Recently, ion mobility - mass spectrometry (IM-MS) showed great potentials for lipidomics. The addition of ion mobility separation into lipidomics significantly improves the selectivity. The use of collision cross-section (CCS) values derived from IM-MS effectively increases the confidence of lipid identification, but this workflow suffers from the limit number of available lipid CCS values. To facilitate the use of CCS values for lipidomics, we developed this LipidCCS web server. Three major functions were designed: (1) prediction of lipid CCS values; (2) LipidCCS database search; and (3) lipid match and identification.
The key innovation is the development of LipidCCS Predictor for the precise prediction of the lipid CCS values. One can simply input the SMAILES structure of the lipid to LipidCCS Predictor, and the program automatically calculates CCS values. Similar to MetCCS, LipidCCS Predictor also employs a support vector regression (SVR) algorithm for prediction. A novel aspect of LipidCCS Predictor compared to MetCCS is the selection and optimization of molecular descriptors to build the prediction model. The prediction precision was externally validated as high as 1% median relative deviation (MRE) for both Agilent and Waters IM-MS systems. This work has been published on Analytical Chemistry (2017).
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Z. Zhou, J. Tu, X. Xin, X. Shen, and Z.-J. Zhu*(Corresponding author), LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision to Support Ion Mobility-Mass Spectrometry based Lipidomics, Analytical Chemistry, 2017, 89, 9559–9566. [link]