The identification of unknown compounds plays a critical role in forensic sciences. Many chemical forensics laboratories utilize GC-MS and LC-MS/MS to perform high-throughput analysis of samples including explosives, drugs of abuse and associated metabolites. However in many cases authentic analytical reference standards are not readily available to perform classical library search. Here approaches that were developed in computational metabolomics can help to alleviate the problem. In case of in silico fragmentation algorithms, the experimental mass spectrum or tandem mass spectrum (MS/MS) is directly imported into the software, spectral peaks are associated with structural fingerprints and ranked lists of potential candidates are presented as output. Tools such as CSI:FingerID, ChemDistiller, MetFrag, CFM-ID and MS-Finder have been successfully used to annotate unknown compounds. Another approach is to computationally generate mass spectra directly from compound structures by means of machine learning (CFM-ID) heuristic approaches (LipidBlast) or quantum chemistry approaches (QCEIMS). These in silico library methods have been successfully utilized in many metabolomics labs that process thousands of samples per year. Advantages and disadvantages will be discussed. Experimental and theoretical reference mass spectra of confirmed novel metabolites should be shared immediately with the community. Here publicly available open access mass spectral databases such as MassBank and MoNA provide a technological platform to obtain access to the novel reference mass spectra in metabolomics. Such databases could be readily adapted to forensic sciences. Modern data sharing approaches including the sharing of actual experimental data and chemical fingerprints of samples including chromatograms of complex mixtures could lead to synergistic success stories between chemical forensics labs. Examples from metabolomics data sharing platforms including MetaboLights, the Metabolomics Workbench and GNPS will be discussed.

Identification of unknown compounds using in silico fragmentation algorithms and in silico reference libraries

2019 Fall National Meeting

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