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

Theinability to identify the structures of most metabolites detectedin environmental or biological samples limits the utility of nontargetedmetabolomics. The most widely used analytical approaches combine massspectrometry and machine learning methods to rank candidate structurescontained in large chemical databases. Given the large chemical spacetypically searched, the use of additional orthogonal data may improvethe identification rates and reliability. Here, we present resultsof combining experimental and computational mass and IR spectral datafor high-throughput nontargeted chemical structure identification.Experimental MS/MS and gas-phase IR data for 148 test compounds wereobtained from NIST. Candidate structures for each of the test compoundswere obtained from PubChem (mean = 4444 candidate structures per testcompound). Our workflow used CSI:FingerID to initially score and rankthe candidate structures. The top 1000 ranked candidates were subsequentlyused for IR spectra prediction, scoring, and ranking using densityfunctional theory (DFT-IR). Final ranking of the candidates was basedon a composite score calculated as the average of the CSI:FingerIDand DFT-IR rankings. This approach resulted in the correct identificationof 88 of the 148 test compounds (59%). 129 of the 148 test compounds(87%) were ranked within the top 20 candidates. These identificationrates are the highest yet reported when candidate structures are usedfrom PubChem. Combining experimental and computational MS/MS and IRspectral data is a potentially powerful option for prioritizing candidatesfor final structure verification.