The structural identification of unknown metabolites in biological samples is often defined as a major challenge in metabolomics research. Most current metabolomics studies rely on mass spectrometry based analytical and computational methods for data extraction, interpretation and structural identification. However, no single computational method is able to interpret the structure of a metabolite solely from mass spectral data due to incompleteness in MS/MS databases and limitations in search algorithms. As an additional approach, we propose an infrared (IR) spectra based computational model for database searching. A molecule’s structure, including its arrangement of atoms in 3D space and interconnectivity, is depicted as a unique spectral pattern in IR spectra and provides the basis for structural discrimination. Our method identifies an unknown metabolite through the comparison of experimental gas-phase IR spectra with predicted IR spectra of candidate compounds obtained from the PubChem database. This computational workflow consists of a fast semi-empirical quantum-mechanical method for initial IR spectra prediction, ranking, and triaging followed by a final IR spectra prediction and ranking using density functional theory. We discuss several variables that influenced identification accuracy such as conformational effect, hydrogen bonding, and candidate bin size, and then demonstrate the potential application of this approach in identifying mass spectral precursor and fragment ions. Further, we demonstrate the possibility of combining IR and mass spectrum rankings into a single composite score for better identification reliability. Overall, this approach resulted in identification rates comparable to current mass spectrum-based methods, suggesting that matching computational with experimental IR spectra is a potentially powerful orthogonal addition to other non-targeted chemical structure identification methods. This work opens new possibilities for advancing non-targeted metabolomics, as well as other analytical techniques that rely on non-targeted structure determination.

High-throughput non-targeted chemical structure identification by searching chemical databases using gas-phase infrared spectra

ACS Fall 2021

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