Collision Cross Section Prediction Based on Machine Learning

Recent Advances in Nontargeted Screening of Chemical Hazards in Foodstuffs

The emergence of several chemical substances continues to enrich and facilitate the development of food science, but their irrational use also poses a threat to food safety and human health. Nontargeted screening (NTS) has become an important tool for rapid traceability and efficient identification of chemical hazards in food matrices. NTS in food analysis is highly integrated with sample pretreatment, instrumental analysis platforms, data acquisition and analysis, and toxicology. This article is a systemic review of current sample preparation, analytical platforms, and toxicity-guided NTS techniques and provides the latest advancements in workflows and innovative applications of the NTS process based on mass spectrometric techniques. High-throughput toxicity screening platforms play an important role in NTS of unknown chemical hazards of complex food matrices. Advanced machine learning and artificial intelligence are increasingly accessible fields that may effectively process large-scale screening data and advance food NTS research.

Evaluating the generalizability of graph neural networks for predicting collision cross section

Ion Mobility coupled with Mass Spectrometry (IM-MS) is a promising analytical technique that enhances molecular characterization by measuring collision cross-section (CCS) values, which are indicative of the molecular size and shape. However, the effective application of CCS values in structural analysis is still constrained by the limited availability of experimental data, necessitating the development of accurate machine learning (ML) models for in silico predictions. In this study, we evaluated state-of-the-art Graph Neural Networks (GNNs), trained to predict CCS values using the largest publicly available dataset to date. Although our results confirm the high accuracy of these models within chemical spaces similar to their training environments, their performance significantly declines when applied to structurally novel regions. This discrepancy raises concerns about the reliability of in silico CCS predictions and underscores the need for releasing further publicly available CCS datasets. To mitigate this, we introduce Mol2CCS which demonstrates how generalization can be partially improved by extending models to account for additional features such as molecular fingerprints, descriptors, and the molecule types. Lastly, we also show how confidence models can support by enhancing the reliability of the CCS estimates.Scientific contributionWe have benchmarked state-of-the-art graph neural networks for predicting collision cross section. Our work highlights the accuracy of these models when trained and predicted in similar chemical spaces, but also how their accuracy drops when evaluated in structurally novel regions. Lastly, we conclude by presenting potential approaches to mitigate this issue.

Evaluation and comparison of liquid chromatography/high-resolution mass spectrometry platforms for the separation and characterization of ginsenosides from the leaves of Panax ginseng

The measurement of data repeatability in small-molecule metabolites acquired within and among different liquid chromatography-mass spectrometry (LC-MS) platforms is crucial for data sharing or data transfer in natural products research. This work was designed to investigate and evaluate the separation and detection performance of three commercial high-resolution LC-MS platforms (e.g., Agilent 6550 QTOF, Waters Vion IM-QTOF, and Thermo Scientific Orbitrap Exploris 120) using 68 ginsenoside references and the extract of Panax ginseng leaf. The retention time (tR), measured on these three platforms (under the same chromatography condition), showed good stability in different concentration tests, and within/among different instruments for both intra-day and inter-day precision examinations. Correlation in tR of ginsenosides was also highly determined on these three platforms. In spite of the different mass analyzers involved, these three platforms gave the accurate mass determination ability, especially enhanced resolution gained because of the ion mobility (IM) separation facilitated by IM-quadrupole time-of-flight. The current study has systematically evaluated the separation and MS detection performance enabled by three high-resolution LC-MS platforms taking ginsenosides as the template, and the reported findings can benefit the researchers for the selection of analytical platforms and the purpose of data sharing or data transfer.

Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules

The application of machine learning (ML) to -omics research is growing at an exponential rate owing to the increasing availability of large amounts of data for model training. Specifically, in metabolomics, ML has enabled the prediction of tandem mass spectrometry and retention time data. More recently, due to the advent of ion mobility, new ML models have been introduced for collision cross-section (CCS) prediction, but those have been trained with different and relatively small data sets covering a few thousands of small molecules, which hampers their systematic comparison. Here, we compared four existing ML-based CCS prediction models and their capacity to predict CCS values using the recently introduced METLIN-CCS data set. We also compared them with simple linear models and with ML models that used fingerprints as regressors. We analyzed the role of structural diversity of the data on which the ML models are trained with and explored the practical application of these models for metabolite annotation using CCS values. Results showed a limited capability of the existing models to achieve the necessary accuracy to be adopted for routine metabolomics analysis. We showed that for a particular molecule, this accuracy could only be improved when models were trained with a large number of structurally similar counterparts. Therefore, we suggest that current annotation capabilities will only be significantly altered with models trained with heterogeneous data sets composed of large homogeneous hubs of structurally similar molecules to those being predicted.

Computational tools and algorithms for ion mobility spectrometry-mass spectrometry

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.

Ion mobility calculations of flexible all-atom systems at arbitrary fields using two-temperature theory

Ion mobility spectrometry (IMS) separates and analyzes ions based on their mobility in a gas under an electric field. When the field is increased, the mobility varies in a complex way that depends on the relative velocity between gas and ion, their electrostatic potential interactions, and the effects from direct impingement. Recently, the two-temperature theory, primarily developed for monoatomic ions in monoatomic gases, has been extended to study mobilities at arbitrary fields using polyatomic ions in polyatomic gases, with some success. However, this extension poses challenges, such as inelastic collisions between gas and ion and structural modifications of ions as they heat up. These challenges become significant when working with diatomic gases and flexible molecules. In a previous study, experimental mobilities of tetraalkylammonium salts were obtained using a FAIMS instrument, showing satisfactory agreement with numerical two-temperature theory predictions. However, deviations occurred at fields greater than 100 Td. To address this issue, this paper introduces a modified high-field calculation method that accounts for the structural changes in ions due to field heating. The study focuses on tetraheptylammonium (THA+), tetradecylammonium (TDA+), and tetradodecylammonium (TDDA+) salts. Molecular structures were generated at various temperatures using MM2 forcefield. The mobility was calculated using IMoS 1.13 with two-temperature trajectory method calculations up to the fourth approximation. Multiple effective temperatures were considered, and a linear weighing system was used to create mobility vs. reduced field strength plots. The results suggest that the structural enlargement due to ion heating plays a significant role in mobility at high fields, aligning better with experimental data. FAIMS' dispersion plots also show improved agreement with experimental results. However, the contribution of inelastic collisions and energy transfer to rotational degrees of freedom in gas molecules remains a complex and challenging aspect.

Global Profiling of the Antioxidant Constituents in Chebulae Fructus Based on an Integrative Strategy of UHPLC/IM-QTOF-MS, MS/MS Molecular Networking, and Spectrum-Effect Correlation

An integrative strategy of UHPLC/IM-QTOF-MS analysis, MS/MS molecular networking (MN), in-house library search, and a collision cross-section (CCS) simulation and comparison was developed for the rapid characterization of the chemical constituents in Chebulae Fructus (CF). A total of 122 Constituents were identified, and most were phenolcarboxylic and tannic compounds. Subsequently, 1,3,6-tri-O-galloyl-β-d-glucose, terflavin A, 1,2,6-tri-O-galloyl-β-d-glucose, punicalagin B, chebulinic acid, chebulagic acid, 1,2,3,4,6-penta-O-galloyl-β-d-glucose, and chebulic acid, among the 23 common constituents of CF, were screened out by UPLC-PDA fingerprinting and multivariate statistical analyses (HCA, PCA, and OPLS-DA). Then, Pearson's correlation analysis and a grey relational analysis were performed for the spectrum-effect correlation between the UPLC fingerprints and the antioxidant capacity of CF, which was finally validated by an UPLC-DPPH• analysis for the main antioxidant constituents. Our study provides a global identification of CF constituents and contributes to the quality control and development of functional foods and preparations dedicated to CF.

Resolving D-Amino Acid Containing Peptides Using Ion Mobility-Mass Spectrometry: Challenges and Recent Developments

Peptides and proteins having D-amino acids in their sequences are now believed to be widespread among different living organisms. Their significance is attributed to the diverse functions of these molecules, such as having a certain pathological implication or enhancing biological activity. Indeed, some peptide molecules with D-amino acids in their structure have already found their way to clinical use such as the antibacterial gramicidin and the antidiabetic nateglinide. Ion mobility mass spectrometry (IM-MS) added an additional dimension of separation as it depends on ions mobility in the space, which is dependent on their shapes, and the shape depends on the orientation of atoms. Thus, D-amino acids containing peptides (DAACPs) will have different mobility and collision cross-section values than those with L-amino acids. Eventually, this will lead to baseline separation of the two peptides. Additionally, ion mobility can precisely locate the position of D-amino acids by analyzing the difference in the arrival times of the fragment ions. The importance of DAACPs, as well as the difficulties in discovering them, were addressed in this review. Similarly, we emphasized how recent developments in IM-MS have improved their detection and analysis. Consequently, the LC-IM-MS/MS platform appears to be promising in isomeric mixture analysis.

A practical strategy enabling more reliable identification of ginsenosides from Panax quinquefolius flower by dimension-enhanced liquid chromatography/mass spectrometry and quantitative structure-retention relationship-based retention behavior prediction

To accurately identify the metabolites is crucial in a number of research fields, and discovery of new compounds from the natural products can benefit the development of new drugs. However, the preferable phytochemistry or liquid chromatography/mass spectrometry approach is time-/labor-extensive or receives unconvincing identifications. Herein, we presented a strategy, by integrating offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), exclusion list-containing high-definition data-dependent acquisition (HDDDA-EL), and quantitative structure-retention relationship (QSRR) prediction of the retention time (tR), to facilitate the in-depth and more reliable identification of herbal components and thus to discover new compounds more efficiently. Using the saponins in Panax quinquefolius flower (PQF) as a case, high orthogonality (0.79) in separating ginsenosides was enabled by configuring the XBridge Amide and CSH C18 columns. HDDDA-EL could improve the coverage in MS2 acquisition by 2.26 folds compared with HDDDA (2933 VS 1298). Utilizing 106 reference compounds, an accurate QSRR prediction model (R2 = 0.9985 for the training set and R2 = 0.88 for the validation set) was developed based on Gradient Boosting Machine (GBM), by which the predicted tR matching could significantly reduce the isomeric candidates identification for unknown ginsenosides. Isolation and establishment of the structures of two malonylginsenosides by NMR partially verified the practicability of the integral strategy. By these efforts, 421 ginsenosides were identified or tentatively characterized, and 284 thereof were not ever reported from the Panax species. The current strategy is thus powerful in the comprehensive metabolites characterization and rapid discovery of new compounds from the natural products.

Collision cross section measurement and prediction methods in omics

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.