CCSbase is a product of the Libin Xu Lab in the Department of Medicinal Chemistry at the University of Washington. CCSbase is an integrated platform consisting of a comprehensive database of Collision Cross Section (CCS) measurements taken from a variety of sources (see references below) and a high-quality and high-throughput CCS prediction model trained with this database using machine learning. The CCS measurements were made on a variety of instruments (both drift tube and traveling wave) using nitrogen as the drift gas and represent broad coverage of chemical structural diversity, such as lipids, water-soluble metabolites, small molecules, drugs, etc. The instrument platform and method for each CCS measurement are specified for each entry for user’s reference.

For commercial users, please contact Dr. Libin Xu and the UW CoMotion team for licensing.

This website and its associated content (the “Website”) are Copyright of Jang Ho Cho, Dylan Ross, and Libin Xu (2019). All rights are reserved. Permission is hereby granted, free of charge, to any person wishing to use the Website, including the CCS database and/or CCS prediction, subject to the following conditions:
1. Any derivative works (e.g. softwares, websites) must reproduce the above copyright notice.
2. Use of the Website must be for academic, non-commercial purposes only.
3. Any published works (e.g. journal articles, blog posts) using or referencing the Website, be they digital or in print, must include the appropriate citation (Ross, D. H., Cho, J. H. & Xu, L. Anal. Chem. (2020). doi:10.1021/acs.analchem.9b05772).

May, J. C. et al. Conformational Ordering of Biomolecules in the Gas Phase: Nitrogen Collision Cross Sections Measured on a Prototype High Resolution Drift Tube Ion Mobility-Mass Spectrometer. Anal. Chem. 86, 2107–2116 (2014).

Paglia, G. et al. Ion Mobility Derived Collision Cross Sections to Support Metabolomics Applications. Anal. Chem. 86, 3985–3993 (2014).

Groessl, M., Graf, S. & Knochenmuss, R. High resolution ion mobility-mass spectrometry for separation and identification of isomeric lipids. Analyst 140, 6904–6911 (2015).

Zhou, Z., Shen, X., Tu, J. & Zhu, Z.-J. Large-Scale Prediction of Collision Cross-Section Values for Metabolites in Ion Mobility-Mass Spectrometry. Anal. Chem. 88, 11084–11091 (2016).

Hines, K. M., Herron, J. & Xu, L. Assessment of altered lipid homeostasis by HILIC-ion mobility-mass spectrometry-based lipidomics. The Journal of Lipid Research 58, 809–819 (2017).

Bijlsma, L. et al. Prediction of Collision Cross-Section Values for Small Molecules: Application to Pesticide Residue Analysis. Anal. Chem. 89, 6583–6589 (2017).

Hines, K. M., Ross, D. H., Davidson, K. L., Bush, M. F. & Xu, L. Large-Scale Structural Characterization of Drug and Drug-Like Compounds by High-Throughput Ion Mobility-Mass Spectrometry. Anal. Chem. 89, 9023–9030 (2017).

Stow, S. M. et al. An Interlaboratory Evaluation of Drift Tube Ion Mobility–Mass Spectrometry Collision Cross Section Measurements. Anal. Chem. 89, 9048–9055 (2017).

Zhou, Z., Tu, J., Xiong, X., Shen, X. & Zhu, Z.-J. LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision To Support Ion Mobility–Mass Spectrometry-Based Lipidomics. Anal. Chem. 89, 9559–9566 (2017).

Zheng, X. et al. A structural examination and collision cross section database for over 500 metabolites and xenobiotics using drift tube ion mobility spectrometry. Chem. Sci. 8, 7724–7736 (2017).

Hines, K. M. et al. Characterization of the Mechanisms of Daptomycin Resistance among Gram-Positive Bacterial Pathogens by Multidimensional Lipidomics. mSphere 2, 99–16 (2017).

Lian, R. et al. Ion mobility derived collision cross section as an additional measure to support the rapid analysis of abused drugs and toxic compounds using electrospray ion mobility time-of-flight mass spectrometry. Anal. Methods 10, 749–756 (2018).

Mollerup, C. B., Mardal, M., Dalsgaard, P. W., Linnet, K. & Barron, L. P. Prediction of collision cross section and retention time for broad scope screening in gradient reversed-phase liquid chromatography-ion mobility-high resolution accurate mass spectrometry. Journal of Chromatography A 1542, 82–88 (2018).

Righetti, L. et al. Ion mobility-derived collision cross section database: Application to mycotoxin analysis. Analytica Chimica Acta 1014, 50–57 (2018).

Tejada-Casado, C. et al. Collision cross section (CCS) as a complementary parameter to characterize human and veterinary drugs. Analytica Chimica Acta 1043, 52–63 (2018).

Nichols, C. M. et al. Untargeted Molecular Discovery in Primary Metabolism: Collision Cross Section as a Molecular Descriptor in Ion Mobility-Mass Spectrometry. Anal. Chem. 90, 14484–14492 (2018).

Hines, K. M. & Xu, L. Lipidomic consequences of phospholipid synthesis defects in Escherichia coli revealed by HILIC-ion mobility-mass spectrometry. Chemistry and Physics of Lipids 219, 15–22 (2019).

Leaptrot, K. L., May, J. C., Dodds, J. N. & McLean, J. A. Ion mobility conformational lipid atlas for high confidence lipidomics. Nature Communications 1–9 (2019).

Blaženović, I. et al. Increasing Compound Identification Rates in Untargeted Lipidomics Research with Liquid Chromatography Drift Time–Ion Mobility Mass Spectrometry. Anal. Chem. 90, 10758–10764 (2018).

Vasilopoulou, C. G. et al. Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts. Nature Communications 1–11 (2020).

Tsugawa, H. et al. MS-DIAL 4: accelerating lipidomics using an MS/MS, CCS, and retention time atlas. bioRxiv 37, 513 (2020).

Poland, J. C. et al. Collision Cross Section Conformational Analyses of Bile Acids via Ion Mobility–Mass Spectrometry. Journal of the American Society for Mass Spectrometry 31, 1625–1631 (2020).

Dodds, J. et al. Rapid Characterization of Per- and Polyfluoroalkyl Substances (PFAS) by Ion Mobility Spectrometry−Mass Spectrometry (IMS-MS). Anal. Chem. 92, 4427-4435 (2020).

Celma, A. et al. Improving Target and Suspect Screening High-Resolution Mass Spectrometry Workflows in Environmental Analysis by Ion Mobility Separation. Environ. Sci. Technol. 54, 15120-15131 (2020)

Experimental Database

V1.3 - Current Version

V1.0 - 7688 Total Entries (February 25, 2020). Initial combined CCS database from Ross et al. Anal. Chem. (2020).

V1.1 - 13704 Total Entries (March 17, 2020). Added two recently published datasets with TIMS CCS values for a large number of lipids in positive and negative modes: Vasilopoulou et al. Nature Comm. 1–11 (2020) and Tsugawa et al. bioRxiv 37, 513 (2020).

V1.2 - 14008 Total Entries (August 20, 2020). Added three datasets covering abused drugs and toxic compounds [Lian, R. et al. Anal. Methods 10, 749–756 (2018)], human and veterinary drugs [Tejada-Casado, C. et al. Anal. Chim. Acta 1043, 52–63 (2018)], and bile acids [Poland, J. C. et al. JASMS 31, 1625–1631 (2020)].

V1.3 - 12577 Total Entries (August 18th, 2021) see relevant commit here

Predicted Database

V1.1 - Current Version

V1.0 - 4823315 Total Entries (August 31, 2020)

V1.1 - 7752366 Total Entries (October 16, 2020)

Prediction Model

V1.2 - Current Version

V1.1 - (January 13, 2021)

V1.2 - (August 18th, 2021) see relevant commit here


V1.2 - Current Version

V1.0 - 101593 Total Entries (April 14th, 2020)

V1.1 - 145387 Total Entries (June 1st, 2020)

V1.2 - 153795 Total Entries (August 18th, 2021), see relevant commit here