In Silico Collision Cross Section Calculations to Aid Metabolite Annotation

The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures.

Eliminating the Deadwood: A Machine Learning Model for CCS Knowledge-Based Conformational Focusing for Lipids

Accurate elucidation of gas-phase chemical structures using collision cross section (CCS) values obtained from ion-mobility mass spectrometry benefits from a synergism between experimental and in silico results. We have shown in recent work that for a molecule of modest size with a proscribed conformational space we can successfully capture a conformation(s) that can match experimental CCS values. However, for flexible systems such as fatty acids that have many rotatable bonds and multiple intramolecular London dispersion interactions, it becomes necessary to sample a much greater conformational space. Sampling more conformers, however, accrues significant computational cost downstream in optimization steps involving quantum mechanics. To reduce this computational expense for lipids, we have developed a novel machine learning (ML) model to facilitate conformer filtering according to the estimated gas-phase CCS values. Herein we report that the implementation of our CCS knowledge-based approach for conformational sampling resulted in improved structure prediction agreement with experiment by achieving favorable average CCS prediction errors of ∼2% for lipid systems in both the validation set and the test set. Moreover, most of the gas-phase candidate conformations obtained by using CCS focusing achieved lower energy-minimum geometries than the candidate conformations without focusing. Altogether, the implementation of this ML model into our modeling workflow has proven to be beneficial for both the quality of the results and the turnaround time. Finally, while our approach is limited to lipids, it can be readily extended to other molecules of interest.

In Silico Characterization of Glycan Ions from IM-MS Collision Cross Section

Ion mobility mass spectrometry (IM-MS) can assist in the identification of isobaric chemical analytes by exploiting the difference in their gas phase collision cross-section (CCS) property. In glycomics, reliable glycan characterization remains challenging, even with IM-MS, because of closely related isomeric species and the available binding arrangements of substituted monosaccharides, allowing for the formation of complex structures. Here, we present a computational procedure to obtain gas-phase structural information from the experimental IM-MS CCS data of carbohydrates. The workflow proceeds with high throughput charge modeling of glycan seed structures to determine the precise protonation or deprotonation site. The charge models were then screened by using density functional theory (DFT) to produce candidate charge states for conformation generation. An extensive conformational scoring of the glycan ions was performed quantum mechanically at the DFT D3-B3LYP/6-31G+(d,p) level for the negative mode, [M - H]-, and at the D3-B3LYP/6-31G(d,p) level for the positive mode, [M + H]+. For most of our test set, the computed CCS values from the final geometry optimized structures showed good agreement with experiment. We also demonstrated the capability of characterizing configurational and constitutional isomeric species. Altogether, we believe that the method we used in this work can be used to build a reliable theoretical reference database for glycans that can be used for experimental quality control and for assigning molecular structure to experimental IM-MS CCS information.

Daily social and physical activity increases slow-wave sleep and daytime neuropsychological performance in the elderly

Decreased levels of physical and social activity associated with aging can be particularly pronounced in residents of assisted living facilities. Reduced exposure to important behavioral and time-giving cues may contribute to the age-related changes in circadian rhythmicity and sleep. The present study was conducted to test the hypothesis that an enforced schedule of structured social and physical activity (0:900 to 10:30 and 19:00 to 20:30 daily for two weeks) can have beneficial effects on circadian rhythmicity, nocturnal sleep, daytime functioning, mood, and vigor. The subjects were 14 elderly residents of continued-care retirement facilities while a similar group of 9 elderly residents served as controls. The group exposed to structured activities had increased amounts of slow-wave sleep and demonstrated improvement in memory-oriented tasks following the intervention. Conversely, no significant changes were noted in the amplitude and phase of the body temperature rhythm or in subjective measures of vigor and mood. These results indicate that short-term exposure to structured social intervention and light physical activity can significantly improve memory performance and enhance slow-wave sleep in older adults without alterations to the circadian phase or amplitude of body temperature. This is the first report to demonstrate that low intensity activity in an elderly population can increase deep sleep and improve memory functioning. The high degree of interest in these activities paired with the simple nature of the tasks makes this a potentially practical intervention which can be adapted for both community dwelling and assisted-living elders.