A Validated Method for Predicting Small Molecule Ionization Sites using Gibb's Free Energies
Jessica L. Bade, Sean M. Colby, Ryan S. Renslow, Thomas O. Metz

TL;DR
This paper introduces a new Gibb's free energy-based method within the ISiCLE pipeline for accurately predicting small molecule ionization sites, significantly improving metabolite identification in complex biological systems.
Contribution
The study develops and validates a GFE-based approach for ionization site prediction, outperforming pKb methods in accuracy within the ISiCLE framework.
Findings
GFE method predicts ionization sites with 100% accuracy.
pKb method shows lower accuracy in ionization site prediction.
Enhanced metabolite identification accuracy in metabolomics studies.
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…
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Taxonomy
TopicsMetabolomics and Mass Spectrometry Studies · Mass Spectrometry Techniques and Applications · Computational Drug Discovery Methods
