Modeling the relative response factor of small molecules in positive electrospray ionization

Abstract

Technological advancements in liquid chromatography (LC) electrospray ionization (ESI) high-resolution mass spectrometry (HRMS) have made it an increasingly popular analytical technique in non-targeted analysis (NTA) of environmental and biological samples. One critical limitation of current methods in NTA is the lack of available analytical standards for many of the compounds detected in biological and environmental samples. Computational approaches can provide estimates of concentrations by modeling the relative response factor of a compound (RRF) expressed as the peak area of a given peak divided by its concentration. In this paper, we explore the application of molecular dynamics (MD) in the development of a computational workflow for predicting RRF. We obtained measurements of RRF for 48 compounds with LC – quadrupole time-of-flight (QTOF) MS and calculated their RRF. We used the CGenFF force field to generate the topologies and GROMACS to conduct the (MD) simulations. We calculated the Lennard-Jones and Coulomb interactions between the analytes and all other molecules in the ESI droplet, which were then sampled to construct a multilinear regression model for predicting RRF using Monte Carlo simulations. The best performing model showed a coefficient of determination (R²) of 0.82 and a mean absolute error (MAE) of 0.13 log units. This performance is comparable to other predictive models including machine learning models. While there is a need for further evaluation of diverse chemical structures, our approach showed promise in predictions of RRF.