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.

SLIM Ultrahigh Resolution Ion Mobility Spectrometry Separations of Isotopologues and Isotopomers Reveal Mobility Shifts due to Mass Distribution Changes.

We report on separations of ion isotopologues and isotopomers using ultrahigh-resolution traveling wave-based Structures for Lossless Ion Manipulations with serpentine ultralong path and extended routing ion mobility spectrometry coupled to mass spectrometry (SLIM SUPER IMS-MS). Mobility separations of ions from the naturally occurring ion isotopic envelopes (e.g., [M], [M+1], [M+2], ... ions) showed the first and second isotopic peaks (i.e., [M+1] and [M+2]) for various tetraalkylammonium ions could be resolved from their respective monoisotopic ion peak ([M]) after SLIM SUPER IMS with resolving powers of ∼400-600. Similar separations were obtained for other compounds (e.g., tetrapeptide ions). Greater separation was obtained using argon versus helium drift gas, as expected from the greater reduced mass contribution to ion mobility described by the Mason-Schamp relationship. To more directly explore the role of isotopic substitutions, we studied a mixture of specific isotopically substituted (15N, 13C, and 2H) protonated arginine isotopologues. While the separations in nitrogen were primarily due to their reduced mass differences, similar to the naturally occurring isotopologues, their separations in helium, where higher resolving powers could also be achieved, revealed distinct additional relative mobility shifts. These shifts appeared correlated, after correction for the reduced mass contribution, with changes in the ion center of mass due to the different locations of heavy atom substitutions. The origin of these apparent mass distribution-induced mobility shifts was then further explored using a mixture of Iodoacetyl Tandem Mass Tag (iodoTMT) isotopomers (i.e., each having the same exact mass, but with different isotopic substitution sites). Again, the observed mobility shifts appeared correlated with changes in the ion center of mass leading to multiple monoisotopic mobilities being observed for some isotopomers (up to a ∼0.04% difference in mobility). These mobility shifts thus appear to reflect details of the ion structure, derived from the changes due to ion rotation impacting collision frequency or momentum transfer, and highlight the potential for new approaches for ion structural characterization.

Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics.

Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called "reference-free metabolomics" methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3-7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution.

Correction: Nielson et al. Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics. Metabolites 2023, 13, 105.

There were missing figures and associated legends for Figure 3 and Figure 4 as published due to a publication error [...].