Computational Expansion of High-Resolution-MS(n) Spectral Libraries

[Image: see text] 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.