High resolution mass spectrometry (HRMS) and non-targeted analysis (NTA) are advancing the identification of emerging contaminants in environmental and agricultural matrices. However, confidence in structure identification of unknowns in NTA presents challenges to analytical chemists. Structure identification requires integration of complementary data types such as reference databases, fragmentation prediction tools, and retention time prediction models. The goal of this research is to optimize and implement structure identification functionality within the US EPA’s CompTox Chemicals Dashboard, an open chemistry resource and web application containing data for ~760,000 substances. Rank-ordering the number of sources associated with chemical records within the Dashboard (Data Source Ranking) improves the identification of unknowns by bringing the most likely candidate structures to the top of a search results list. Incorporating additional data streams contained within the database underlying the Dashboard further enhances identifications. Integrating tandem mass spectrometry data into NTA workflows enables spectral match scores and increases confidence in structural assignments. We have generated and stored predicted MS/MS fragmentation spectra for the entirety of the Chemistry Dashboard using the in silico prediction tool CFM-ID. Predicted fragments incorporated into the identification workflow were used as both a scoring term and as a candidate threshold cutoff. Combining these steps within an open chemistry resource provides a freely available software tool for structure identification and NTA. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.

1. Consensus ranking and fragmentation
prediction for identification of unknowns
in high resolution mass spectrometry
Antony Williams1, Andrew D. McEachran2, Tommy Cathey3,
Tom Transue3 and Jon R. Sobus4
1) National Center for Computational Toxicology, U.S. Environmental Protection Agency, RTP, NC
2) Oak Ridge Institute of Science and Education (ORISE) Research Participant, RTP, NC
3) GDIT, Research Triangle Park, North Carolina, United State
4) National Exposure Research Laboratory, U.S. Environmental Protection Agency, RTP, NC
Spring 2019
ACS Spring Meeting, Orlando
http://www.orcid.org/0000-0002-2668-4821
The views expressed in this presentation are those of the author and do not necessarily reflect the views or policies of the U.S. EPA

2. Suspect Screening and Non-Targeted
Analysis Workflows
1
DSSTox Chemical Database
“Molecular Features”
Extracted Samples
Raw Samples
Raw Features
Matched Formulas
Mapped Structures
Prioritized Structures
(using ToxPi)
Confirmed Structures
(using ToxCast standards)
Processed Features
Prioritized Features
Predicted Formulas
Candidate Structures
Sorted Structures
Predicted Retention Times
Predicted/Observed Functional Use
Top Candidate Structure(s)
Suspect Screening Non-Targeted Analysis
Predicted Concentrations
Predicted/Observed Media Occurrence
Predicted Mass Spectra
Methodological Concordance
Red = Analytical Chemistry
Blue = Data Processing & Analysis
Green = Informatics & Web Services
Purple = Mathematical & QSPR Modeling
Color Key

3. CompTox Chemicals Dashboard
https://comptox.epa.gov/dashboard
2
875k Chemical Substances

4. Detailed Chemical Pages
3

5. Access to Chemical Hazard Data
4

6. Sources of Exposure to Chemicals
5

7. Link Access
6

8. MassBank of North America
https://mona.fiehnlab.ucdavis.edu
7

9. Mass/Formula
Searching and
Metadata Ranking
8

10. Advanced Searches
Mass Search
9

11. Advanced Searches
Mass Search
10

12. MS-Ready Structures for
Formula Search
11

13. “MS-Ready Structures”
https://doi.org/10.1186/s13321-018-0299-2
12

14. 13

15. MS-Ready Mappings
14

16. MS-Ready Mappings Set
15

17. MS-Ready Mappings
• EXACT Formula: C10H16N2O8: 3 Hits
16

18. MS-Ready Mappings
• Same Input Formula: C10H16N2O8
• MS Ready Formula Search: 125 Chemicals
17

19. MS-Ready Mappings
• 125 chemicals returned in total
– 8 of the 125 are single component chemicals
– 3 of the 8 are isotope-labeled
– 3 are neutral compounds and 2 are charged
18

20. Candidate ranking
using public
resources
19

21. Data Source Ranking of
“known unknowns”
20
• Mass and/or formula is for an
unknown chemical but contained
within a reference database
• Most likely candidate chemicals
have the most associated data
sources, most associated lit.
articles or both
C14H22N2O3
266.16304
Chemical
Reference
Database
Sorted candidate
structures

22. Is a bigger database better?
21
• ChemSpider was 26 million chemicals then
• Much BIGGER today
• Is bigger better??

23. Using Metadata for Ranking
• Use available metadata to rank candidates
– Associated data sources
• Associated lists in the underlying database
• Associated data sources in PubChem
• Specific types (e.g. water, surfactants, pesticides etc.)
– Number of associated literature articles (Pubmed)
– Chemicals in the environment – the number of
products/categories containing the chemical is a
very important source of data
22

24. Identification ranks for 1783 chemicals
using multiple data streams
23
DS: Data Sources
PC: PubChem
PM: PubMed
STOFF: DB
KEMI: DB

25. Comparing Search Performance
24
• Dashboard content was 720k chemicals
• Only 3% of ChemSpider size
• What was the comparison in performance?

26. SAME dataset for comparison
25

27. How did performance compare?
26
For the same 162 chemicals,
Dashboard outperforms
ChemSpider

28. How did performance compare?
27

29. Data Quality is important
• Data quality in free web-based databases!
28

30. Will the correct Microcystin LR Stand Up?
ChemSpider Skeleton Search
29

31. Comparing ChemSpider Structures
30

32. Comparing ChemSpider Structures
31

33. Other Searches
32

34. Processing
thousands of
chemical signatures
as mass and formula
33

35. Batch Searching
• Singleton searches are useful but we work
with thousands of masses and formulae!
• Typical questions
– What is the list of chemicals for the formula CxHyOz
– What is the list of chemicals for a mass +/- error
– Can I get chemical lists in Excel files? In SDF files?
– Can I include properties in the download file?
34

36. Batch Searching Formula/Mass
35

37. Searching batches using MS-Ready
Formula (or mass) searching
36

38. Related Searches
to Support Mass
Spectrometry
37

39. Find me “related structures”
Formula-Based Search
38

40. Select Chemicals of Interest
39

41. Find me “related structures”
Based on Structure Similarity
40

42. Find me “related structures”
Based on Structure Similarity
41

43. Find me “related structures”
Structure Similarity – sort on mass
42

44. Literature Searching
43

45. Literature Searching
44

46. Literature Searching
45

47. The benefits of
building
chemical lists
46

48. Chemical Lists
47

49. EPAHFR: Hydraulic Fracturing
48

50. PFAS lists of Chemicals
49

51. Ongoing
Research in
Progress
50

52. Suspect Screening and Non-Targeted
Analysis Workflow
51
DSSTox Chemical Database
“Molecular Features”
Extracted Samples
Raw Samples
Raw Features
Matched Formulas
Mapped Structures
Prioritized Structures
(using ToxPi)
Confirmed Structures
(using ToxCast standards)
Processed Features
Prioritized Features
Predicted Formulas
Candidate Structures
Sorted Structures
Predicted Retention Times
Predicted/Observed Functional Use
Top Candidate Structure(s)
Suspect Screening Non-Targeted Analysis
Predicted Concentrations
Predicted/Observed Media Occurrence
Predicted Mass Spectra
Methodological Concordance
Red = Analytical Chemistry
Blue = Data Processing & Analysis
Green = Informatics & Web Services
Purple = Mathematical & QSPR Modeling
Color Key

53. Work in Progress
• Predicted Spectra for candidate ranking
– Viewing and Downloading pre-predicted spectra
– Search spectra against the database
52

54. Predicted Mass Spectra
http://cfmid.wishartlab.com/
• MS/MS spectra prediction for ESI+, ESI-, and EI
• Predictions generated and stored for >800,000
structures, to be accessible via Dashboard
53

55. Search Expt. vs. Predicted Spectra

56. Search Expt. vs. Predicted Spectra

57. Spectral Viewer Comparison
56

58. CASMI Contest
57
• Critical Assessment of Small Molecule Identification
– Training data= 312 peak lists (from 285 substances)
• 234 MS/MS in positive mode
• 58 in negative mode
– Challenge Data= 208 peak lists (from 188 substances)
• 127 in positive mode
• 81 in negative mode
• Precursor ion search window= 15 ppm
• Fragment ion match threshold= 0.02 Da
• Candidates limited to Dashboard results within precursor ion
search window – early work on 765K ONLY
http://www.casmi-contest.org/2016/index.shtml

59. Testing CFM-ID matching
8/20/18
# Identified % of Total
#1 Hits 89 43%
Top 5 154 74%
Top 10 174 84%
Top 20 190 91%
# Identified % of Total
#1 Hits 154 74%
Top 5 195 94%
Top 10 198 95%
Top 20 202 97%
CFM-ID only CFM-ID +DSSTox Data Sources
CASMI 2016 Contest Challenge Set (n=208)

60. Work in Progress
• Predicted Spectra for candidate ranking
– Viewing and Downloading pre-predicted spectra
– Search spectra against the database
• Retention Time Index Prediction
59

61. Retention Time Prediction for Ranking
60

62. Moving to Relative Retention Times
61

63. Work in Progress
• Predicted Spectra for candidate ranking
– Viewing and Downloading pre-predicted spectra
– Search spectra against the database
• Retention Time Index Prediction
• Structure/substructure/similarity search
62

64. Prototype Development
63

65. Prototype Development
64

66. Work in Progress
• Predicted Spectra for candidate ranking
– Viewing and Downloading pre-predicted spectra
– Search spectra against the database
• Retention Time Index Prediction
• Structure/substructure/similarity search
• Access to API and web services for
programmatic access
65

67. API services and Open Data
• Groups waiting on our API and web services
• Mass Spec companies instrument integration
• Release will be in iterations but for now our
data are available
66

68. Benefiting the
community with
Open Data
67

69. NORMAN Suspect List Exchange
https://www.norman-network.com/?q=node/236
68

70. Integration to MetFrag in place
https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0299-2
69

71. Conclusion
• Dashboard access to data for ~875,000 chemicals
• MS-Ready data facilitates structure identification
• Related metadata facilitates candidate ranking
70
• Relationship mappings and
chemical lists of great utility
• Dashboard and contents
are one part of the solution
• We are committed to open
API development with time..

72. Acknowledgements
• IT Development team – especially Jeff
Edwards and Jeremy Dunne
• Chris Grulke for the ChemReg system
• NERL colleagues – Jon Sobus, Elin Ulrich,
Mark Strynar, Seth Newton, Alex Chao
• Reza Aalizadeh & Nikolaos Thomaidis, Athens
• Emma Schymanski, LCSB, Luxembourg
• The NORMAN Network and all contributors
71

73. Contact
Antony Williams
US EPA Office of Research and Development
National Center for Computational Toxicology
EMAIL: Williams.Antony@epa.gov
ORCID: https://orcid.org/0000-0002-2668-4821
72