Development of Ecom₅₀ and Retention Index Models for Nontargeted Metabolomics: Identification of 1,3-Dicyclohexylurea in Human Serum by HPLC/Mass Spectrometry

Abstract

The goal of many metabolomic studies is to identify the molecular structure of endogenous molecules that are differentially expressed among sampled or treatment groups. The identified compounds can then be used to gain an understanding of disease mechanisms. Unfortunately, despite recent advances in a variety of analytical techniques, small molecule (<1000 Da) identification remains difficult. Rarely can a chemical structure be determined from experimental “features” such as retention time, exact mass, and collision induced dissociation spectra. Thus, without knowing structure, biological significance remains obscure. In this study we explore an identification method in which the measured exact mass of an unknown is used to query available chemical databases to compile a list of candidate compounds. Predictions are made for the candidates using models of experimental features that have been measured for the unknown. The predicted values are used to filter the candidate list by eliminating compounds with predicted values substantially different from the unknown. The intent is to reduce the list of candidates to a reasonable number that can be obtained and measured for confirmation. To facilitate this exploration, we measured data and created models for two experimental features; MS Ecom50 (the energy in eV required to fragment 50% of a selected precursor ion) and HPLC retention index. Using a dataset of 52 compounds, Ecom50 models were developed based on both Molconn and CODESSA structural descriptors. These models gave r2 values of 0.89 to 0.94 depending on the number of inputs, the modeling algorithm chosen, and whether neutral or protonated structures were used. The retention index model was developed with 400 compounds using a back propagation artificial neural network and 33 Molconn structure descriptors. External validation gave a v2 = 0.86 and standard error of 38 retention index units. As a test of the validity of the filtering approach, the Ecom50 and retention index models, along with exact mass and collision induced dissociation spectra matching, were used to identify 1,3-dicyclohexylurea in human plasma. This compound was not previously known to exist in human biofluids and its elemental formula was identical to 315 other candidate compounds downloaded from PubChem. These results suggest that the use of Ecom50 and retention index predictive models can improve non-targeted metabolite structure identification using HPLC/MS derived structural features.

Retention time prediction for dereplication of natural products (CxHyOz) in LC–MS metabolite profiling

“…In NP research, molecular formulae can be used in combination with chemotaxonomic information to generate putative structure attribution to a given LC peak (Funari et al, 2012;Smyth et al, 2012). This process however only lead peak annotation with given confidence levels (Sumner et al, 2007) and orthogonal dimension to further strengthen on-metabolite identification are needed (Creek et al, 2014;Hall et al, 2012 The preliminary study of the composition of natural extracts (metabolite profiling) is mainly performed using LC-HR-MS (Eugster et al, 2011). In this process, the efficient separation of the metabolites in complex mixtures prior to MS detection is important to generate high quality data for dereplication.…”

“…Most QSRR models are built using multiple linear regression (MLR) (Creek et al, 2011;Kaliszan et al, 2005), partial least squares (PLS) regression (Kempe and Kempe, 2010), and artificial neural networks (ANN) (Akbar et al, 2012;D'Archivio et al, 2010;Hall et al, 2012). The models usually predict retention factors (k), their logarithm (log k) or the RT from a representative calibration set of analytes.…”

Discriminating precursors of common fragments for large-scale metabolite profiling by triple quadrupole mass spectrometry

“…First, after initial MS 1 profiling of the sample, the prior probability of an ion identity given the precursor mass can be obtained by modeling the expected retention times of each structure (Creek et al, 2011;Hall et al, 2012;Stanstrup et al, 2013). Second, while our work selected all fragments simultaneously, if multiple experiments are used to profile a sample, fragments can be selected based on obtained experimental results, restricting the likely structure candidates for each experiment.…”

Correction of precursor and product ion relative abundances in order to standardize CID spectra and improve Ecom50 accuracy for non-targeted metabolomics

“…Similar observations were made by Guzowski and Hieftje in a study characterizing RF only hexapole ion guides (Guzowski Jr & Hieftje 2001). Thus, inaccurate relative ion peak intensities can affect Ecom 50 calculations and hinder structure identification studies (Hall et al 2012). …”