more detail. Mass Spectral Match. Although the various programs for mass spectral prediction (e.g., MOLGEN-MS, MetFrag, and MassFrontier) have the ability to identify the top candidates, on average, only in the top 27−38% of candidates (RRPs of 0.27− 0.38; see Table 1), the use of these programs in parallel could improve candidate selection. As MOLGEN-MS and MassFrontier both use fragmentation rules, MetFrag offers a purely combinatorial approach that also works for the cases where no rules exist. Although ACD Mass Fragmenter offers another alternative, our results from an earlier study indicate that it is not particularly useful for candidate selection with GC/MS data.15b Further, the fact that a candidate has a good match according to both rule-based and bond-breakage fragmentation is a good indication that it is a good match, whereas if both have poor values, the candidate is less likely to fit the data. Thus, it makes sense to include results from both approaches in a consensus scoring approach, rather than basing the results exclusively on one program. The program Fragment Identificator (FiD)39 could also be used to calculate fragments for candidate structures. It has been shown to outperform Mass Frontier (version 5.0) for accurate, tandem mass spectrometry data, especially for negative mode for which few fragmentation rules are included in Mass Frontier.39 FiD also uses a combinatorial approach to generate fragment structures and thus offers a complementary approach to MOLGEN-MS and Mass Frontier, similar to MetFrag. The

Chart 4. Spectral Match and Consensus Score Data for the Top Four Candidates for Unknown 2a

a

MV = match value, MetFrag S = MetFrag score, CS = consensus score.

for the discrepancy in values are discussed in the Discussion section. As a result, no structures satisfied all of the inclusion criteria in Table S-9 (Supporting Information). However, using the consensus scoring method, we prioritized structures that satisfied most of these criteria. The consensus score values ranged from 0.358 to 0.848, and the most likely candidate was clearly separated from the other top candidates, with all evidence pointing to structure 274, phthalimide. Confirmation of Unknown 2. The standard of the tentatively identified compound phthalimide was purchased from Riedel-de-Haen (Seelze, Germany) and measured using the same GC/MS methods with the KRI and LRI standards. A peak at 25.464 min was detected, with KRI = 1474 and LRI = 251.3, compared with the unknown's values of KRI = 1472 and LRI = 251.0. The NIST match5 between the measured standard and unknown spectrum was 947, with a reverse match value of 949, indicating excellent agreement between the spectra. In addition, the peak m/z [M − H]− = 146.0249 (0.7 ppm) was detected in LC/MS/MS analysis (APCI negative) of the sample BR1A1 at 4.80 min, consistent with the standard phthalimide retention time of 4.85 min with signal m/z [M − H]− = 146.0254 (4 ppm) and the calculated exact mass of [M − H]− = 146.0248 and monoisotopic mass of 147.0320 (C8H5NO2). As a result, the presence of phthalimide was confirmed using structure-generation methods, GC/MS analysis, and LC/MS/MS analysis. Toxicity Confirmation. Mutagenicity testing of both identified substances using the standards at eight concentrations between 0.022 and 360 mg/L for phthalimide and between 0.189 and 6200 mg/L for phthalic anhydride in the Ames fluctuation test revealed no mutagenicity associated with either compound. The upper bound was taken from experimental (phthalimide) and estimated (phthalic anhydride) water solubility values from EPISuite.36 Thus, both compounds were confirmed analytically but were not responsible for the toxicity of the sample. The recombined fraction was tested at eight concentrations between 0.6 and 160 mg of blue rayon equivalent/well and revealed that mutagenicity was not lost during the fractionation. Thus, as no other peaks of interest were found in the GC/MS chromatogram or in the other fractions analyzed, the toxicants responsible for the effects are either below the detection limits or not detectable using GC/ MS methods.

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DISCUSSION

The work presented here provides a number of important extensions to the structure elucidation methods proposed in 3292



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Steric Energy Considerations. The steric energy calculations were found to be among the most important calculations for ultimately identifying the correct candidate from the remaining structures generated for both unknown examples. The percentiles derived from the combined data set of 1698 compounds were, however, too conservative to eliminate all of the sterically hindered compounds shown in Charts 2 and 4. Through calculations on the large PubChem and MOLGEN data sets, we were able to show that the energy values for the generated structures were much higher than those for the existing structures in the PubChem database, especially for structures with a large degree of unsaturation (see Figure S-1, Supporting Information). As a result, we were able to improve the criteria used for Obenergy and MOPAC, which we took to be the 90th percentile of the PubChem data set. This was shown to be crucial in finally ranking the top candidate in first place for both unknown examples, as the results for both Obenergy Ghemical and UFF force fields had only the correct candidate below the energy criterion. Although we presented a number of possible criteria here and found the 90th percentile based on PubChem results to be useful for both examples, this approach needs to be tested on a larger range of compounds to ensure that the 90th percentile is the most applicable value in other cases. We were able to derive more stringent criteria for unsaturated molecules using the ring and double bond count (see Table S-3, Supporting Information), but this criterion actually resulted in the elimination of the correct candidate for unknown 2a fact that would have been compensated in the consensus scoring approach. As a result, we chose to stick with the more widely applicable PubChem criterion. Before using these criteria, it is important to be sure that they are applicable for the candidates in question. We observed large differences in the values calculated using some of the programs depending on the starting format of the input files, especially Obenergy and MOPAC. As MOLGEN-MS generated twodimensional SDFs by default, this is the starting format we used for the PubChem and MOLGEN data sets. Inputs for Obenergy must be optimized using gen3d prior to calculation to obtain reasonable results. Using three-dimensional SDFs saved by MOLGEN-QSPR instead of gen3d resulted in consistently lower Obenergy results. Neither MOPAC nor Obenergy can handle zero-dimensional SDFs generated, for example, with SMILES codes. If in doubt, the 1698-molecule data set can be used to provide a quick and still useful criterion to exclude the majority of sterically unlikely candidates. Although there are alternatives to using energy values to restrict the unlikely candidates (e.g., restricting molecule generation to those with five or more atoms in the ring), this strategy has two disadvantages. One is that valid structures can be eliminated (e.g., epichlorohydrin, ChemSpider ID 131706, CAS RN 67843-74-7, or irgarol, ChemSpider ID 82701, CAS RN 28159-98-0), and the other is that bridged structures can still get through these restrictions (e.g., structure 57 in Chart 2, ChemSpider ID 56321, CAS RN 4891-67-2). Thus, these energy values provide a quick look at the energetic likelihood of the molecules and focuses consideration on the more likely candidates, irrespective of the restrictions used in structure generation. Consensus Scoring. Although the use of structure generation, substructure classifiers, and exclusion criteria has already been shown to be a valuable method of providing additional evidence for a tentative identification, the scoring of

much longer computation times, especially for nominal mass data, mean that FiD is less suited to the candidate selection process proposed here, compared with the much faster MetFrag. However, FiD could be used to separate a few candidate structures once only a few structures remain in consideration. Retention Prediction. The use of the BP/LRI correlation of Eckel and Kind,37 shown to be useful despite the large errors associated with the predictions,11 yielded more mixed results here. This criterion was useful for unknown 1 (only 35 candidates matched this criterion, including the correct structure), but would result in the exclusion of the correct candidate (and most others) for unknown 2 if a filtering approach were applied. This finding warranted more investigation. Although no LRI was previously available for phthalimide, a comparison of boiling-point data using a few common sources revealed a very large discrepancy in predicted and experimental values for phthalimide (all in °C): 424.5 (EPISuite36), 359 (ACD/Laboratories within ChemSpider9), 308 (SPARC40), and 366 (experimental, listed in ChemSpider9). These values show a range of over 100 K, indicating that the boiling point might be an unreliable criterion for this compound or even class of compounds. Eckel and Kind also reported a relationship for calculating the boiling point from the LRI,37 which gives BP = 267 °C for phthalimide using the data measured here, which is within the inclusion range shown in Table S-9 (Supporting Information). Again, this result highlights the advantage of consensus scoring rather than candidate filtering or exclusion; the former method chooses candidates that match the most criteria, whereas the latter excludes all those failing at least one criterion and thus makes no consideration for prediction errors, as in the case here. Thus, the consideration of all criteria, rather than progressive filtering of candidates, avoids the early exclusion of candidates as a result of such problems. The inclusion of the NIST KRI prediction provides an important alternative to the BP/LRI correlation and did not exclude as many candidates for unknown 2. The error margin used here was conservative (the 95% confidence interval), which resulted in a very large inclusion range, so that only very few candidates were outside this range. Our previous experience indicates that this conservative value is necessary to avoid false negatives,11 and the correct candidates for both unknowns would have been outside the range if we had adopted the less conservative value used by Kumari et al.19 In the future, using fuzzy logic to create softer decision criteria (rather than 1 if within the range and 0 if outside the range) might offer a compromise between the less and more conservative approaches. Partitioning Behavior. The partitioning behavior has been shown in many previous studies to be useful in terms of candidate selection.14,29,41 Although this was not the case for the two unknown examples presented here (the criteria using three different implementations could exclude a maximum of only 13% of candidates for unknown 2 and 11% for unknown 1), partitioning behavior is still an important part of candidate selection in general. Trials on more unknowns will be able to provide more information about the use of this criterion in the context of consensus scoring. The consensus scoring used here approaches the consensus log P42 and the value available online,43 although with only limited results available in batch mode. 3293



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the chances of achieving a reliable identification in the end. The current method provides a valuable contribution to the identification of unknown organic contaminants of environmental significance, and extension to LC-based techniques will further strengthen the use of structure generation for unknown identification.

candidates using a consensus approach (rather than progressive filtering or exclusion) improves the applicability of the method considerably. Although we tested only two unknowns here (no other peaks were present in the samples), especially unknown 2 shows the benefits clearly when predictions result in false negatives (see the section Retention Prediction). As the use of many of the criteria proposed are unknown-specific, the combination of all criteria in one score is a clear advantage, such that thresholds are no longer necessary, rather just an examination of the top few candidates. This is important when considering calculations from many programs, especially in the case where one criterion provides erroneous results for some compounds or classes. This scoring technique also means that the user can choose as many or as few calculations as desired during candidate selection; only the division factors in eq 4 will need to be adjusted. We chose freely available software where possible to allow as many potential users as possible to access the methods applied here. The application of this method to more samples will provide further evidence for the use of the energy criterion coupled with the other criteria for candidate selection. Additionally, the use of this method on more spectra will allow the development of more fuzzy criteria for the scoring system, rather than the yes/no approach used here. The use of a hard criterion is not especially appropriate for predictions with large errors, but the data set in this work was too small to train fuzzy criteria. This would be of great interest for further research. Although mutagenicity testing showed that the compounds identified here were not associated with the mutagenicity in the sample, the only two significant peaks of interest detected in the mutagenic samples based on GC/MS measurements were confirmed analytically following identification using the methods developed during this study. Identification efforts of mutagenic compounds based on LC/MS/MS measurements are continuing and will be presented elsewhere.44 These results confirm that many compounds of relevance in the environment cannot be analyzed using GC/EI-MS. Derivatization of samples prior to analysis can also disrupt identification efforts.19 An extension of the methods developed here to LC/MSn-based systems would be of distinct advantage in the identification of environmentally relevant compounds. One of the major hurdles blocking progress in this direction is the lack of substructural classifiers for non-EI-MS techniques, thus resulting in the generation of too many candidates for each unknown. Although exact mass data provide information about the formulas of substructures present and some annotation methods have already been developed,45 they are not yet associated with the probabilities that formed an integral part of the methods described and tested here.

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ASSOCIATED CONTENT

S Supporting Information *

(1) List of abbreviations, (2) additional information on analytical methods and energy calculations, (3) additional results for conformational energy, and (4) additional results for the unknown identification. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +41 58 765 5537. Fax: +41 58 765 5826. E-mail: emma. [email protected]. Present Address ‡

Eawag - Swiss Federal Institute of Aquatic Science and Technology, Ü berlandstrasse 133, CH-8600 Dü bendorf, Switzerland. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the European Commission through the integrated projects MODELKEY (Contract 511237-GOCE) and the Marie Curie Research Training Network KEYBIOEFFECTS (Contract MRTN-CT-2006035695) and by the Helmholtz Interdisciplinary Graduate School for Environmental Research (HIGRADE). We thank A. Kerber and co-workers for the ongoing use and development of the MOLGEN programs, S. Stein for permission to use the NIST KRI prediction algorithm and T. Kind for providing the wrapper, M. Heinrich for performing the GC/MS measurements, G. Schüürmann for fruitful discussions, and J. Hollender for the opportunity to complete this work. A free academic license for ChemAxon MarvinSketch and Calculator Plugins was provided by ChemAxon Ltd.

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REFERENCES

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CONCLUSIONS The results reported herein demonstrate that structure generation can be used as a viable alternative to database searching to identify unknown compounds and even unknown unknowns measured using GC/EI-MS alone. Although developed specifically for EDA studies, these methods are applicable to any GC/EI-MS data set and thus fill a gap in spectral identification where spectra are not present in a given database. This approach provides a valuable enhancement to the identification of unknown compounds in complex environmental or other samples based on GC/EI-MS. The work presented herein clearly shows that the more information available for use in candidate selection, the better 3294



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