Proton-Bound 3-Cyanophenylalanine Trimethylamine Clusters: Isomer-Specific Fragmentation Pathways and Evidence of Gas-Phase Zwitterions

The hitchhiker's guide to dynamic ion-solvent clustering: applications in differential ion mobility spectrometry

This article highlights the fundamentals of ion-solvent clustering processes that are pertinent to understanding an ion's behaviour during differential mobility spectrometry (DMS) experiments. We contrast DMS with static-field ion mobility, where separation is affected by mobility differences under the high-field and low-field conditions of an asymmetric oscillating electric field. Although commonly used in mass spectrometric (MS) workflows to enhance signal-to-noise ratios and remove isobaric contaminants, the chemistry and physics that underpins the phenomenon of differential mobility has yet to be fully fleshed out. Moreover, we are just now making progress towards understanding how the DMS separation waveform creates a dynamic clustering environment when the carrier gas is seeded with the vapour of a volatile solvent molecule (e.g., methanol). Interestingly, one can correlate the dynamic clustering behaviour observed in DMS experiments with gas-phase and solution-phase molecular properties such as hydrophobicity, acidity, and solubility. However, to create a generalized, global model for property determination using DMS data one must employ machine learning. In this article, we provide a first-principles description of differential ion mobility in a dynamic clustering environment. We then discuss the correlation between dynamic clustering propensity and analyte physicochemical properties and demonstrate that analytes exhibiting similar ion-solvent interactions (e.g., charge-dipole) follow well-defined trends with respect to DMS clustering behaviour. Finally, we describe how supervised machine learning can be used to create predictive models of molecular properties using DMS data. We additionally highlight open questions in the field and provide our perspective on future directions that can be explored.

Vibrational Spectroscopy of Homo- and Heterochiral Amino Acid Dimers: Conformational Landscapes

The homo- and heterochiral protonated dimers of asparagine with serine and with valine were investigated using infrared multiple-photon dissociation (IRMPD) spectroscopy. Extensive quantum-chemical calculations were used in a three-tiered strategy to screen the conformational spaces of all four dimer species. The resulting binary structures were further grouped into five different types based on their intermolecular binding topologies and subunit configurations. For each dimer species, there are eight to fourteen final conformational geometries within a 10 kJ mol-1 window of the global minimum structure for each species. The comparison between the experimental IRMPD spectra and the simulated harmonic IR features allowed us to clearly identify the types of structures responsible for the observation. The monomeric subunits of the observed homo- and heterochiral dimers are compared to the corresponding protonated/neutral amino acid monomers observed experimentally in previous IRMDP/rotational spectroscopic studies. Possible chirality and kinetic influences on the experimental IRMPD spectra are discussed.

Determining Collision Cross Sections from Differential Ion Mobility Spectrometry

The experimental determination of ion-neutral collision cross sections (CCSs) is generally confined to ion mobility spectrometry (IMS) technologies that operate under the so-called low-field limit or those that enable empirical calibration strategies (e.g., traveling wave IMS; TWIMS). Correlation of ion trajectories to CCS in other non-linear IMS techniques that employ dynamic electric fields, such as differential mobility spectrometry (DMS), has remained a challenge since its inception. Here, we describe how an ion's CCS can be measured from DMS experiments using a machine learning (ML)-based calibration. The differential mobility of 409 molecular cations (m/z: 86-683 Da and CCS 110-236 Å2) was measured in a N2 environment to train the ML framework. Several open-source ML routines were tested and trained using DMS-MS data in the form of the parent ion's m/z and the compensation voltage required for elution at specific separation voltages between 1500 and 4000 V. The best performing ML model, random forest regression, predicted CCSs with a mean absolute percent error of 2.6 ± 0.4% for analytes excluded from the training set (i.e., out-of-the-bag external validation). This accuracy approaches the inherent statistical error of ∼2.2% for the MobCal-MPI CCS calculations employed for training purposes and the <2% threshold for matching literature CCSs with those obtained on a TWIMS platform.

Separating chiral isomers of amphetamine and methamphetamine using chemical derivatization and differential mobility spectrometry

The separation and analysis of chiral compounds, especially enantiomers, presents a great challenge to modern analytical chemistry, particularly to mass spectrometry (MS). As a result, integrated orthogonal separations, such as chiral liquid chromatography (chiral LC), gas chromatography (GC), or capillary electrophoresis (CE), are often employed to separate enantiomers prior to MS analysis. Here, we combine chemical derivatization with differential mobility spectrometry (DMS) and MS to separate and quantitate the transformed enantiomeric pairs R- and S-amphetamine, as well as R- and S-methamphetamine. We also demonstrate separation of these drugs by using reverse-phase LC. However, while the LC method requires ∼5 min to provide separation, we have developed a flow-injection analysis (FIA) method using DMS as the exclusive mode of separation (FIA-DMS), requiring only ∼1.5 min with equivalent quantitative metrics (1-1000 ng/mL range) to the LC method. The DMS-based separation of each diastereomeric pair is driven by differences in binding energies between the analyte ions and the chemical modifier molecules (acetonitrile) added to the DMS environment.

A First Principle Model of Differential Ion Mobility: the Effect of Ion-Solvent Clustering

The use of differential mobility spectrometry (DMS) as a separation tool prior to mass analysis has increased in popularity over the years. However, the fundamental principles behind the difference between high- and low-field mobility is still a matter of debate-especially regarding the strong impact of solvent molecules added to the gas phase in chemically modified DMS environments. In this contribution, we aim to present a thorough model for the determination of the ion mobility over a wide range of field strengths and subsequent calculation of DMS dispersion plots. Our model relies on first principle calculations only, incorporating the modeling of the "hard-sphere" mobility, the change in CCS with field strength, and the degree of clustering of solvent molecules to the ion. We show that all three factors have to be taken into account to qualitatively predict dispersion plots. In particular, type A behavior (i.e., strong clustering) in DMS can only be explained by a significant change of the mean cluster size with field strengths. The fact that our model correctly predicts trends between differently strong binding solvents, as well as the solvent concentration and the background gas temperature, highlights the importance of clustering for differential mobility.

Augmenting Basin-Hopping With Techniques From Unsupervised Machine Learning: Applications in Spectroscopy and Ion Mobility

Evolutionary algorithms such as the basin-hopping (BH) algorithm have proven to be useful for difficult non-linear optimization problems with multiple modalities and variables. Applications of these algorithms range from characterization of molecular states in statistical physics and molecular biology to geometric packing problems. A key feature of BH is the fact that one can generate a coarse-grained mapping of a potential energy surface (PES) in terms of local minima. These results can then be utilized to gain insights into molecular dynamics and thermodynamic properties. Here we describe how one can employ concepts from unsupervised machine learning to augment BH PES searches to more efficiently identify local minima and the transition states connecting them. Specifically, we introduce the concepts of similarity indices, hierarchical clustering, and multidimensional scaling to the BH methodology. These same machine learning techniques can be used as tools for interpreting and rationalizing experimental results from spectroscopic and ion mobility investigations (e.g., spectral assignment, dynamic collision cross sections). We exemplify this in two case studies: (1) assigning the infrared multiple photon dissociation spectrum of the protonated serine dimer and (2) determining the temperature-dependent collision cross-section of protonated alanine tripeptide.

Determining molecular properties with differential mobility spectrometry and machine learning

The fast and accurate determination of molecular properties is highly desirable for many facets of chemical research, particularly in drug discovery where pre-clinical assays play an important role in paring down large sets of drug candidates. Here, we present the use of supervised machine learning to treat differential mobility spectrometry - mass spectrometry data for ten topological classes of drug candidates. We demonstrate that the gas-phase clustering behavior probed in our experiments can be used to predict the candidates' condensed phase molecular properties, such as cell permeability, solubility, polar surface area, and water/octanol distribution coefficient. All of these measurements are performed in minutes and require mere nanograms of each drug examined. Moreover, by tuning gas temperature within the differential mobility spectrometer, one can fine tune the extent of ion-solvent clustering to separate subtly different molecular geometries and to discriminate molecules of very similar physicochemical properties.

Infrared Multiphoton Dissociation Spectroscopy with Free-Electron Lasers: On the Road from Small Molecules to Biomolecules

Infrared multiphoton dissociation (IRMPD) spectroscopy is commonly used to determine the structure of isolated, mass-selected ions in the gas phase. This method has been widely used since it became available at free-electron laser (FEL) user facilities. Thus, in this Minireview, we examine the use of IRMPD/FEL spectroscopy for investigating ions derived from small molecules, metal complexes, organometallic compounds and biorelevant ions. Furthermore, we outline new applications of IRMPD spectroscopy to study biomolecules.

Mode-Selective Laser Control of Palladium Catalyst Decomposition

It is generally assumed that molecules behave ergodically during chemical reactions, that is, reactivities depend only on the total energy content and not on the initial state of the molecule. While there are a few examples of nonergodic behavior in small (usually electronically excited) species, to date there have been no reports of such behavior in larger covalently bound species composed of several tens of atoms. Here, we demonstrate vibrational mode-selective behavior in a series of palladium catalysts. When we excite solvent-tagged gas-phase Pd catalysts with an infrared laser that is tuned to be resonant with specific molecular vibrations, depending on which vibration we excite, we can select different reaction pathways. We also demonstrate that this behavior can be "turned off" via chemical substitution.

The structures and properties of anionic tryptophan complexes

IRMPD spectroscopy and electronic structure calculations are employed to identify π–π interactions in ionic tryptophan clusters.

Applying Machine Learning to Vibrational Spectroscopy

The low-energy region of the potential energy surface (PES) of the protonated phenylalanine/serine dimer is mapped using the basin-hoping search algorithm, and 37 isomers are identified within 180 kJ·mol^-1 of the global-minimum structure. Cluster structures are grouped using hierarchical clustering to partition the PES in terms of nuclear configuration. Calculated IR spectra for the various isomers are then compared with the isomer-specific IR spectra by means of the cosine distance metric to facilitate spectral assignment and identify which regions of the PES are populated in the electrospray ionization process.

Assessing Physicochemical Properties of Drug Molecules via Microsolvation Measurements with Differential Mobility Spectrometry

The microsolvated state of a molecule, represented by its interactions with only a small number of solvent molecules, can play a key role in determining the observable bulk properties of the molecule. This is especially true in cases where strong local hydrogen bonding exists between the molecule and the solvent. One method that can probe the microsolvated states of charged molecules is differential mobility spectrometry (DMS), which rapidly interrogates an ion's transitions between a solvated and desolvated state in the gas phase (i.e., few solvent molecules present). However, can the results of DMS analyses of a class of molecules reveal information about the bulk physicochemical properties of those species? Our findings presented here show that DMS behaviors correlate strongly with the measured solution phase pK_a and pK_b values, and cell permeabilities of a set of structurally related drug molecules, even yielding high-resolution discrimination between isomeric forms of these drugs. This is due to DMS's ability to separate species based upon only subtle (yet predictable) changes in structure: the same subtle changes that can influence isomers' different bulk properties. Using 2-methylquinolin-8-ol as the core structure, we demonstrate how DMS shows promise for rapidly and sensitively probing the physicochemical properties of molecules, with particular attention paid to drug candidates at the early stage of drug development. This study serves as a foundation upon which future drug molecules of different structural classes could be examined.

Intramolecular cation–π interactions in protonated phenylalanine derivatives

The structures and properties of a series of phenylalanine (Phe) derivatives have been investigated in a joint computational and experimental infrared multiple photon dissociation (IRMPD) study.

Studying Gas-Phase Interconversion of Tautomers Using Differential Mobility Spectrometry

In this study, we report on the use of differential mobility spectrometry (DMS) as a tool for studying tautomeric species, allowing a more in-depth interrogation of these elusive isomers using ion/molecule reactions and tandem mass spectrometry. As an example, we revisit a case study in which gas-phase hydrogen-deuterium exchange (HDX)-a probe of ion structure in mass spectrometry-actually altered analyte ion structure by tautomerization. For the N- and O-protonated tautomers of 4-aminobenzoic acid, when separated using DMS and subjected to subsequent HDX with trace levels of D2O, the anticipated difference between the exchange rates of the two tautomers is observed. However, when using higher levels of D2O or a more basic reagent, equivalent and almost complete exchange of all labile protons is observed. This second observation is a result of the interconversion of the N-protonated tautomer to the O-protonated form during HDX. We can monitor this transformation experimentally, with support from detailed molecular dynamics and electronic structure calculations. In fact, calculations suggest the onset of bulk solution phase properties for 4-aminobenzoic acid upon solvation with eight CH3OH molecules. These findings also underscore the need for choosing HDX reagents and conditions judiciously when separating interconvertible isomers using DMS. Graphical Abstract ᅟ.

The structures and properties of proton- and alkali-bound cysteine dimers

The proton-, lithium-, and sodium-bound cysteine dimers have been investigated in a joint computational and experimental infrared multiple photon dissociation (IRMPD) study.

Using differential mobility spectrometry to measure ion solvation: an examination of the roles of solvents and ionic structures in separating quinoline-based drugs

Understanding the mechanisms and energetics of ion solvation using differential mobility spectrometry.

Mode-specific fragmentation of amino acid-containing clusters

Mode-specific IR-induced fragmentation is observed as a result of isomerization-induced transparency in an amino acid-containing cluster.

Ion-molecule clustering in differential mobility spectrometry: lessons learned from tetraalkylammonium cations and their isomers

Differential mobility spectrometry (DMS) can distinguish ions based upon the differences in their high- and low-field ion mobilities as they experience the asymmetric waveform applied to the DMS cell. These mobilities are known to be influenced by the ions' structure, m/z, and charge distribution (i.e., resonance structures) within the ions themselves, as well as by the gas-phase environment of the DMS cell. While these associations have been developed over time through empirical observations, the exact role of ion structures or their interactions with clustering molecules remains generally unknown. In this study, that relationship is explored by observing the DMS behaviors of a series of tetraalkylammonium ions as a function of their structures and the gas-phase environment of the DMS cell. To support the DMS experiments, the basin-hopping search strategy was employed to identify candidate cluster structures for density functional theory treatment. More than a million cluster structures distributed across 72 different ion-molecule cluster systems were sampled to determine global minimum structures and cluster binding energies. This joint computational and experimental approach suggests that cluster geometry, in particular ion-molecule intermolecular separation, plays a critical role in DMS.

Isomer-selective infrared spectroscopy of the cationic trimethylamine dimer to reveal its charge sharing and enhanced acidity of the methyl groups

The isomer-selective infrared spectroscopy revealed the charge-shared (hemibond) and the C⋯HN hydrogen-bond structures of the trimethylamine dimer cation.