Assigning confidence to structural annotations from mass spectra with COSMIC

Abstract

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but these libraries are vastly incomplete; in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. As biological interpretation relies on accurate structure annotations, the ability to assign confidence to such annotations is a key outstanding problem. We introduce the COSMIC workflow that combines structure database generation, in silico annotation, and a confidence score consisting of kernel density p-value estimation and a Support Vector Machine with enforced directionality of features. In evaluation, COSMIC annotates a substantial number of hits at small false discovery rates, and outperforms spectral library search for this purpose. To demonstrate that COSMIC can annotate structures never reported before, we annotated twelve novel bile acid conjugates; nine structures were confirmed by manual evaluation and two structures using synthetic standards. Second, we annotated and manually evaluated 315 molecular structures in human samples currently absent from the Human Metabolome Database. Third, we applied COSMIC to 17,400 experimental runs and annotated 1,715 structures with high confidence that were absent from spectral libraries.

A Synthesis-Based Reverse Metabolomics Approach for the Discovery of Chemical Structures from Humans and Animals.

“…Recently, we reported that gut microbes in humans and mice can also conjugate cholic acid with Leu, Phe, and Tyr 33 . Since then, we have also found Ser conjugated to cholic acid, Phe conjugated to deoxycholate 19 and in silico predictions for twelve bile acids conjugates where Phe and Trp chenodeoxycholic acid conjugates were validated 34 . Following these discoveries, we imagined that these microbially conjugated bile acids were part of a much larger class of bile acid amidates that includes other naturally occurring amino acids, both proteinogenic and non proteinogenic.…”

The LOTUS initiative for open knowledge management in natural products research

“…The authors have previously demonstrated that taxonomically informed metabolite annotation is critical for the improvement of the NP annotation process ( Rutz et al, 2019 ). Alternative approaches linking structural annotation to biological organisms have also shown substantial improvements ( Hoffmann et al, 2021 ). In this context, the LOTUS initiative offers new opportunities for linking chemical objects to both their biological occurrences and spectral information and should significantly facilitate such applications.…”

Exploring correlations between MS and NMR for compound identification using essential oils: A pilot study

“…19 However, NMR delivers highly specific structural information and direct quantification capabilities. 1,20 Even though, most studies dealing with complex mixtures and NPs tend to prefer the use of MS-based techniques, the compound annotation capability of MS is intrinsically limited to molecular weight and other general structure information 21 ; e.g. it is generally impossible to assert the position of substituents.…”