-
Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion抯 Polarizability and Molecular Mass with Limited Data
- Back
Document Title
Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion抯 Polarizability and Molecular Mass with Limited Data
Author
Wisanpitayakorn P. Sartyoungkul S. Kurilung A. Sirivatanauksorn Y. Visessanguan W. Sathirapongsasuti N. Khoomrung S.
Affiliations
Siriraj Center of Research Excellence in Metabolomics and Systems Biology (SiCORE-MSB) Faculty of Medicine Siriraj Hospital Mahidol University Bangkok 10700 Thailand; Siriraj Metabolomics and Phenomics Center Faculty of Medicine Siriraj Hospital Mahidol University Bangkok 10700 Thailand; National Center for Genetic Engineering and Biotechnology (BIOTEC) Pathumthani 12120 Thailand; Section of Translational Medicine Faculty of Medicine Ramathibodi Hospital Mahidol University Bangkok 10400 Thailand; Research Network of NANOTEC - MU Ramathibodi on Nanomedicine Bangkok 12120 Thailand; Department of Biochemistry Faculty of Medicine Siriraj Hospital Mahidol University Bangkok 10700 Thailand; Center of Excellence for Innovation in Chemistry (PERCH?CIC) Faculty of Science Mahidol University Bangkok 10400 Thailand
Type
Article
Source Title
Journal of Chemical Information and Modeling
ISSN
15499596
Year
2024
Volume
64
Issue
5
Page
1533-1542
Open Access
All Open Access Hybrid Gold
Publisher
American Chemical Society
DOI
10.1021/acs.jcim.3c01491
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抯 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. ? 2024 The Authors. Published by American Chemical Society.
License
CC BY
Rights
Authors
Publication Source
WOS