Large-Scale Prediction of Collision Cross-Section Values for Metabolites in Ion Mobility-Mass Spectrometry

The
rapid development of metabolomics has significantly advanced
health and disease related research. However, metabolite identification
remains a major analytical challenge for untargeted metabolomics.
While the use of collision cross-section (CCS) values obtained in
ion mobility-mass spectrometry (IM-MS) effectively increases identification
confidence of metabolites, it is restricted by the limited number
of available CCS values for metabolites. Here, we demonstrated the
use of a machine-learning algorithm called support vector regression
(SVR) to develop a prediction method that utilized 14 common molecular
descriptors to predict CCS values for metabolites. In this work, we
first experimentally measured CCS values (ΩN2) of
∼400 metabolites in nitrogen buffer gas and used these values
as training data to optimize the prediction method. The high prediction
precision of this method was externally validated using an independent
set of metabolites with a median relative error (MRE) of ∼3%,
better than conventional theoretical calculation. Using the SVR based
prediction method, a large-scale predicted CCS database was generated
for 35 203 metabolites in the Human Metabolome Database (HMDB).
For each metabolite, five different ion adducts in positive and negative
modes were predicted, accounting for 176 015 CCS values in
total. Finally, improved metabolite identification accuracy was demonstrated
using real biological samples. Conclusively, our results proved that
the SVR based prediction method can accurately predict nitrogen CCS
values (ΩN2) of metabolites from molecular descriptors
and effectively improve identification accuracy and efficiency in
untargeted metabolomics. The predicted CCS database, namely, MetCCS,
is freely available on the Internet.