In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples.

Autor: Chao A; Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA. chao.alex@epa.gov.; Student Contractor, U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA. chao.alex@epa.gov., Al-Ghoul H; Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA.; Student Contractor, U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA., McEachran AD; Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA.; Agilent Technologies Inc., Santa Clara, CA, 95051, USA., Balabin I; General Dynamics Information Technology, 79 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA., Transue T; General Dynamics Information Technology, 79 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA., Cathey T; General Dynamics Information Technology, 79 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA., Grossman JN; Student Contractor, U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA.; Agilent Technologies Inc., Santa Clara, CA, 95051, USA., Singh RR; Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA.; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365, Esch-sur-Alzette, Luxembourg., Ulrich EM; U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA., Williams AJ; U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA., Sobus JR; U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA. sobus.jon@epa.gov.
Jazyk: angličtina
Zdroj: Analytical and bioanalytical chemistry [Anal Bioanal Chem] 2020 Feb; Vol. 412 (6), pp. 1303-1315. Date of Electronic Publication: 2020 Jan 22.
DOI: 10.1007/s00216-019-02351-7
Abstrakt: High-resolution mass spectrometry (HRMS) enables rapid chemical annotation via accurate mass measurements and matching of experimentally derived spectra with reference spectra. Reference libraries are generated from chemical standards and are therefore limited in size relative to known chemical space. To address this limitation, in silico spectra (i.e., MS/MS or MS2 spectra), predicted via Competitive Fragmentation Modeling-ID (CFM-ID) algorithms, were generated for compounds within the U.S. Environmental Protection Agency's (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database (totaling, at the time of analysis, ~ 765,000 substances). Experimental spectra from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) mixtures (n = 10) were then used to evaluate the performance of the in silico spectra. Overall, MS2 spectra were acquired for 377 unique compounds from the ENTACT mixtures. Approximately 53% of these compounds were correctly identified using a commercial reference library, whereas up to 50% were correctly identified as the top hit using the in silico library. Together, the reference and in silico libraries were able to correctly identify 73% of the 377 ENTACT substances. When using the in silico spectra for candidate filtering, an examination of binary classifiers showed a true positive rate (TPR) of 0.90 associated with false positive rates (FPRs) of 0.10 to 0.85, depending on the sample and method of candidate filtering. Taken together, these findings show the abilities of in silico spectra to correctly identify true positives in complex samples (at rates comparable to those observed with reference spectra), and efficiently filter large numbers of potential false positives from further consideration. Graphical abstract.
Databáze: MEDLINE
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