News Release

Measuring and Predicting collision cross section (CCS) values for unknown compounds

Study structures by different modeling techniques using ion mobility spectrometry

Peer-Reviewed Publication

Auburn University College of Sciences and Mathematics

Different ion mobility technologies with representative illustrations of separation mechanisms, applied field, and gas dynamics.

image: Different ion mobility technologies with representative illustrations of separation mechanisms, applied field, and gas dynamics. CCS, collision cross section; DMA, differential mobility analyzer; DTIMS, drift tube ion mobility spectrometry; FAIMS, field asymmetric ion mobility spectrometry; TIMS, trapped ion mobility spectrometry; TWIMS, traveling wave ion mobility spectrometry. view more 

Credit: Journal of Mass Spectrometry

Although ion mobility can separate ions very well, it still has some limitations. Since accurate experimental CCS determination in untargeted analysis usually requires reference CCS from standards. Since it is challenging to find appropriate standards, it can be difficult to obtain accurate experimental CCS values for different chemical classes. Therefore, theoretical CCS determination can help increase the confidence of experimental CCS measurements in untargeted analysis. In recent decades, researchers have developed many methods for predicting CCS values based on machine learning and computer models. One of the first methods was the computational approach, which uses the 3D models of a compound. These approaches can be accurate, but they are also prone to larger errors depending on the structure of the compound of interest. They also require high computational power and typically take up to days to determine CCS values for many compounds in a batch. On the contrary, the machine learning approach is faster in determining the CCS values and requires minimum computational power compared to the computational approach, and it yields an error of less than 10%. The limitations of the machine learning approach are that it depends on the available experimental CCS databases, the nature of the chemical classes, and the resolving power of the experimental CCS. For example, if a CCS database constructed primarily with lipids is used to train the machine learning model, the error for the predicted CCS values of a protein compound will likely be higher. Therefore, acquiring and updating experimental databases with CCS from different chemical classes and higher resolving power (~300 and above) can improve the accuracy and error of predicted CCS values in the future through machine learning.

“By using ion mobility spectrometry (IMS), the Hamid Lab determined CCS values for compounds that will provide a foundation for measurements using portable ion mobility spectrometers in the future,” said Ahmed M. Hamid, assistant professor in the Department of Chemistry and Biochemistry at Auburn University.

The study in the Journal of Mass Spectrometry discusses different types of technologies for the determination of CCS values:

  • Experimental CCS values obtained from different ion mobility platforms (such as Drift Tube Ion Mobility Spectrometry and Traveling Wave Ion Mobility Spectrometry, etc.).
  • Theoretical CCS values via computational and machine learning models.
  • Future outlooks for machine learning and experimental CCS values.

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