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In Silico MS/MS Spectra for Identifying Unknowns: A Critical Examination Using CFM-ID Algorithms and ENTACT Mixture Samples
Citation:
Chao, A., H. Al Ghoul, A. McEachran, I. Balabin, T. Transue, T. Cathey, J. Grossman, R. Singh, E. Ulrich, A. Williams, AND J. Sobus. In Silico MS/MS Spectra for Identifying Unknowns: A Critical Examination Using CFM-ID Algorithms and ENTACT Mixture Samples. Analytical and Bioanalytical Chemistry. Springer, New York, NY, 412:1303–1315, (2020). https://doi.org/10.1007/s00216-019-02351-7
Impact/Purpose:
Confident identification of unknowns in NTA studies often requires the use of reference library spectra. The relatively modest size of existing reference libraries limits the number of possible identifications for any given study. Use of in silico MS2 libraries can expand coverage into areas not reached by reference libraries alone. Analyses of the ENTACT mixture data shows promising results for the performance of in silico spectra towards aiding chemical identification strategies. The expansion of NTA workflows to incorporate in silico spectra for DSSTox compounds will enable more rapid and certain identifications of xenobiotics and other emerging compounds.
Description:
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.
URLs/Downloads:
DOI: In Silico MS/MS Spectra for Identifying Unknowns: A Critical Examination Using CFM-ID Algorithms and ENTACT Mixture Samples
https://pubmed.ncbi.nlm.nih.gov/31965249