Computational Expansion of High-Resolution-MS n 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 MS 2 analysis, such as MS n fragmentation, can be applied to probe metabolites for additional structural information. In MS n fragmentation, recursive cycles of activation are applied to fragment ions originating from the same precursor ion detected on an MS 1 spectrum. This resonant-type collision-activated dissociation (CAD) can yield information that cannot be ascertained from MS 2 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-MS n spectra are relatively rare in spectral libraries. Here, we describe a computational approach to generate a database of high-resolution-MS n spectra by converting existing low-resolution-MS n spectra using complementary high-resolution-MS 2 spectra generated by beam-type CAD. Using this method, we have generated a database, derived from the NIST20 MS/MS database, of MS n spectral trees representing 9637 compounds and 19386 precursor ions where at least 90% of signal intensity was converted from low-to-high resolution.