Deconvolution of High-Dimensional Ion Mobility Data Using Reversible-Jump Markov Chain Monte Carlo

Area of Science:

• Analytical Chemistry
• Computational Chemistry
• Biophysical Chemistry

Background:

• Deconvoluting multicomponent arrival time distributions is challenging due to high dimensionality.
• Robust global optimization is essential for automated analysis of arrival time distributions in collision cross section extraction and population analysis.

Purpose of the Study:

• To develop a robust global optimizer for automated analysis of arrival time distributions.
• To combine gas-phase ion transport theory with advanced computational techniques for deconvolution.
• To enable accurate collision cross section extraction and population analysis.

Main Methods:

• Integration of gas-phase ion transport theory with equi-energy sampling.
• Application of Bayesian sequential partitioning and reversible-jump Markov chain Monte Carlo (RJMCMC).
• Validation using synthetic and experimental drift tube ion mobility data.

Main Results:

• Successful automatic determination of deconvolution solution probabilities and component numbers.
• Accurate global deconvolution solutions and collision cross section determination.
• Enabled tracking of conformational ensembles for cytochrome c and cross-platform annotation for glycan species.

Conclusions:

• The developed method provides a robust solution for deconvoluting complex arrival time distributions.
• This approach advances automated analysis in ion mobility spectrometry, impacting biomolecular conformational studies and glycomics.
• The strategy holds potential for standardized cross-platform collision cross section annotations.