Computational Expansion of High-Resolution-MSⁿ Spectral Libraries

Commonly, in MS-based untargeted metabolomics, some metabolites cannot be confidently identified due to ambiguities in resolving isobars and structurally similar species. To address this, analytical techniques beyond traditional MS² analysis, such as MSⁿ fragmentation, can be applied to probe metabolites for additional structural information. In MSⁿ fragmentation, recursive cycles of activation are applied to fragment ions originating from the same precursor ion detected on an MS¹ spectrum. This resonant-type collision-activated dissociation (CAD) can yield information that cannot be ascertained from MS² spectra alone, which helps improve the performance of metabolite identification workflows. However, most approaches for metabolite identification require mass-to-charge (m/z) values measured with high resolution, as this enables the determination of accurate mass values. Unfortunately, high-resolution-MSⁿ spectra are relatively rare in spectral libraries. Here, we describe a computational approach to generate a database of high-resolution-MSⁿ spectra by converting existing low-resolution-MSⁿ spectra using complementary high-resolution-MS² spectra generated by beam-type CAD. Using this method, we have generated a database, derived from the NIST20 MS/MS database, of MSⁿ spectral trees representing 9637 compounds and 19386 precursor ions where at least 90% of signal intensity was converted from low-to-high resolution.