Coming to Grips with Ambiguity: Ion Mobility-Mass Spectrometry for Protein Quaternary Structure Assignment

HDXRank: A Deep Learning Framework for Ranking Protein Complex Predictions with Hydrogen-Deuterium Exchange Data

Accurate modeling of protein-protein complex structures is essential for understanding biological mechanisms. Hydrogen-deuterium exchange (HDX) experiments provide valuable insights into binding interfaces. Incorporating HDX data into protein complex modeling workflows offers a promising approach to improve prediction accuracy. Here, we developed HDXRank, a graph neural network (GNN)-based framework for candidate structure ranking utilizing alignment with HDX experimental data. Trained on a newly curated HDX data set, HDXRank captures nuanced local structural features critical for accurate HDX profile prediction. This versatile framework can be integrated with a variety of protein complex modeling tools, transforming the HDX profile alignment into a model quality metric. HDXRank demonstrates effectiveness at ranking models generated by rigid docking or AlphaFold, successfully prioritizing functionally relevant models and improving prediction quality across all tested protein targets. These findings underscore HDXRank's potential to become a pivotal tool for understanding molecular recognition in complex biological systems.

Exploring snake venoms beyond the primary sequence: From proteoforms to protein-protein interactions

Snakebite envenomation has been a long-standing global issue that is difficult to treat, largely owing to the flawed nature of current immunoglobulin-based antivenom therapy and the complexity of snake venoms as sophisticated mixtures of bioactive proteins and peptides. Comprehensive characterisation of venom compositions is essential to better understanding snake venom toxicity and inform effective and rationally designed antivenoms. Additionally, a greater understanding of snake venom composition will likely unearth novel biologically active proteins and peptides that have promising therapeutic or biotechnological applications. While a bottom-up proteomic workflow has been the main approach for cataloguing snake venom compositions at the toxin family level, it is unable to capture snake venom heterogeneity in the form of protein isoforms and higher-order protein interactions that are important in driving venom toxicity but remain underexplored. This review aims to highlight the importance of understanding snake venom heterogeneity beyond the primary sequence, in the form of post-translational modifications that give rise to different proteoforms and the myriad of higher-order protein complexes in snake venoms. We focus on current top-down proteomic workflows to identify snake venom proteoforms and further discuss alternative or novel separation, instrumentation, and data processing strategies that may improve proteoform identification. The current higher-order structural characterisation techniques implemented for snake venom proteins are also discussed; we emphasise the need for complementary and higher resolution structural bioanalytical techniques such as mass spectrometry-based approaches, X-ray crystallography and cryogenic electron microscopy, to elucidate poorly characterised tertiary and quaternary protein structures. We envisage that the expansion of the snake venom characterisation "toolbox" with top-down proteomics and high-resolution protein structure determination techniques will be pivotal in advancing structural understanding of snake venoms towards the development of improved therapeutic and biotechnology applications.

Structural studies of supramolecular complexes and assemblies by ion mobility mass spectrometry

Recent advances in instrumentation and development of computational strategies for ion mobility mass spectrometry (IM-MS) studies have contributed to an extensive growth in the application of this analytical technique to comprehensive structural description of supramolecular systems. Apart from the benefits of IM-MS for interrogation of intrinsic properties of noncovalent aggregates in the experimental gas-phase environment, its merits for the description of native structural aspects, under the premises of having maintained the noncovalent interactions innate upon the ionization process, have attracted even more attention and gained increasing interest in the scientific community. Thus, various types of supramolecular complexes and assemblies relevant for biological, medical, material, and environmental sciences have been characterized so far by IM-MS supported by computational chemistry. This review covers the state-of-the-art in this field and discusses experimental methods and accompanying computational approaches for assessing the reliable three-dimensional structural elucidation of supramolecular complexes and assemblies by IM-MS.

Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver

Ion mobility coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM). Although there are several computational methods for predicting CCSIM based on protein structures, including our previously developed projection approximation using rough circular shapes (PARCS), the process usually requires prior experience with the command-line interface. To overcome this challenge, here we present a web application on the Rosetta Online Server that Includes Everyone (ROSIE) webserver to predict CCSIM from protein structure using projection approximation with PARCS. In this web interface, the user is only required to provide one or more PDB files as input. Results from our case studies suggest that CCSIM predictions (with ROSIE-PARCS) are highly accurate with an average error of 6.12%. Furthermore, the absolute difference between CCSIM and CCSPARCS can help in distinguishing accurate from inaccurate AlphaFold2 protein structure predictions. ROSIE-PARCS is designed with a user-friendly interface, is available publicly and is free to use. The ROSIE-PARCS web interface is supported by all major web browsers and can be accessed via this link (https://rosie.graylab.jhu.edu).

Are the Gas-Phase Structures of Molecular Elephants Enduring or Ephemeral? Results from Time-Dependent, Tandem Ion Mobility

The structural stability of biomolecules in the gas phase remains an important topic in mass spectrometry applications for structural biology. Here, we evaluate the kinetic stability of native-like protein ions using time-dependent, tandem ion mobility (IM). In these tandem IM experiments, ions of interest are mobility-selected after a first dimension of IM and trapped for up to ∼14 s. Time-dependent, collision cross section distributions are then determined from separations in a second dimension of IM. In these experiments, monomeric protein ions exhibited structural changes specific to both protein and charge state, whereas large protein complexes did not undergo resolvable structural changes on the timescales of these experiments. We also performed energy-dependent experiments, i.e., collision-induced unfolding, as a comparison for time-dependent experiments to understand the extent of unfolding. Collision cross section values observed in energy-dependent experiments using high collision energies were significantly larger than those observed in time-dependent experiments, indicating that the structures observed in time-dependent experiments remain kinetically trapped and retain some memory of their solution-phase structure. Although structural evolution should be considered for highly charged, monomeric protein ions, these experiments demonstrate that higher-mass protein ions can have remarkable kinetic stability in the gas phase.

Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction

Ion mobility (IM) mass spectrometry provides structural information about protein shape and size in the form of an orientationally-averaged collision cross-section (CCSIM). While IM data have been used with various computational methods, they have not yet been utilized to predict monomeric protein structure from sequence. Here, we show that IM data can significantly improve protein structure determination using the modelling suite Rosetta. We develop the Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm that allows for fast and accurate prediction of CCSIM from structure. Following successful testing of the PARCS algorithm, we use an integrative modelling approach to utilize IM data for protein structure prediction. Additionally, we propose a confidence metric that identifies near native models in the absence of a known structure. The results of this study demonstrate the ability of IM data to consistently improve protein structure prediction.

Simulation of Energy-Resolved Mass Spectrometry Distributions from Surface-Induced Dissociation

Understanding the relationship between protein structure and experimental data is crucial for utilizing experiments to solve biochemical problems and optimizing the use of sparse experimental data for structural interpretation. Tandem mass spectrometry (MS/MS) can be used with a variety of methods to collect structural data for proteins. One example is surface-induced dissociation (SID), which is used to break apart protein complexes (via a surface collision) into intact subcomplexes and can be performed at multiple laboratory frame SID collision energies. These energy-resolved MS/MS experiments have shown that the profile of the breakages depends on the acceleration energy of the collision. It is possible to extract an appearance energy (AE) from energy-resolved mass spectrometry (ERMS) data, which shows the relative intensity of each type of subcomplex as a function of SID acceleration energy. We previously determined that these AE values for specific interfaces correlated with structural features related to interface strength. In this study, we further examined the structural relationships by developing a method to predict the full ERMS plot from the structure, rather than extracting a single value. First, we noted that for proteins with multiple interface types, we could reproduce the correct shapes of breakdown curves, further confirming previous structural hypotheses. Next, we demonstrated that interface size and energy density (measured using Rosetta) correlated with data derived from the ERMS plot (R2 = 0.71). Furthermore, based on this trend, we used native crystal structures to predict ERMS. The majority of predictions resulted in good agreement, and the average root-mean-square error was 0.20 for the 20 complexes in our data set. We also show that if additional information on cleavage as a function of collision energy could be obtained, the accuracy of predictions improved further. Finally, we demonstrated that ERMS prediction results were better for the native than for inaccurate models in 17/20 cases. An application to run this simulation has been developed in Rosetta, which is freely available for use.

Approaches to Heterogeneity in Native Mass Spectrometry

Native mass spectrometry (MS) is aimed at preserving and determining the native structure, composition, and stoichiometry of biomolecules and their complexes from solution after they are transferred into the gas phase. Major improvements in native MS instrumentation and experimental methods over the past few decades have led to a concomitant increase in the complexity and heterogeneity of samples that can be analyzed, including protein-ligand complexes, protein complexes with multiple coexisting stoichiometries, and membrane protein-lipid assemblies. Heterogeneous features of these biomolecular samples can be important for understanding structure and function. However, sample heterogeneity can make assignment of ion mass, charge, composition, and structure very challenging due to the overlap of tens or even hundreds of peaks in the mass spectrum. In this review, we cover data analysis, experimental, and instrumental advances and strategies aimed at solving this problem, with an in-depth discussion of theoretical and practical aspects of the use of available deconvolution algorithms and tools. We also reflect upon current challenges and provide a view of the future of this exciting field.

Collision Cross Sections for Native Proteomics: Challenges and Opportunities

Recent advancements place a comprehensive catalog of protein structure, oligomeric state, sequence, and modification status tentatively within reach, thus providing an unprecedented roadmap to therapies for many human diseases. To achieve this goal, revolutionary technologies capable of bridging key gaps in our ability to simultaneously measure protein composition and structure must be developed. Much of the current progress in this area has been catalyzed by mass spectrometry (MS) tools, which have become an indispensable resource for interrogating the structural proteome. For example, methods associated with native proteomics seek to comprehensively capture and quantify the endogenous assembly states for all proteins within an organism. Such technologies have often been partnered with ion mobility (IM) separation, from which collision cross section (CCS) information can be rapidly extracted to provide protein size information. IM technologies are also being developed that utilize CCS values to enhance the confidence of protein identification workflows derived from liquid chromatography-IM-MS analyses of enzymatically produced peptide mixtures. Such parallel advancements in technology beg the question: can CCS values prove similarly useful for the identification of intact proteins and their complexes in native proteomics? In this perspective, I examine current evidence and technology trends to explore the promise and limitations of such CCS information for the comprehensive analysis of multiprotein complexes from cellular mixtures.

Reaction Monitoring and Structural Characterisation of Coordination Driven Self-Assembled Systems by Ion Mobility-Mass Spectrometry

Nature creates exquisite molecular assemblies, required for the molecular-level functions of life, via self-assembly. Understanding and harnessing these complex processes presents an immense opportunity for the design and fabrication of advanced functional materials. However, the significant industrial potential of self-assembly to fabricate highly functional materials is hampered by a lack of knowledge of critical reaction intermediates, mechanisms, and kinetics. As we move beyond the covalent synthetic regime, into the domain of non-covalent interactions occupied by self-assembly, harnessing and embracing complexity is a must, and non-targeted analyses of dynamic systems are becoming increasingly important. Coordination driven self-assembly is an important subtype of self-assembly that presents several wicked analytical challenges. These challenges are "wicked" due the very complexity desired confounding the analysis of products, intermediates, and pathways, therefore limiting reaction optimisation, tuning, and ultimately, utility. Ion Mobility-Mass Spectrometry solves many of the most challenging analytical problems in separating and analysing the structure of both simple and complex species formed via coordination driven self-assembly. Thus, due to the emerging importance of ion mobility mass spectrometry as an analytical technique tackling complex systems, this review highlights exciting recent applications. These include equilibrium monitoring, structural and dynamic analysis of previously analytically inaccessible complex interlinked structures and the process of self-sorting. The vast and largely untapped potential of ion mobility mass spectrometry to coordination driven self-assembly is yet to be fully realised. Therefore, we also propose where current analytical approaches can be built upon to allow for greater insight into the complexity and structural dynamics involved in self-assembly.

Prediction of Protein Complex Structure Using Surface-Induced Dissociation and Cryo-Electron Microscopy

A variety of techniques involving the use of mass spectrometry (MS) have been developed to obtain structural information on proteins and protein complexes. One example of these techniques, surface-induced dissociation (SID), has been used to study the oligomeric state and connectivity of protein complexes. Recently, we demonstrated that appearance energies (AE) could be extracted from SID experiments and that they correlate with structural features of specific protein-protein interfaces. While SID AE provides some structural information, the AE data alone are not sufficient to determine the structures of the complexes. For this reason, we sought to supplement the data with computational modeling, through protein-protein docking. In a previous study, we demonstrated that the scoring of structures generated from protein-protein docking could be improved with the inclusion of SID data; however, this work relied on knowledge of the correct tertiary structure and only built full complexes for a few cases. Here, we performed docking using input structures that require less prior knowledge, using homology models, unbound crystal structures, and bound+perturbed crystal structures. Using flexible ensemble docking (to build primarily subcomplexes from an ensemble of backbone structures), the RMSD₁₀₀ of all (15/15) predicted structures using the combined Rosetta, cryo-electron microscopy (cryo-EM), and SID score was less than 4 Å, compared to only 7/15 without SID and cryo-EM. Symmetric docking (which used symmetry to build full complexes) resulted in predicted structures with RMSD₁₀₀ less than 4 Å for 14/15 cases with experimental data, compared to only 5/15 without SID and cryo-EM. Finally, we also developed a confidence metric for which all (26/26) proteins flagged as high confidence were accurately predicted.

Interrogating the Quaternary Structure of Noncanonical Hemoglobin Complexes by Electrospray Mass Spectrometry and Collision-Induced Dissociation

Various activation methods are available for the fragmentation of gaseous protein complexes produced by electrospray ionization (ESI). Such experiments can potentially yield insights into quaternary structure. Collision-induced dissociation (CID) is the most widely used fragmentation technique. Unfortunately, CID of protein complexes is dominated by the ejection of highly charged monomers, a process that does not yield any structural insights. Using hemoglobin (Hb) as a model system, this work examines under what conditions CID generates structurally informative subcomplexes. Native ESI mainly produced tetrameric Hb ions. In addition, "noncanonical" hexameric and octameric complexes were observed. CID of all these species [(αβ)₂, (αβ)₃, and (αβ)₄] predominantly generated highly charged monomers. In addition, we observed hexamer → tetramer + dimer dissociation, implying that hexamers have a tetramer··dimer architecture. Similarly, the observation of octamer → two tetramer dissociation revealed that octamers have a tetramer··tetramer composition. Gas-phase candidate structures of Hb assemblies were produced by molecular dynamics (MD) simulations. Ion mobility spectrometry was used to identify the most likely candidates. Our data reveal that the capability of CID to produce structurally informative subcomplexes depends on the fate of protein-protein interfaces after transfer into the gas phase. Collapse of low affinity interfaces conjoins the corresponding subunits and favors CID via monomer ejection. Structurally informative subcomplexes are formed only if low affinity interfaces do not undergo a major collapse. However, even in these favorable cases CID is still dominated by monomer ejection, requiring careful analysis of the experimental data for the identification of structurally informative subcomplexes.

Hybrid methods for combined experimental and computational determination of protein structure

Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.

Collision cross-section analysis of self-assembled metallomacrocycle isomers and isobars via ion mobility mass spectrometry

RATIONALE

Coordinatively driven self-assembly of transition metal ions and bidentate ligands gives rise to organometallic complexes that usually contain superimposed isobars, isomers, and conformers. In this study, the double dispersion ability of ion mobility mass spectrometry (IM-MS) was used to provide a comprehensive structural characterization of the self-assembled supramolecular complexes by their mass and charge, revealed by the MS event, and their shape and collision cross-section (Ω), revealed by the IM event.

METHODS

Self-assembled complexes were synthesized by reacting a bis(terpyridine) ligand exhibiting a 60^o dihedral angle between the two ligating terpyridine sites (T) with divalent Zn, Ni, Cd, or Fe. The products were isolated as (Metal²⁺ [T])_n (PF₆ )_2n salts and analyzed using IM-MS after electrospray ionization (ESI) which produced several charge states from each n-mer, depending on the number of PF₆ - anions lost upon ESI. Experimental Ω data, derived using IM-MS, and computational Ω predictions were used to elucidate the size and architecture of the complexes.

RESULTS

Only macrocyclic dimers, trimers, and tetramers were observed with Cd²⁺ , whereas Zn²⁺ formed the same plus hexameric complexes. These two metals led to the simplest product distributions and no linear isomers. In sharp contrast, Ni²⁺ and Fe²⁺ formed all possible ring sizes from dimer to hexamer as well as various linear isomers. The experimental and theoretical Ω data indicated rather planar macrocyclic geometries for the dimers and trimers, twisted 3D architectures for the larger rings, and substantially larger sizes with spiral conformation for the linear congeners. Adding PF₆ - to the same complex was found to mainly cause size contraction due to new stabilizing anion-cation interactions.

CONCLUSIONS

Complete structural identification could be accomplished using ESI-IM-MS. Our results affirm that self-assembly with Cd²⁺ and Zn²⁺ proceeds through reversible equilibria that generate the thermodynamically most stable structures, encompassing exclusively macrocyclic architectures that readily accommodate the 60^o ligand used. In contrast, complexation with Ni²⁺ and Fe²⁺ , which form stronger coordinative bonds, proceeds through kinetic control, leading to more complex mixtures and kinetically trapped less stable architectures, such as macrocyclic pentamers and linear isomers.

A Novel Ion Pseudo-trapping Phenomenon within Traveling Wave Ion Guides

The widespread use of traveling wave ion mobility (TWIM) technology in fields such as omics and structural biology motivates efforts to deepen our understanding of ion transport within such devices. Here, we describe a new advancement in TWIM theory, where pseudo-trapping within TW ion guides is characterized in detail. During pseudo-trapping, ions with different mobilities can travel with the same mean velocity, leaving others within the same TWIM experiment to separate as normal. Furthermore, pseudo-trapping limits typical band broadening experienced by ions during TWIM, manifesting as peaks with apparently improved IM resolving power, but all ions that undergo pseudo trapping are unable to separate by IM. SIMION simulations show that ions become locked into a repeated pattern of motion with respect to the TW reference frame during pseudo-trapping. We developed a simplified model capable of reproducing TW pseudo-trapping and reproducing trends observed in experimental data. Our model and simulations suggest that pseudo-trapping occurs only during experiments performed under static TWIM conditions, to an extent that depends on the detailed shape of the traveling wave. We show that pseudo-trapping alters the ion transit times and can adversely affect calibrated CCS measurements. Finally, we provide recommendations for avoiding unintentional pseudo-trapping in TWIM in order to obtain optimal separations and CCS determinations.

Multidimensional Nanoparticle Characterization through Ion Mobility-Mass Spectrometry

Multidimensional techniques that combine fully or partially orthogonal characterization methods in a single setup often provide a more comprehensive description of analytes. When applied to nanoparticles, they have the potential to reveal particle properties not accessible to more conventional 1D techniques. Herein, we apply recently developed 2D characterization techniques to nanoparticles using atmospheric-pressure ion mobility-mass spectrometry (IM-MS), and we demonstrate the analytical capability of this approach using ultraporous mesostructured silica nanoparticles (UMNs). We show that IM-MS yields a 2D particle size-mass distribution function, which in turn can be used to calculate not only important 1D distributions, i.e. particle size distributions, but also nanoparticle structural property distributions not accessible by other methods, including size-dependent particle porosity and the specific pore volume distribution function. IM-MS measurement accuracy was confirmed by measurement of NIST-certified polystyrene latex particle standards. For UMNs, comparison of IM-MS results with TEM and N₂ physisorption yields quantitative agreement in particle size and qualitative agreement in average specific pore volume. IM-MS uniquely shows how within a single UMN population, porosity increases with increasing particle size, consistent with the proposed UMN growth mechanism. In total, we demonstrate the potential of IM-MS as a standard approach for the characterization of structurally complex nanoparticle populations, as it yields size-specific structural distribution functions.

Predicting Protein Complex Structure from Surface-Induced Dissociation Mass Spectrometry Data

Recently, mass spectrometry (MS) has become a viable method for elucidation of protein structure. Surface-induced dissociation (SID), colliding multiply charged protein complexes or other ions with a surface, has been paired with native MS to provide useful structural information such as connectivity and topology for many different protein complexes. We recently showed that SID gives information not only on connectivity and topology but also on relative interface strengths. However, SID has not yet been coupled with computational structure prediction methods that could use the sparse information from SID to improve the prediction of quaternary structures, i.e., how protein subunits interact with each other to form complexes. Protein-protein docking, a computational method to predict the quaternary structure of protein complexes, can be used in combination with subunit structures from X-ray crystallography and NMR in situations where it is difficult to obtain an experimental structure of an entire complex. While de novo structure prediction can be successful, many studies have shown that inclusion of experimental data can greatly increase prediction accuracy. In this study, we show that the appearance energy (AE, defined as 10% fragmentation) extracted from SID can be used in combination with Rosetta to successfully evaluate protein-protein docking poses. We developed an improved model to predict measured SID AEs and incorporated this model into a scoring function that combines the RosettaDock scoring function with a novel SID scoring term, which quantifies agreement between experiments and structures generated from RosettaDock. As a proof of principle, we tested the effectiveness of these restraints on 57 systems using ideal SID AE data (AE determined from crystal structures using the predictive model). When theoretical AEs were used, the RMSD of the selected structure improved or stayed the same in 95% of cases. When experimental SID data were incorporated on a different set of systems, the method predicted near-native structures (less than 2 Å root-mean-square deviation, RMSD, from native) for 6/9 tested cases, while unrestrained RosettaDock (without SID data) only predicted 3/9 such cases. Score versus RMSD funnel profiles were also improved when SID data were included. Additionally, we developed a confidence measure to evaluate predicted model quality in the absence of a crystal structure.

Structural mass spectrometry goes viral

Over the last 20 years, mass spectrometry (MS), with its ability to analyze small sample amounts with high speed and sensitivity, has more and more entered the field of structural virology, aiming to investigate the structure and dynamics of viral proteins as close to their native environment as possible. The use of non-perturbing labels in hydrogen-deuterium exchange MS allows for the analysis of interactions between viral proteins and host cell factors as well as their dynamic responses to the environment. Cross-linking MS, on the other hand, can analyze interactions in viral protein complexes and identify virus-host interactions in cells. Native MS allows transferring viral proteins, complexes and capsids into the gas phase and has broken boundaries to overcome size limitations, so that now even the analysis of intact virions is possible. Different MS approaches not only inform about size, stability, interactions and dynamics of virus assemblies, but also bridge the gap to other biophysical techniques, providing valuable constraints for integrative structural modeling of viral complex assemblies that are often inaccessible by single technique approaches. In this review, recent advances are highlighted, clearly showing that structural MS approaches in virology are moving towards systems biology and ever more experiments are performed on cellular level.

A Semi-Empirical Framework for Interpreting Traveling Wave Ion Mobility Arrival Time Distributions

The inherent structural heterogeneity of biomolecules is an important biophysical property that is essential to their function, but is challenging to characterize experimentally. We present a workflow that rapidly and quantitatively assesses the conformational heterogeneity of peptides and proteins in the gas phase using traveling wave ion mobility (TWIM) arrival time distributions (ATDs). We have established a set of semi-empirical equations that model the TWIM ATD peak width and resolution across a wide range of wave amplitudes (V) and wave velocities (v). In addition, a conformational broadening parameter, δ, can be extracted from this analysis that reports on the contribution of conformational heterogeneity to the broadening of TWIM ATD peak width during ion mobility separation. We use this δ value to evaluate the conformational heterogeneity of a set of helical peptides, and our analysis correlates well with previous peak width observations reported for these ions. Furthermore, we use molecular dynamics simulations to independently investigate the general flexibility of these peptides in the gas phase, and generate similar trends found in experimental TWIM data. Finally, we extended our analysis to Avidin, a 64-kDa homotetramer, and quantify the structural heterogeneity of this intact complex using TWIM ATD data as a function of cross-linking. We observe an initial reduction in δ values as a function of cross-linker concentration, demonstrating the sensitivity of our δ value analysis to changes in flexibility of the assembly.

Native Mass Spectrometry, Ion Mobility, Electron-Capture Dissociation, and Modeling Provide Structural Information for Gas-Phase Apolipoprotein E Oligomers

Apolipoprotein E (apoE) is an essential protein in lipid and cholesterol metabolism. Although the three common isoforms in humans differ only at two sites, their consequences in Alzheimer's disease (AD) are dramatically different: only the ε4 allele is a major genetic risk factor for late-onset Alzheimer's disease. The isoforms exist as a mixture of oligomers, primarily tetramer, at low μM concentrations in a lipid-free environment. This self-association is involved in equilibrium with the lipid-free state, and the oligomerization interface overlaps with the lipid-binding region. Elucidation of apoE wild-type (WT) structures at an oligomeric state, however, has not yet been achieved. To address this need, we used native electrospray ionization and mass spectrometry (native MS) coupled with ion mobility (IM) to examine the monomer and tetramer of the three WT isoforms. Although collision-induced unfolding (CIU) cannot distinguish the WT isoforms, the monomeric mutant (MM) of apoE3 shows higher stability when submitted to CIU than the WT monomer. From ion-mobility measurements, we obtained the collision cross section and built a coarse-grained model for the tetramer. Application of electron-capture dissociation (ECD) to the tetramer causes unfolding starting from the C-terminal domain, in good agreement with solution denaturation data, and provides additional support for the C4 symmetry structure of the tetramer.

Conditions for Analysis of Native Protein Structures Using Uniform Field Drift Tube Ion Mobility Mass Spectrometry and Characterization of Stable Calibrants for TWIM-MS

Determination of collisional cross sections (CCS) by travelling wave ion mobility mass spectrometry (TWIM-MS) requires calibration against standards for which the CCS has been measured previously by drift tube ion mobility mass spectrometry (DTIM-MS). The different extents of collisional activation in TWIM-MS and DTIM-MS can give rise to discrepancies in the CCS of calibrants across the two platforms. Furthermore, the conditions required to ionize and transmit large, folded proteins and assemblies may variably affect the structure of the calibrants and analytes. Stable hetero-oligomeric phospholipase A₂ (PDx) and its subunits were characterized as calibrants for TWIM-MS. Conditions for acquisition of native-like TWIM (Synapt G1 HDMS) and DTIM (Agilent 6560 IM-Q-TOF) mass spectra were optimized to ensure the spectra exhibited similar charge state distributions. CCS measurements (DTIM-MS) for ubiquitin, cytochrome c, holo-myoglobin, serum albumin and glutamate dehydrogenase were in good agreement with other recent results determined using this and other DTIM-MS instruments. PDx and its β and γ subunits were stable across a wide range of cone and trap voltages in TWIM-MS and were stable in the presence of organic solvents. The CCS of PDx and its subunits were determined by DTIM-MS and were used as calibrants in determination of CCS of native-like cytochrome c, holo-myoglobin, carbonic anhydrase, serum albumin and haemoglobin in TWIM-MS. The CCS values were in good agreement with those measured by DTIM-MS where available. These experiments demonstrate conditions for analysis of native-like proteins using a commercially available DTIM-MS instrument, characterize robust calibrants for TWIM-MS, and present CCS values determined by DTIM-MS and TWIM-MS for native proteins to add to the current literature database. Graphical Abstract ᅟ.

Structural mass spectrometry comes of age: new insight into protein structure, function and interactions

Mass spectrometry (MS) provides an impressive array of information about the structure, function and interactions of proteins. In recent years, many new developments have been in the field of native MS and these exemplify a new coming of age of this field. In this mini review, we connect the latest methodological and instrumental developments in native MS to the new insights these have enabled. We highlight the prominence of an increasingly common strategy of using hybrid approaches, where multiple MS-based techniques are used in combination, and integrative approaches, where MS is used alongside other techniques such as ion-mobility spectrometry. We also review how the emergence of a native top-down approach, which combines native MS with top-down proteomics into a single experiment, is the pièce de résistance of structural mass spectrometry's coming of age. Finally, we outline key developments that have enabled membrane protein native MS to shift from being extremely challenging to routine, and how this technique is uncovering inaccessible details of membrane protein–lipid interactions.

On the Effect of Sphere-Overlap on Super Coarse-Grained Models of Protein Assemblies

Ion mobility mass spectrometry (IM/MS) can provide structural information on intact protein complexes. Such data, including connectivity and collision cross sections (CCS) of assemblies' subunits, can in turn be used as a guide to produce representative super coarse-grained models. These models are constituted by ensembles of overlapping spheres, each representing a protein subunit. A model is considered plausible if the CCS and sphere-overlap levels of its subunits fall within predetermined confidence intervals. While the first is determined by experimental error, the latter is based on a statistical analysis on a range of protein dimers. Here, we first propose a new expression to describe the overlap between two spheres. Then we analyze the effect of specific overlap cutoff choices on the precision and accuracy of super coarse-grained models. Finally, we propose a method to determine overlap cutoff levels on a per-case scenario, based on collected CCS data, and show that it can be applied to the characterization of the assembly topology of symmetrical homo-multimers. Graphical Abstract.

Direct observation of C60− nano-ion gas phase ozonation via ion mobility-mass spectrometry

Atmospheric pressure differential mobility analysis-mass spectrometry facilitates determination of nano-ion-neutral reaction rates approaching the collision controlled limit.

Monitoring Metallo-Macromolecular Assembly Equilibria by Ion Mobility-Mass Spectrometry

Ion mobility-mass spectrometry (IM-MS) allows the separation of isomeric and isobaric species on the basis of their size, shape, and charge. The fast separation timescale (ms) and high sensitivity of these measurements make IM-MS an ideally suitable method for monitoring changes in macromolecular structure, such as those occurring in interconverting terpyridine-based metallosupramolecular self-assemblies. IM-MS is used to verify the elemental composition (size) and architecture (shape) of the self-assembled products. Additionally, this article demonstrates its applicability to the elucidation of concentration-driven association-dissociation (fusion-fission) equilibria between isobaric structures. IM-MS enables both quantitative separation and identification of the interconverting complexes as well as derivation of the corresponding equilibrium constants (i.e., thermodynamic information) from extracted IM-MS abundance data.

A Structural Model of the Urease Activation Complex Derived from Ion Mobility-Mass Spectrometry and Integrative Modeling

The synthesis of active Klebsiella aerogenes urease via an 18-subunit enzyme apoprotein-accessory protein pre-activation complex has been well studied biochemically, but thus far this complex has remained refractory to direct structural characterization. Using ion mobility-mass spectrometry, we characterized several protein complexes between the core urease apoprotein and its accessory proteins, including the 610-kDa (UreABC)₃(UreDFG)₃ complex. Using our recently developed computational modeling workflow, we generated ensembles of putative (UreABC)₃(UreDFG)₃ species consistent with experimental restraints and characterized the structural ambiguity present in these models. By integrating structural information from previous studies, we increased the resolution of the ion mobility-mass spectrometry-derived models substantially, and we observe a discrete population of structures consistent with all of the available data for this complex.