Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification

The
inability to identify the structures of most metabolites detected
in environmental or biological samples limits the utility of nontargeted
metabolomics. The most widely used analytical approaches combine mass
spectrometry and machine learning methods to rank candidate structures
contained in large chemical databases. Given the large chemical space
typically searched, the use of additional orthogonal data may improve
the identification rates and reliability. Here, we present results
of combining experimental and computational mass and IR spectral data
for high-throughput nontargeted chemical structure identification.
Experimental MS/MS and gas-phase IR data for 148 test compounds were
obtained from NIST. Candidate structures for each of the test compounds
were obtained from PubChem (mean = 4444 candidate structures per test
compound). Our workflow used CSI:FingerID to initially score and rank
the candidate structures. The top 1000 ranked candidates were subsequently
used for IR spectra prediction, scoring, and ranking using density
functional theory (DFT-IR). Final ranking of the candidates was based
on a composite score calculated as the average of the CSI:FingerID
and DFT-IR rankings. This approach resulted in the correct identification
of 88 of the 148 test compounds (59%). 129 of the 148 test compounds
(87%) were ranked within the top 20 candidates. These identification
rates are the highest yet reported when candidate structures are used
from PubChem. Combining experimental and computational MS/MS and IR
spectral data is a potentially powerful option for prioritizing candidates
for final structure verification.