Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion’s Polarizability and Molecular Mass with Limited Data
Issued Date
2023-01-01
Resource Type
ISSN
15499596
eISSN
1549960X
Scopus ID
2-s2.0-85186399236
Journal Title
Journal of Chemical Information and Modeling
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Chemical Information and Modeling (2023)
Suggested Citation
Wisanpitayakorn P., Sartyoungkul S., Kurilung A., Sirivatanauksorn Y., Visessanguan W., Sathirapongsasuti N., Khoomrung S. Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion’s Polarizability and Molecular Mass with Limited Data. Journal of Chemical Information and Modeling (2023). doi:10.1021/acs.jcim.3c01491 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97526
Title
Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion’s Polarizability and Molecular Mass with Limited Data
Corresponding Author(s)
Other Contributor(s)
Abstract
The rotationally averaged collision cross-section (CCS) determined by ion mobility-mass spectrometry (IM-MS) facilitates the identification of various biomolecules. Although machine learning (ML) models have recently emerged as a highly accurate approach for predicting CCS values, they rely on large data sets from various instruments, calibrants, and setups, which can introduce additional errors. In this study, we identified and validated that ion’s polarizability and mass-to-charge ratio (m/z) have the most significant predictive power for traveling-wave IM CCS values in relation to other physicochemical properties of ions. Constructed solely based on these two physicochemical properties, our CCS prediction approach demonstrated high accuracy (mean relative error of <3.0%) even when trained with limited data (15 CCS values). Given its ability to excel with limited data, our approach harbors immense potential for constructing a precisely predicted CCS database tailored to each distinct experimental setup. A Python script for CCS prediction using our approach is freely available at https://github.com/MSBSiriraj/SVR_CCSPrediction under the GNU General Public License (GPL) version 3.