Exploring the Impacts of Conformer Selection Methods on Ion Mobility Collision Cross Section Predictions

The
prediction of structure dependent molecular properties, such
as collision cross sections as measured using ion mobility spectrometry,
are crucially dependent on the selection of the correct population
of molecular conformers. Here, we report an in-depth evaluation of
multiple conformation selection techniques, including simple averaging,
Boltzmann weighting, lowest energy selection, low energy threshold
reductions, and similarity reduction. Generating 50 000 conformers
each for 18 molecules, we used the In Silico Chemical Library Engine
(ISiCLE) to calculate the collision cross sections for the entire
data set. First, we employed Monte Carlo simulations to understand
the variability between conformer structures as generated using simulated
annealing. Then we employed Monte Carlo simulations to the aforementioned
conformer selection techniques applied on the simulated molecular
property: the ion mobility collision cross section. Based on our analyses,
we found Boltzmann weighting to be a good trade-off between precision
and theoretical accuracy. Combining multiple techniques revealed that
energy thresholds and root-mean-squared deviation-based similarity
reductions can save considerable computational expense while maintaining
property prediction accuracy. Molecular dynamic conformer generation
tools like AMBER can continue to generate new lowest energy conformers
even after tens of thousands of generations, decreasing precision
between runs. This reduced precision can be ameliorated and theoretical
accuracy increased by running density functional theory geometry optimization
on carefully selected conformers.