A Validated Method for Predicting Small Molecule Ionization Sites using Gibb's Free Energies

Abstract

Accurate molecular identification of metabolites can unlock new areas of the molecular universe and allow greater insight into complex biological and environmental systems than currently possible. Analytical approaches for measuring the metabolome, such as NMR spectroscopy, and separation techniques coupled with mass spectrometry, such as LC-IMS-MS, have risen to this challenge by yielding rich experimental data that can be queried by cross-reference with similar information for known standards in reference libraries. Confident identification of molecules in metabolomics studies, though, is often limited by the diversity of available data across chemical space, the unavailability of authentic reference standards, and the corresponding lack of comprehensiveness of standard reference libraries. The In Silico Chemical Library Engine (ISiCLE) addresses theses hindrances by providing a first-principles, cheminformatics pipeline that yields collisional cross section (CCS) values for any given molecule and without the need for training data. In this program, chemical identifiers undergo MD simulations, quantum chemical transformations, and ion mobility calculations for the generation of predicted CCS values. Here, we present a new module for ISiCLE that addresses the sensitivity of CCS predictions to ionization site location. An update to adduct creation methods is proposed concerning a transition from pKa and pKb led predictions to a Gibb's free energy (GFE) based determinacy of true ionization site location. A validation set of experimentally confirmed molecular protonation sites was assembled from literature and cross-referenced with the respective pKb predicted locations and GFE values for all potential ionization site placements. Upon evaluation of the two methods, the lowest GFE value was found to predict the true ionization site location with 100% accuracy while pKb had less accuracy.