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

## TL;DR

This paper introduces a new method for analyzing complex ion mobility data to determine molecular structures and conformations.

## Contribution

A novel Bayesian approach combining ion transport theory and MCMC for automated deconvolution of ion mobility data.

## Key findings

- The method successfully identifies global deconvolution solutions from synthetic and experimental data.
- It enables tracking of cytochrome c conformational ensembles under activation.
- The approach reveals potential for cross-platform annotations of isomeric O-glycan species.

## Abstract

Deconvolution of multicomponent arrival time distributions
is known
to be a highly challenging task due to the “curse of dimensionality”.
Therefore, the development of a robust global optimizer is a crucial
milestone in the automated analysis of arrival time distributions
for collision cross section extraction and population analysis. Here,
we report an approach that combines gas-phase ion transport theory
with equi-energy sampling, Bayesian sequential partitioning, and reversible-jump
Markov chain Monte Carlo to automatically determine probabilities
of deconvolution solutions and predict the number of components in
the arrival time distribution. The robustness of the method was evaluated
against synthetic and experimental drift tube ion mobility data to
find global deconvolution solutions and automatically determine collision
cross sections. Analyzing the collision-induced unfolding profile
of cytochrome c using the developed pipeline ultimately enables tracking
of the conformational ensemble under activation. Additionally, using
the same strategy on isomeric O-glycan species further
revealed the potential for cross-platform collision cross section
annotations.