Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH- and CCSNa+) of emerging contaminants using Multiple Adaptive Regression Splines

Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or non-target approaches (i.e. when no reference standards are available) there is no information on retention time (RT) and collision cross section (CCS) values to facilitate identification. In-silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) was evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the empirical data. The RT model (R2=0.855) showed a deviation between predicted and empirical data of ± 2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using CCSH model (R2=0.966) was ± 4.05% with 95% confidence intervals. The CCSH model was also tested for the prediction of deprotonated molecules resulting in deviations below ± 5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCSNa, R2=0.954) with deviation below ± 5.25% for the 95% of the cases. The developed models have been incorporated in an open access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry

Most frequently, the identification of peptides in mass spectrometry-based proteomics is carried out using high-resolution tandem mass spectrometry. In order to increase the accuracy of analysis, additional information on the peptides such as chromatographic retention time and collision cross section in ion mobility spectrometry can be used. An accurate prediction of the collision cross section values allows erroneous candidates to be rejected using a comparison of the observed values and the predictions based on the amino acids sequence. Recently, a massive high-quality data set of peptide collision cross sections was released. This opens up an opportunity to apply the most sophisticated deep learning techniques for this task. Previously, it was shown that a recurrent neural network allows for predicting these values accurately. In this work, we present a deep convolutional neural network that enables us to predict these values more accurately compared with previous studies. We use a neural network with complex architecture that contains both convolutional and fully connected layers and comprehensive methods of converting a peptide to multi-channel 1D spatial data and vector. The source code and pre-trained model are available online.

Large-Scale Evaluation of Collision Cross Sections to Investigate Blood-Brain Barrier Permeation of Drugs

Successful drug administration to the central nervous system requires accurate adjustment of the drugs’ molecular properties. Therefore, structure-derived descriptors of potential brain therapeutic agents are essential for an early evaluation of pharmacokinetics during drug development. The collision cross section (CCS) of molecules was recently introduced as a novel measurable parameter to describe blood-brain barrier (BBB) permeation. This descriptor combines molecular information about mass, structure, volume, branching and flexibility. As these chemical properties are known to influence cerebral pharmacokinetics, CCS determination of new drug candidates may provide important additional spatial information to support existing models of BBB penetration of drugs. Besides measuring CCS, calculation is also possible; but however, the reliability of computed CCS values for an evaluation of BBB permeation has not yet been fully investigated. In this work, prediction tools based on machine learning were used to compute CCS values of a large number of compounds listed in drug libraries as negative or positive with respect to brain penetration (BBB+ and BBB− compounds). Statistical evaluation of computed CCS and several other descriptors could prove the high value of CCS. Further, CCS-deduced maximum molecular size of BBB+ drugs matched the dimensions of BBB pores. A threshold for transcellular penetration and possible permeation through pore-like openings of cellular tight-junctions is suggested. In sum, CCS evaluation with modern in silico tools shows high potential for its use in the drug development process.

Uncovering Xenobiotics in the Dark Metabolome using Ion Mobility Spectrometry, Mass Defect Analysis and Machine Learning

The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, excretion, and co-existence with other endogenous molecules however greatly complicate nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites and xenobiotics is often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate the use of ion mobility spectrometry coupled with MS (IMS-MS) and mass defect filtering in a xenobiotic structural annotation workflow to reduce large metabolomic feature lists and uncover potential xenobiotic classes and species detected in the metabolomic studies. To evaluate the workflow, xenobiotics having known high toxicities including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) were examined. Initially, to address the lack of available IMS collision cross section (CCS) values for per- and polyfluoroalkyl substances (PFAS), 88 PFAS standards were evaluated with IMS-MS to both develop a targeted PFAS CCS library and for use in machine learning predictions. The CCS values for biomolecules and xenobiotics were then plotted versus m/z, clearly distinguishing the biomolecules and halogenated xenobiotics. The xenobiotic structural annotation workflow was then used to annotate potential PFAS features in NIST human serum. The workflow reduced the 2,423 detected LC-IMS-MS features to 80 possible PFAS with 17 confidently identified through targeted analyses and 48 additional features correlating with possible CompTox entries.

Cold Denaturation of Proteins in the Absence of Solvent

The effect of temperature on the stability of proteins is well explored for high temperatures, but harder to track below the freezing point of water. This challenge is met with the use of variable temperature ion mobility mass spectrometry (VT IM-MS), which allows the structure of isolated, solvent free molecules to be measured at sub ambient temperatures in the form of their collision cross section (CCS). Here we monitor conformational changes that occur to two isotypes of monoclonal antibodies over a temperature range from 295 to 165 K. For each we observe a large increase in the magnitude of the CCS at 250K (-20 °C) substantially above that predicted. This loss of structure in the absence of bulk solvent is attributed to a change in the strength of stabilizing intermolecular interactions, causing rearrangement. At 190 K (-80 °C) the CCS distribution narrows which we attribute to better resolution. These findings indicate that in vacuo deep-freezing minimizes denaturation and maintains the gas phase native fold supporting this practice in vitro. Comparing the data for each isotype suggests that the disulfide bridging influences thermal structural rearrangement and taken together we show that this method provides unique insights to the phenomenon of cold denaturation.

Paper Spray Ionisation Ion Mobility Mass Spectrometry of Sebum Classifies Biomarker Classes for the Diagnosis of Parkinson’s Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disorder and identification of robust biomarkers to complement clinical diagnosis will accelerate treatment options. Here we demonstrate the use of direct infusion of sebum from skin swabs using paper spray ionisation coupled with ion mobility mass spectrometry (PS-IM-MS) to determine the regulation of molecular classes of lipids in sebum that are diagnostic of PD. A PS-IM-MS method for sebum samples that takes three minutes per swab was developed and optimised. The method was applied to skin swabs collected from 150 people and elucidates ~ 4200 features from each subject which were independently analysed. The data included high molecular weight lipids (>600 Da.) that differ significantly in the sebum of people with PD. Putative metabolite annotations of several lipid classes, predominantly triglycerides and larger acyl glycerides, were obtained using accurate mass, tandem mass spectrometry and collision cross section measurements.

‘O+ + N2’ ÇARPIŞMASINA AİT REAKSİYON PARAMETRELERİNİN GÜNEŞ TUTULMASINA TEPKİSİ

The ionosphere is the part of the earth's atmosphere that extends from about 75 km to 1000 km. The sun is the main factor in shaping and dividing the ionosphere according to electron density. Therefore, the investigation and examination of solar-induced events is of great importance in understanding the structure of the ionosphere. The solar eclipses are one of the most important solar-induced events that cause sudden and medium-scale changes on the ionosphere. The solar eclipse of 29 March 2006 was observed as a total eclipse in some places and as a partial eclipse in some places. One of the regions recorded as a partial eclipse is Kharkov/Ukraine. In this study, the variations of reaction dynamics such as rate constant and cross section of the ‘O+ + N2’ collision, which is one of the ionosphere reaction processes, were investigated according to altitude (187 km) and local time during the 29 March 2006 solar eclipse over the city of Kharkov. The findings revealed that the changes in the reaction rate constant were similar to the changes in temperature, but the same similarities were not observed in the collision cross-section changes. According to another result obtained from this study, it has been determined that the use of experimental-based sources such as Kharkov incoherent scatter radar will give more effective results in investigating the effects of solar eclipses on the ionosphere.

Differentiation of Polyamide 6, 6.6, and 12 Contaminations in Polyolefin-Recyclates Using HPLC Coupled to Drift-Tube Ion- Mobility Quadrupole Time-of-Flight Mass Spectrometry

Recycling is a current hot topic with a focus especially on plastics. The quality of such plastic recyclates is of utmost importance for further processing because impurities lead to a reduction thereof. Contaminations originating from other polymers are highly problematic due to their immiscibility with the recyclate, leading to possible product failures. Therefore, methods for the determination of polymer impurities in recyclates should be investigated. In this paper, an approach for the identification of three different polyamide grades (polyamide 6, 6.6, and 12) is presented, applicable for the analysis of polyolefin-recyclates. An HPLC equipped with a drift-tube ion-mobility QTOF-MS was used for the identification and differentiation of compounds originating from the polyamides, which were then used as markers. These marker compounds are specific for each type and can be identified by their corresponding value of the collision cross section (CCS). After a simple sample preparation, all three types of polyamides were identified within one measurement. In particular, the problematic differentiation of polyamide 6 and 6.6 was easily made possible.