In Silico Collision Cross Section Calculations to Aid Metabolite Annotation
Document Type
Article
Publication Date
4-4-2022
Publication Title
Journal of the American Society for Mass Spectrometry
Abstract
The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures.
Volume
33
Issue
5
First Page
750
Last Page
755
DOI
10.1021/jasms.1c00315
Rights
Copyright © 2022 American Society for Mass Spectrometry. Published by American Chemical Society. All rights reserved.
Recommended Citation
Das, Susanta K.; Aramis Tanemura, Kiyoto; Dinpazhoh, Laleh; Keng, Mithony; Schumm, Christina; Leahy, Lydia; Asef, Carter K.; Rainey, Markace; Edison, Arthur S.; Fernández, Facundo M.; and Merz, Kenneth M. Jr., "In Silico Collision Cross Section Calculations to Aid Metabolite Annotation" (2022). Mechanical Engineering Publications. 240.
https://digitalcommons.kettering.edu/mech_eng_facultypubs/240
