In-Electrospray source Hydrogen/Deuterium exchange coupled to multistage fragmentation for the investigation of the protonation and fragmentation pathways of gas phase ions.

Identification of molecules in complex natural matrices relies on matching the fragmentation spectra of ions under investigation and the spectra acquired for the corresponding analytical standards. Currently, there are many databases of experimentally measured tandem mass spectrometry spectra (such as NIST, MzCloud, and Metlin), and considerable progress has been made in the development of software for predicting tandem mass spectrometry fragments in silico using combinatorial, machine learning, and quantum chemistry approaches (such as MetFrag, CFM-ID, and QCxMS). However, the electrospray ionization molecules can be ionized at different sites (protonated or deprotonated), and the fragmentation spectra of such ions are different. Here, we are using the combination of the in-ESI source hydrogen/deuterium exchange reaction and MSn fragmentation for the investigation of the fragmentation pathways for different protomers of organic molecules. It is shown that the distribution of the deuterium in the fragment ions reflects the presence of different protomers. For several molecules, the distribution of deuterium was traced up to the MS5 level of fragmentation revealing many unusual and unexpected effects. For example, we investigated the loss of HF from the ciprofloxacin and norfloxacin ions and observed that for ions protonated at -COOH group, the eliminating hydrogen always comes from -NH group. When ions are protonated at another site, the elimination of hydrogen with a probability of 30% occurs from the -NH group, and with a probability of 70%, it originates from other sites on the molecule. Such effects were not described previously. Quantum chemical simulation was used for the verification of the protonated structures and simulation of the corresponding fragmentation spectra.

Investigating the Metabolism of Plants Germinated in Heavy Water, D2O, and H218O-Enriched Media Using High-Resolution Mass Spectrometry.

Mass spectrometry has been an essential technique for the investigation of the metabolic pathways of living organisms since its appearance at the beginning of the 20th century. Due to its capability to resolve isotopically labeled species, it can be applied together with stable isotope tracers to reveal the transformation of particular biologically relevant molecules. However, low-resolution techniques, which were used for decades, had limited capabilities for untargeted metabolomics, especially when a large number of compounds are labelled simultaneously. Such untargeted studies may provide new information about metabolism and can be performed with high-resolution mass spectrometry. Here, we demonstrate the capabilities of high-resolution mass spectrometry to obtain insights on the metabolism of a model plant, Lepidium sativum, germinated in D2O and H218O-enriched media. In particular, we demonstrated that in vivo labeling with heavy water helps to identify if a compound is being synthesized at a particular stage of germination or if it originates from seed content, and tandem mass spectrometry allows us to highlight the substructures with incorporated isotope labels. Additionally, we found in vivo labeling useful to distinguish between isomeric compounds with identical fragmentation patterns due to the differences in their formation rates that can be compared by the extent of heavy atom incorporation.

Untargeted Lipidomics after D2O Administration Reveals the Turnover Rate of Individual Lipids in Various Organs of Living Organisms.

The administration of low doses of D2O to living organisms was used for decades for the investigation of metabolic pathways and for the measurement of the turnover rate for specific compounds. Usually, the investigation of the deuterium uptake in lipids is performed by measuring the deuteration level of the palmitic acid residue using GC-MS instruments, and to our knowledge, the application of the modern untargeted LC-MS/MS lipidomics approaches was only reported a few times. Here, we investigated the deuterium uptake for >500 lipids for 13 organs and body liquids of mice (brain, lung, heart, liver, kidney, spleen, plasma, urine, etc.) after 4 days of 100% D2O administration. The maximum deuteration level was observed in the liver, plasma, and lung, while in the brain and heart, the deuteration level was lower. Using MS/MS, we demonstrated the incorporation of deuterium in palmitic and stearic fragments in lipids (PC, PE, TAG, PG, etc.) but not in the corresponding free forms. Our results were analyzed based on the metabolic pathways of lipids.

Simple In Vitro 18O Labeling for Improved Mass Spectrometry-Based Drug Metabolites Identification: Deep Drug Metabolism Study.

The identification of drug metabolites formed with different in vitro systems by HPLC-MS is a standard step in preclinical research. In vitro systems allow modeling of real metabolic pathways of a drug candidate. Despite the emergence of various software and databases, identification of compounds is still a complex task. Measurement of the accurate mass, correlation of chromatographic retention times and fragmentation spectra are often insufficient for identification of compounds especially in the absence of reference materials. Metabolites can "slip under the nose", since it is often not possible to reliably confirm that a signal belongs to a metabolite and not to other compounds in complex systems. Isotope labeling has proved to be a tool that aids in small molecule identification. The introduction of heavy isotopes is done with isotope exchange reactions or with complicated synthetic schemes. Here, we present an approach based on the biocatalytic insertion of oxygen-18 isotope under the action of liver microsomes enzymes in the presence of 18O2. Using the local anesthetic bupivacaine as an example, more than 20 previously unknown metabolites were reliably discovered and annotated in the absence of the reference materials. In combination with high-resolution mass spectrometry and modern methods of mass spectrometric metabolism data processing, we demonstrated the ability of the proposed approach to increase the degree of confidence in interpretating metabolism data.

Biphenyl scaffold for the design of NMDA-receptor negative modulators: molecular modeling, synthesis, and biological activity.

NMDA (N-methyl-d-aspartate) receptor antagonists are promising tools for the treatment of a wide variety of central nervous system impairments including major depressive disorder. We present here the activity optimization process of a biphenyl-based NMDA negative allosteric modulator (NAM) guided by free energy calculations, which led to a 100 times activity improvement (IC50 = 50 nM) compared to a hit compound identified in virtual screening. Preliminary calculation results suggest a low affinity for the human ether-a-go-go-related gene ion channel (hERG), a high affinity for which was earlier one of the main obstacles for the development of first-generation NMDA-receptor negative allosteric modulators. The docking study and the molecular dynamics calculations suggest a completely different binding mode (ifenprodil-like) compared to another biaryl-based NMDA NAM EVT-101.

Amine additives for improved in-ESI H/D exchange.

In-ESI H/D exchange is a convenient technique for analyzing small-molecular complex mixtures. However, such experiments do not yield sufficient levels of exchange or require an elevated temperature of the ion transfer capillary. Increased temperature may result in unexpected additional exchanges of -CH groups that may complicate the interpretation of the H/D exchange data used for identification. Gas-phase H/D exchange depends on the gas-phase basicity of the deuterating agent. In-ESI exchange involves both droplet-phase and gas-phase mechanisms, depending on a particular ion source setup and the deuterating agent used. Therefore, the addition of strong bases to the reaction mixture should facilitate in-ESI exchange. This work aimed to investigate the capabilities of different amines to improve in-ESI H/D exchange compared with pure D2O and to choose an amine modifier to increase the extent of H/D exchange. It was shown that such additives substantially enhanced the extent of H/D exchange in small molecules, peptides, and proteins even without heating the capillary. It was found that the extent of exchange increases in the following order: tertiary amines < secondary amines < primary amines. Therefore, we suggest that amines act as deuterating agents after being exchanged with D2O. These findings may improve H/D exchange applications, especially in small molecule analysis. The observation of improved H/D exchange with amine additives in peptides and proteins may become a subject of future research.

Analysis of 16O/18O and H/D Exchange Reactions between Carbohydrates and Heavy Water Using High-Resolution Mass Spectrometry.

Mono- and polysaccharides are an essential part of every biological system. Identifying underivatized carbohydrates using mass spectrometry is still a challenge because carbohydrates have a low capacity for ionization. Normally, the intensities of protonated carbohydrates are relatively low, and in order to increase the corresponding peak height, researchers add Na+, K+, or NH4+to the solution. However, the fragmentation spectra of the corresponding ions are very poor. Based on this, reliably identifying carbohydrates in complex natural and biological objects can benefit frommeasuring additional molecular descriptors, especially those directly connected to the molecular structure. Previously, we reported that the application of the isotope exchange approach (H/D and 16O/18O) to high-resolution mass spectrometry can increase the reliability of identifying drug-like compounds. Carbohydrates possess many -OH and -COOH groups, making it reasonable to expect that the isotope exchange approach would have considerable potential for detecting carbohydrates. Here, we used a collection of standard carbohydrates to investigate the isotope exchange reaction (H/D and 16O/18O) in carbohydrates and estimate its analytical applications.

PyFragMS-A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways.

Dissociation induced by the accumulation of internal energy via collisions of ions with neutral molecules is one of the most important fragmentation techniques in mass spectrometry (MS), and the identification of small singly charged molecules is based mainly on the consideration of the fragmentation spectrum. Many research studies have been dedicated to the creation of databases of experimentally measured tandem mass spectrometry (MS/MS) spectra (such as MzCloud, Metlin, etc.) and developing software for predicting MS/MS fragments in silico from the molecular structure (such as MetFrag, CFM-ID, CSI:FingerID, etc.). However, the fragmentation mechanisms and pathways are still not fully understood. One of the limiting obstacles is that protomers (positive ions protonated at different sites) produce different fragmentation spectra, and these spectra overlap in the case of the presence of different protomers. Here, we are proposing to use a combination of two powerful approaches: computing fragmentation trees that carry information of all consecutive fragmentations and consideration of the MS/MS data of isotopically labeled compounds. We have created PyFragMS-a web tool consisting of a database of annotated MS/MS spectra of isotopically labeled molecules (after H/D and/or 16O/18O exchange) and a collection of instruments for computing fragmentation trees for an arbitrary molecule. Using PyFragMS, we investigated how the site of protonation influences the fragmentation pathway for small molecules. Also, PyFragMS offers capabilities for performing database search when MS/MS data of the isotopically labeled compounds are taken into account.

Increasing the reliability of compound identification in biological samples using 16O/18O-exchange mass spectrometry.

The task of multipurpose analysis of biological samples and identification of individual compounds in them is actual for many organizations in various fields; the results of such analyses can affect lives. The most frequently used, most accurate, and highly sensitive method used for this kind of analysis is the combination of gas/liquid chromatography and high-resolution mass spectrometry. However, in some areas, it is necessary to increase the reliability of compound identification. In this paper, we present a method that combines the reaction of oxygen isotope exchange with mass spectrometry; the method allows to increase the reliability of identification of individual compounds. Oxygen isotope exchange reaction is a "selective" one, which means that not all oxygen present in the molecule can exchange, but only in certain functional groups. Thus, by the number of isotope exchanges that have occurred in this molecule, the right structural formula might be more accurately chosen. The method was tested both on pure pharmaceutical substances and on real human urine samples. In both cases, the effectiveness of the method was shown: the number of expected exchanges in known substances coincided with the experimental one, and from several possible structures of unknown substances, the correct one was chosen based on the number of isotope exchanges.

Oxygen Isotope Exchange Reaction for Untargeted LC-MS Analysis.

LC-MS is a key technique for the identification of small molecules in complex samples. Accurate mass, retention time, and fragmentation spectra from LC-MS experiments are compared to reference values for pure chemical standards. However, this information is often unavailable or insufficient, leading to an assignment to a list of candidates instead of a single hit; therefore, additional features are desired to filter candidates. One such promising feature is the number of specific functional groups of a molecule that can be counted via derivatization or isotope-exchange techniques. Hydrogen/deuterium exchange (HDX) is the most widespread implementation of isotope exchange for mass spectrometry, while oxygen 16O/18O exchange is not applied as frequently as HDX. Nevertheless, it is known that some functional groups may be selectively exchanged in 18O enriched media. Here, we propose an implementation of 16O/18O isotope exchange to highlight various functional groups. We evaluated the possibility of using the number of exchanged oxygen atoms as a descriptor to filter database candidates in untargeted LC-MS-based workflows. It was shown that 16O/18O exchange provides 62% (median, n = 45) search space reduction for a panel of drug molecules. Additionally, it was demonstrated that studying the fragmentation spectra after 16O/18O can aid in eliminating false positives and, in some cases, help to annotate fragments formed with water traces in the collisional cell.

Transfer learning for small molecule retention predictions.

Small molecule retention time prediction is a sophisticated task because of the wide variety of separation techniques resulting in fragmented data available for training machine learning models. Predictions are typically made with traditional machine learning methods such as support vector machine, random forest, or gradient boosting. Another approach is to use large data sets for training with a consequent projection of predictions. Here we evaluate the applicability of transfer learning for small molecule retention prediction as a new approach to deal with small retention data sets. Transfer learning is a state-of-the-art technique for natural language processing (NLP) tasks. We propose using text-based molecular representations (SMILES) widely used in cheminformatics for NLP-like modeling on molecules. We suggest using self-supervised pre-training to capture relevant features from a large corpus of one million molecules followed by fine-tuning on task-specific data. Mean absolute error (MAE) of predictions was in range of 88-248 s for tested reversed-phase data sets and 66 s for HILIC data set, which is comparable with MAE reported for traditional machine learning models based on descriptors or projection approaches on the same data.

Detecting cooking state of grilled chicken by electronic nose and computer vision techniques.

Determination of food doneness remains a challenge for automation in the cooking industry. The complex physicochemical processes that occur during cooking require a combination of several methods for their control. Herein, we utilized an electronic nose and computer vision to check the cooking state of grilled chicken. Thermogravimetry, differential mobility analysis, and mass spectrometry were employed to deepen the fundamental insights towards the grilling process. The results indicated that an electronic nose could distinguish the odor profile of the grilled chicken, whereas computer vision could identify discoloration of the chicken. The integration of these two methods yields greater selectivity towards the qualitative determination of chicken doneness. The odor profile is matched with detected water loss, and the release of aromatic and sulfur-containing compounds during cooking. This work demonstrates the practicability of the developed technique, which we compared with a sensory evaluation, for better deconvolution of food state during cooking.

Machine learning to predict retention time of small molecules in nano-HPLC.

Retention time is an important parameter for identification in untargeted LC-MS screening. Precise retention time prediction facilitates the annotation process and is well known for proteomics. However, the lack of available experimental information for a long time has limited the prediction accuracy for small molecules. Recently introduced large databases for small-molecule retention times make possible reliable machine learning-based predictions for the whole diversity of compounds. Applying simple projections may expand these predictions on various LC systems and conditions. In our work, we describe a complex approach to predict retention times for nano-HPLC that includes the consequent deployment of binary and regression gradient boosting models trained on the METLIN small-molecule dataset and simple projection of the results with a small number of easily available compounds onto nano-HPLC separations. The proposed model outperforms previous attempts to use machine learning for predictions with a 46-s mean absolute error. The overall performance after transfer to nano-LC conditions is less than 155 s (10.8%) in terms of the median absolute (relative) error. To illustrate the applicability of the described approach, we successfully managed to eliminate averagely 25 to 42% of false-positives with a filter threshold derived from ROC curves. Thus, the proposed approach should be used in addition to other well-established in silico methods and their integration may broaden the range of correctly identified molecules.