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

Roughhousing with Ions: Surface-Induced Dissociation and Electron Capture Dissociation as Diagnostics of Q-Cyclic IMS-TOF Instrument Tuning Gentleness

Native mass spectrometry can characterize a range of biomolecular features pertinent to structural biology, including intact mass, stoichiometry, ligand-bound states, and topology. However, when an instrument's ionization source is tuned to maximize signal intensity or adduct removal, it is possible that the biomolecular complex's tertiary and quaternary structures can be rearranged in a way that no longer reflect its native-like conformation. This could affect downstream ion activation experiments, leading to erroneous conclusions about the native-like structure. One activation strategy is surface-induced dissociation (SID), which generally causes native-like protein complexes to dissociate along the weakest subunit interfaces, revealing critical information about the complex's native-like topology and subunit connectivity. If the quaternary structure has been disturbed, then the SID fingerprint will shift as well. Thus, SID was used to diagnose source-induced quaternary structure rearrangement and help tune an instrument's source and other upstream transmission regions to strike the balance between signal intensity, adduct removal, and conserving the native-like structure. Complementary to SID, electron-capture dissociation (ECD) can also diagnose rearranged quaternary structures and was used after in-source activation to confirm that the subunit interfaces were rearranged, opening the structure to electron capture and subsequent dissociation. These results provide a valuable guide for new practitioners of native mass spectrometry and highlight the importance of using standard protein complexes when tuning new instrument platforms for optimal native mass spectrometry performance.

Fe-S cluster biosynthesis and maturation: Mass spectrometry-based methods advancing the field

Iron‑sulfur (FeS) clusters are inorganic protein cofactors that perform essential functions in many physiological processes. Spectroscopic techniques have historically been used to elucidate details of FeS cluster type, their assembly and transfer, and changes in redox and ligand binding properties. Structural probes of protein topology, complex formation, and conformational dynamics are also necessary to fully understand these FeS protein systems. Recent developments in mass spectrometry (MS) instrumentation and methods provide new tools to investigate FeS cluster and structural properties. With the unique advantage of sampling all species in a mixture, MS-based methods can be utilized as a powerful complementary approach to probe native dynamic heterogeneity, interrogate protein folding and unfolding equilibria, and provide extensive insight into protein binding partners within an entire proteome. Here, we highlight key advances in FeS protein studies made possible by MS methodology and contribute an outlook for its role in the field.

Mosaic neuraminidase-based vaccine induces antigen-specific T cell responses against homologous and heterologous influenza viruses

Seasonal influenza is an annually severe crisis for global public health, and an ideal influenza vaccine is expected to provide broad protection against constantly drifted strains. Compared to highly flexible hemagglutinin (HA), increasing data have demonstrated that neuraminidase (NA) might be a potential target against influenza variants. In the present study, a series of genetic algorithm-based mosaic NA were designed, and then cloned into recombinant DNA and replication-defective Vesicular Stomatitis Virus (VSV) vector as a novel influenza vaccine candidate. Our Results showed that DNA prime/VSV boost strategy elicited a robust NA-specific Th1-dominated immune response, but the traditional inactivated influenza vaccine elicited a Th2-dominated immune response. More importantly, the superior NA-specific immunity induced by our strategy could confer both a full protection against lethal homologous influenza challenge and a partial protection against heterologous influenza infection. These findings will provide insights on designing NA-based universal vaccine strategy against influenza variants.

Oxidized and Reduced Dimeric Protein Complexes Illustrate Contrasting CID and SID Charge Partitioning

Charge partitioning during the dissociation of protein complexes in the gas phase is influenced by many factors, such as interfacial interactions, protein flexibility, protein conformation, and dissociation methods. In the present work, two cysteine-containing homodimer proteins, β-lactoglobulin and α-lactalbumin, with the disulfide bonds intact and reduced, were used to gain insight into the charge partitioning behaviors of collision-induced dissociation (CID) and surface-induced dissociation (SID) processes. For these proteins, we find that restructuring dominates with CID and dissociation with symmetric charge partitioning dominates with SID, regardless of whether intramolecular disulfide bonds are oxidized or reduced. CID of the charge-reduced dimeric protein complex leads to a precursor with a slightly smaller collision cross section (CCS), greater stability, and more symmetrically distributed charges than the significantly expanded form produced by CID of the higher charged dimer. Collision-induced unfolding plots demonstrate that the unfolding-restructuring of the protein complexes initiates the charge migration of higher charge-state precursors. Overall, gas collisions reveal the charge-dependent restructuring/unfolding properties of the protein precursor, while surface collisions lead predominantly to more charge-symmetric monomer separation. CID's multiple low-energy collisions sequentially reorganize intra- and intermolecular bonds, while SID's large-step energy jump cleaves intermolecular interfacial bonds in preference to reorganizing intramolecular bonds. The activated population of precursors that have taken on energy without dissociating (populated in CID over a wide range of collision energies, populated in SID for only a narrow distribution of collision energies near the onset of dissociation) is expected to be restructured, regardless of the activation method.

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).

Determining the gas-phase structures of α-helical peptides from shape, microsolvation, and intramolecular distance data

Mass spectrometry is a powerful technique for the structural and functional characterization of biomolecules. However, it remains challenging to accurately gauge the gas-phase structure of biomolecular ions and assess to what extent native-like structures are maintained. Here we propose a synergistic approach which utilizes Förster resonance energy transfer and two types of ion mobility spectrometry (i.e., traveling wave and differential) to provide multiple constraints (i.e., shape and intramolecular distance) for structure-refinement of gas-phase ions. We add microsolvation calculations to assess the interaction sites and energies between the biomolecular ions and gaseous additives. This combined strategy is employed to distinguish conformers and understand the gas-phase structures of two isomeric α-helical peptides that might differ in helicity. Our work allows more stringent structural characterization of biologically relevant molecules (e.g., peptide drugs) and large biomolecular ions than using only a single structural methodology in the gas phase.

Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization

The discovery of functional protein complex and the interrogation of the complex structure-function relationship (SFR) play crucial roles in the understanding and intervention of biological processes. Affinity purification-mass spectrometry (AP-MS) has been proved as a powerful tool in the discovery of protein complexes. However, validation of these novel protein complexes as well as elucidation of their molecular interaction mechanisms are still challenging. Recently, native top-down MS (nTDMS) is rapidly developed for the structural analysis of protein complexes. In this review, we discuss the integration of AP-MS and nTDMS in the discovery and structural characterization of functional protein complexes. Further, we think the emerging artificial intelligence (AI)-based protein structure prediction is highly complementary to nTDMS and can promote each other. We expect the hybridization of integrated structural MS with AI prediction to be a powerful workflow in the discovery and SFR investigation of functional protein complexes.

Ion Mobility Mass Spectrometry (IM-MS) for Structural Biology: Insights Gained by Measuring Mass, Charge, and Collision Cross Section

The investigation of macromolecular biomolecules with ion mobility mass spectrometry (IM-MS) techniques has provided substantial insights into the field of structural biology over the past two decades. An IM-MS workflow applied to a given target analyte provides mass, charge, and conformation, and all three of these can be used to discern structural information. While mass and charge are determined in mass spectrometry (MS), it is the addition of ion mobility that enables the separation of isomeric and isobaric ions and the direct elucidation of conformation, which has reaped huge benefits for structural biology. In this review, where we focus on the analysis of proteins and their complexes, we outline the typical features of an IM-MS experiment from the preparation of samples, the creation of ions, and their separation in different mobility and mass spectrometers. We describe the interpretation of ion mobility data in terms of protein conformation and how the data can be compared with data from other sources with the use of computational tools. The benefit of coupling mobility analysis to activation via collisions with gas or surfaces or photons photoactivation is detailed with reference to recent examples. And finally, we focus on insights afforded by IM-MS experiments when applied to the study of conformationally dynamic and intrinsically disordered proteins.

Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling

Covalent labeling (CL) in combination with mass spectrometry can be used as an analytical tool to study and determine structural properties of protein-protein complexes. However, data from these experiments is sparse and does not unambiguously elucidate protein structure. Thus, computational algorithms are needed to deduce structure from the CL data. In this work, we present a hybrid method that combines models of protein complex subunits generated with AlphaFold with differential CL data via a CL-guided protein-protein docking in Rosetta. In a benchmark set, the RMSD (root-mean-square deviation) of the best-scoring models was below 3.6 Å for 5/5 complexes with inclusion of CL data, whereas the same quality was only achieved for 1/5 complexes without CL data. This study suggests that our integrated approach can successfully use data obtained from CL experiments to distinguish between nativelike and non-nativelike models.

Native mass spectrometry for the investigation of protein structural (dis)order

A central challenge in structural biology is represented by dynamic and heterogeneous systems, as typically represented by proteins in solution, with the extreme case of intrinsically disordered proteins (IDPs) [1-3]. These proteins lack a specific three-dimensional structure and have poorly organized secondary structure. For these reasons, they escape structural characterization by conventional biophysical methods. The investigation of these systems requires description of conformational ensembles, rather than of unique, defined structures or bundles of largely superimposable structures. Mass spectrometry (MS) has become a central tool in this field, offering a variety of complementary approaches to generate structural information on either folded or disordered proteins [4-6]. Two main categories of methods can be recognized. On one side, conformation-dependent reactions (such as cross-linking, covalent labeling, H/D exchange) are exploited to label molecules in solution, followed by the characterization of the labeling products by denaturing MS [7-11]. On the other side, non-denaturing ("native") MS can be used to directly explore the different conformational components in terms of geometry and structural compactness [12-16]. All these approaches have in common the capability to conjugate protein structure investigation with the peculiar analytical power of MS measurements, offering the possibility of assessing species distributions for folding and binding equilibria and the combination of both. These methods can be combined with characterization of noncovalent complexes [17, 18] and post-translational modifications [19-23]. This review focuses on the application of native MS to protein structure and dynamics investigation, with a general methodological section, followed by examples on specific proteins from our laboratory.

Structural Studies of a Stapled Peptide with Native Ion Mobility-Mass Spectrometry and Transition Metal Ion Förster Resonance Energy Transfer in the Gas Phase

Native mass spectrometry has emerged as an important tool for gas-phase structural biology. However, the conformations that a biomolecular ion adopts in the gas phase can differ from those found in solution. Herein, we report a synergistic, native ion mobility-mass spectrometry (IM-MS) and transition metal ion Förster resonance energy transfer (tmFRET)-based approach to probe the gas-phase ion structures of a nonstapled peptide (nsp; Ac-CAARAAHAAAHARARA-NH₂) and a stapled peptide (sp; Ac-CXARAXHAAAHARARA-NH₂). The stapled peptide contains a single hydrocarbon chain connecting the peptide backbone in the i and i + 4 positions via a Grubbs ring-closure metathesis. Fluorescence lifetime measurements indicated that the Cu-bound complexes of carboxyrhodamine 6g (crh6g)-labeled stapled peptide (sp-crh6g) had a shorter donor-acceptor distance (r_DA) than the labeled nonstapled peptide (nsp-crh6g). Experimental collision cross-section (CCS) values were then determined by native IM-MS, which could separate the conformations of Cu-bound complexes of nsp-crh6g and sp-crh6g. Finally, the experimental CCS (i.e., shape) and r_DA (i.e., distance) values were used as constraints for computational studies, which unambiguously revealed how a staple reduces the elongation of the peptide ions in the gas phase. This study demonstrates the superiority of combining native IM-MS, tmFRET, and computational studies to investigate the structure of biomolecular ions.

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.

Rational design of AAVrh10-vectored ACE2 functional domain to broadly block the cell entry of SARS-CoV-2 variants

The frequently emerging SARS-CoV-2 variants have weakened the effectiveness of existing COVID-19 vaccines and neutralizing antibody therapy. Nevertheless, the infections of SARS-CoV-2 variants still depend on angiotensin-converting enzyme 2 (ACE2) receptor-mediated cell entry, and thus the soluble human ACE2 (shACE2) is a potential decoy for broadly blocking SARS-CoV-2 variants. In this study, we firstly generated the recombinant AAVrh10-vectored shACE2 constructs, a kind of adeno-associated virus (AAV) serotype with pulmonary tissue tropism, and then validated its inhibition capacity against SARS-CoV-2 infection. To further optimize the minimized ACE2 functional domain candidates, a comprehensive analysis was performed to clarify the interactions between the ACE2 orthologs from various species and the receptor binding domain (RBD) of SARS-CoV-2 spike (S) protein. Based on the key interface amino acids, we designed a series of truncated ACE2 orthologs, and then assessed their potential affinity to bind to SARS-CoV-2 variants RBD in silico. Of note, we found that the 24-83aa fragment of dog ACE2 (dACE2_24-83) had a higher affinity to the RBD of SARS-CoV-2 variants than that of human ACE2. Importantly, AAVrh10-vectored shACE2 or dACE2_24-83 constructs exhibited a broadly blockage breadth against SARS-CoV-2 prototype and variants in vitro and ex vivo. Collectively, these data highlighted a promising therapeutic strategy against SARS-CoV-2 variants.

Native top-down mass spectrometry for higher-order structural characterization of proteins and complexes

Progress in structural biology research has led to a high demand for powerful and yet complementary analytical tools for structural characterization of proteins and protein complexes. This demand has significantly increased interest in native mass spectrometry (nMS), particularly native top-down mass spectrometry (nTDMS) in the past decade. This review highlights recent advances in nTDMS for structural research of biological assemblies, with a particular focus on the extra multi-layers of information enabled by TDMS. We include a short introduction of sample preparation and ionization to nMS, tandem fragmentation techniques as well as mass analyzers and software/analysis pipelines used for nTDMS. We highlight unique structural information offered by nTDMS and examples of its broad range of applications in proteins, protein-ligand interactions (metal, cofactor/drug, DNA/RNA, and protein), therapeutic antibodies and antigen-antibody complexes, membrane proteins, macromolecular machineries (ribosome, nucleosome, proteosome, and viruses), to endogenous protein complexes. The challenges, potential, along with perspectives of nTDMS methods for the analysis of proteins and protein assemblies in recombinant and biological samples are discussed.

Mapping paratopes of nanobodies using native mass spectrometry and ultraviolet photodissociation

Following immense growth and maturity of the field in the past decade, native mass spectrometry has garnered widespread adoption for the structural characterization of macromolecular complexes. Routine analysis of biotherapeutics by this technique has become commonplace to assist in the development and quality control of immunoglobulin antibodies. Concurrently, 193 nm ultraviolet photodissociation (UVPD) has been developed as a structurally sensitive ion activation technique capable of interrogating protein conformational changes. Here, UVPD was applied to probe the paratopes of nanobodies, a class of single-domain antibodies with an expansive set of applications spanning affinity reagents, molecular imaging, and biotherapeutics. Comparing UVPD sequence fragments for the free nanobodies versus nanobody·antigen complexes empowered assignment of nanobody paratopes and intermolecular salt-bridges, elevating the capabilities of UVPD as a new strategy for characterization of nanobodies.

Ultraviolet photodissociation mass spectrometry is used to probe the paratopes of nanobodies, a class of single-domain antibodies, and to determine intersubunit salt-bridges and explore the nanobody·antigen interfaces.

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.

Surface-induced Dissociation Mass Spectrometry as a Structural Biology Tool

Native mass spectrometry (nMS) is evolving into a workhorse for structural biology. The plethora of online and offline preparation, separation, and purification methods as well as numerous ionization techniques combined with powerful new hybrid ion mobility and mass spectrometry systems has illustrated the great potential of nMS for structural biology. Fundamental to the progression of nMS has been the development of novel activation methods for dissociating proteins and protein complexes to deduce primary, secondary, tertiary, and quaternary structure through the combined use of multiple MS/MS technologies. This review highlights the key features and advantages of surface collisions (surface-induced dissociation, SID) for probing the connectivity of subunits within protein and nucleoprotein complexes and, in particular, for solving protein structure in conjunction with complementary techniques such as cryo-EM and computational modeling. Several case studies highlight the significant role SID, and more generally nMS, will play in structural elucidation of biological assemblies in the future as the technology becomes more widely adopted. Cases are presented where SID agrees with solved crystal or cryoEM structures or provides connectivity maps that are otherwise inaccessible by "gold standard" structural biology techniques.

Accounting for Neighboring Residue Hydrophobicity in Diethylpyrocarbonate Labeling Mass Spectrometry Improves Rosetta Protein Structure Prediction

Covalent labeling mass spectrometry allows for protein structure elucidation via covalent modification and identification of exposed residues. Diethylpyrocarbonate (DEPC) is a commonly used covalent labeling reagent that provides insight into structure through the labeling of lysine, histidine, serine, threonine, and tyrosine residues. We recently implemented a Rosetta algorithm that used binary DEPC labeling data to improve protein structure prediction efforts. In this work, we improved on our modeling efforts by accounting for the level of hydrophobicity of neighboring residues in the microenvironment of serine, threonine, and tyrosine residues to obtain a more accurate estimate of the hydrophobic neighbor count. This was incorporated into Rosetta functionality, along with considerations for solvent-exposed histidine and lysine residues. Overall, our new Rosetta score term successfully identified best scoring models with less than 2 Å root-mean-squared deviations (RMSDs) for five of the seven benchmark proteins tested. We additionally developed a confidence metric to measure prediction success for situations in which a native structure is unavailable.

Low-energy electron holography imaging of conformational variability of single-antibody molecules from electrospray ion beam deposition

Molecular imaging at the single-molecule level of large and flexible proteins such as monoclonal IgG antibodies is possible by low-energy electron holography after chemically selective sample preparation by native electrospray ion beam deposition (ES-IBD) from native solution conditions. The single-molecule nature of the measurement allows the mapping of the structural variability of the molecules that originates from their intrinsic flexibility and from different adsorption geometries. Additionally, we can distinguish gas-phase–related conformations and conformations induced by the landing of the molecules on the surface. Our results underpin the relation between the gas-phase structure of protein ions created by native electrospray ionization (ESI) and the native protein structure and are of relevance for structural biology applications in the gas phase.

Imaging of proteins at the single-molecule level can reveal conformational variability, which is essential for the understanding of biomolecules. To this end, a biologically relevant state of the sample must be retained during both sample preparation and imaging. Native electrospray ionization (ESI) can transfer even the largest protein complexes into the gas phase while preserving their stoichiometry and overall shape. High-resolution imaging of protein structures following native ESI is thus of fundamental interest for establishing the relation between gas phase and solution structure. Taking advantage of low-energy electron holography’s (LEEH) unique capability of imaging individual proteins with subnanometer resolution, we investigate the conformational flexibility of Herceptin, a monoclonal IgG antibody, deposited by native electrospray mass-selected ion beam deposition (ES-IBD) on graphene. Images reconstructed from holograms reveal a large variety of conformers. Some of these conformations can be mapped to the crystallographic structure of IgG, while others suggest that a compact, gas-phase–related conformation, adopted by the molecules during ES-IBD, is retained. We can steer the ratio of those two types of conformations by changing the landing energy of the protein on the single-layer graphene surface. Overall, we show that LEEH can elucidate the conformational heterogeneity of inherently flexible proteins, exemplified here by IgG antibodies, and thereby distinguish gas-phase collapse from rearrangement on surfaces.

Influence of Primary Structure on Fragmentation of Native-Like Proteins by Ultraviolet Photodissociation

Analysis of native-like protein structures in the gas phase via native mass spectrometry and auxiliary techniques has become a powerful tool for structural biology applications. In combination with ultraviolet photodissociation (UVPD), native top-down mass spectrometry informs backbone flexibility, topology, hydrogen bonding networks, and conformational changes in protein structure. Although it is known that the primary structure affects dissociation of peptides and proteins in the gas phase, its effect on the types and locations of backbone cleavages promoted by UVPD and concomitant influence on structural characterization of native-like proteins is not well understood. Here, trends in the fragmentation of native-like proteins were evaluated by tracking the propensity of 10 fragment types (a, a+1, b, c, x, x+1, y, y-1, Y, and z) in relation to primary structure in a native-top down UVPD data set encompassing >9600 fragment ions. Differing fragmentation trends are reported for the production of distinct fragment types, attributed to a combination of both direct dissociation pathways from excited electronic states and those surmised to involve intramolecular vibrational energy redistribution after internal conversion. The latter pathways were systematically evaluated to evince the role of proton mobility in the generation of "CID-like" fragments through UVPD, providing pertinent insight into the characterization of native-like proteins. Fragmentation trends presented here are envisioned to enhance analysis of the protein higher-order structure or augment scoring algorithms in the high-throughput analysis of intact proteins.

Protein structure prediction using residue-resolved protection factors from hydrogen-deuterium exchange NMR

Hydrogen-deuterium exchange (HDX) measured by nuclear magnetic resonance (NMR) provides structural information for proteins relating to solvent accessibility and flexibility. While this structural information is beneficial, the data cannot be used exclusively to elucidate structures. However, the structural information provided by the HDX-NMR data can be supplemented by computational methods. In previous work, we developed an algorithm in Rosetta to predict structures using qualitative HDX-NMR data (categories of exchange rate). Here we expand on the effort, and utilize quantitative protection factors (PFs) from HDX-NMR for structure prediction. From observed correlations between PFs and solvent accessibility/flexibility measures, we present a scoring function to quantify the agreement with HDX data. Using a benchmark set of 10 proteins, an average improvement of 5.13 Å in root-mean-square deviation (RMSD) is observed for cases of inaccurate Rosetta predictions. Ultimately, seven out of 10 predictions are accurate without including HDX data, and nine out of 10 are accurate when using our PF-based HDX score.

Protein Structure Prediction with Mass Spectrometry Data

Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen-deuterium exchange, hydroxyl radical protein footprinting, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Utilization of Hydrophobic Microenvironment Sensitivity in Diethylpyrocarbonate Labeling for Protein Structure Prediction

Diethylpyrocarbonate (DEPC) labeling analyzed with mass spectrometry can provide important insights into higher order protein structures. It has been previously shown that neighboring hydrophobic residues promote a local increase in DEPC concentration such that serine, threonine, and tyrosine residues are more likely to be labeled despite low solvent exposure. In this work, we developed a Rosetta algorithm that used the knowledge of labeled and unlabeled serine, threonine, and tyrosine residues and assessed their local hydrophobic environment to improve protein structure prediction. Additionally, DEPC-labeled histidine and lysine residues with higher relative solvent accessible surface area values (i.e., more exposed) were scored favorably. Application of our score term led to reductions of the root-mean-square deviations (RMSDs) of the lowest scoring models. Additionally, models that scored well tended to have lower RMSDs. A detailed tutorial describing our protocol and required command lines is included. Our work demonstrated the considerable potential of DEPC covalent labeling data to be used for accurate higher order structure determination.

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.

Protein Structure Prediction from NMR Hydrogen-Deuterium Exchange Data

Amide hydrogen-deuterium exchange (HDX) has long been used to determine regional flexibility and binding sites in proteins; however, the data are too sparse for full structural characterization. Experiments that measure HDX rates, such as HDX-NMR, have far higher throughput compared to structure determination via X-ray crystallography, cryo-EM, or a full suite of NMR experiments. Data from HDX-NMR experiments encode information on the protein structure, making HDX a prime candidate to be supplemented by computational algorithms for protein structure prediction. We have developed a methodology to incorporate HDX-NMR data into ab initio protein structure prediction using the Rosetta software framework to predict structures based on experimental agreement. To demonstrate the efficacy of our algorithm, we examined 38 proteins with HDX-NMR data available, comparing the predicted model with and without the incorporation of HDX data into scoring. The root-mean-square deviation (rmsd, a measure of the average atomic distance between superimposed models) of the predicted model improved by 1.42 Å on average after incorporating the HDX-NMR data into scoring. The average rmsd improvement for the proteins where the selected model rmsd changed after incorporating HDX data was 3.63 Å, including one improvement of more than 11 Å and seven proteins improving by greater than 4 Å, with 12/15 proteins improving overall. Additionally, for independent verification, two proteins that were not part of the original benchmark were scored including HDX data, with a dramatic improvement of the selected model rmsd of nearly 9 Å for one of the proteins. Moreover, we have developed a confidence metric allowing us to successfully identify near-native models in the absence of a native structure. Improvement in model selection with a strong confidence measure demonstrates that protein structure prediction with HDX-NMR is a powerful tool which can be performed with minimal additional computational strain and expense.

Accurate protein structure prediction with hydroxyl radical protein footprinting data

Hydroxyl radical protein footprinting (HRPF) in combination with mass spectrometry reveals the relative solvent exposure of labeled residues within a protein, thereby providing insight into protein tertiary structure. HRPF labels nineteen residues with varying degrees of reliability and reactivity. Here, we are presenting a dynamics-driven HRPF-guided algorithm for protein structure prediction. In a benchmark test of our algorithm, usage of the dynamics data in a score term resulted in notable improvement of the root-mean-square deviations of the lowest-scoring ab initio models and improved the funnel-like metric Pnear for all benchmark proteins. We identified models with accurate atomic detail for three of the four benchmark proteins. This work suggests that HRPF data along with side chain dynamics sampled by a Rosetta mover ensemble can be used to accurately predict protein structure.

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.

Mass Spectrometry-Based Protein Footprinting for Higher-Order Structure Analysis: Fundamentals and Applications

Proteins adopt different higher-order structures (HOS) to enable their unique biological functions. Understanding the complexities of protein higher-order structures and dynamics requires integrated approaches, where mass spectrometry (MS) is now positioned to play a key role. One of those approaches is protein footprinting. Although the initial demonstration of footprinting was for the HOS determination of protein/nucleic acid binding, the concept was later adapted to MS-based protein HOS analysis, through which different covalent labeling approaches "mark" the solvent accessible surface area (SASA) of proteins to reflect protein HOS. Hydrogen-deuterium exchange (HDX), where deuterium in D2O replaces hydrogen of the backbone amides, is the most common example of footprinting. Its advantage is that the footprint reflects SASA and hydrogen bonding, whereas one drawback is the labeling is reversible. Another example of footprinting is slow irreversible labeling of functional groups on amino acid side chains by targeted reagents with high specificity, probing structural changes at selected sites. A third footprinting approach is by reactions with fast, irreversible labeling species that are highly reactive and footprint broadly several amino acid residue side chains on the time scale of submilliseconds. All of these covalent labeling approaches combine to constitute a problem-solving toolbox that enables mass spectrometry as a valuable tool for HOS elucidation. As there has been a growing need for MS-based protein footprinting in both academia and industry owing to its high throughput capability, prompt availability, and high spatial resolution, we present a summary of the history, descriptions, principles, mechanisms, and applications of these covalent labeling approaches. Moreover, their applications are highlighted according to the biological questions they can answer. This review is intended as a tutorial for MS-based protein HOS elucidation and as a reference for investigators seeking a MS-based tool to address structural questions in protein science.

Using NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD Protein Structure Refinement

Cryo-EM has become one of the prime methods for protein structure elucidation, frequently yielding density maps with near-atomic or medium resolution. If protein structures cannot be deduced unambiguously from the density maps, computational structure refinement tools are needed to generate protein structural models. We have previously developed an iterative Rosetta-MDFF protocol that used cryo-EM densities to refine protein structures. Here we show that, in addition to cryo-EM densities, incorporation of other experimental restraints into the Rosetta-MDFF protocol further improved refined structures. We used NMR chemical shift (CS) data integrated with cryo-EM densities in our hybrid protocol in both the Rosetta step and the molecular dynamics (MD) simulations step. In 15 out of 18 cases for all MD rounds, the refinement results obtained when density maps and NMR chemical shift data were used in combination outperformed those of density map-only refinement. Notably, the improvement in refinement was highest when medium and low-resolution density maps were used. With our hybrid method, the RMSDs of final models obtained were always better than the RMSDs obtained by our previous protocol with just density refinement for both medium (6.9 Å) and low (9 Å) resolution maps. For all the six test proteins with medium resolution density maps (6.9 Å), the final refined structure RMSDs were lower for the hybrid method than for the cryo-EM only refinement. The final refined RMSDs were less than 1.5 Å when our hybrid protocol was used with 4 Å density maps. For four out of the six proteins the final RMSDs were even less than 1 Å. This study demonstrates that by using a combination of cryo-EM and NMR restraints, it is possible to refine structures to atomic resolution, outperforming single restraint refinement. This hybrid protocol will be a valuable tool when only low-resolution cryo-EM density data and NMR chemical shift data are available to refine structures.

Charge Movement and Structural Changes in the Gas-Phase Unfolding of Multimeric Protein Complexes Captured by Native Top-Down Mass Spectrometry

The extent to which noncovalent protein complexes retain native structure in the gas phase is highly dependent on experimental conditions. Energetic collisions with background gas can cause structural changes ranging from unfolding to subunit dissociation. Additionally, recent studies have highlighted the role of charge in such structural changes, but the mechanism is not completely understood. In this study, native top down (native TD) mass spectrometry was used to probe gas-phase structural changes of alcohol dehydrogenase (ADH, 4mer) under varying degrees of in-source activation. Changes in covalent backbone fragments produced by electron capture dissociation (ECD) or 193 nm ultraviolet photodissociation (UVPD) were attributed to structural changes of the ADH 4mer. ECD fragments indicated unfolding started at the N-terminus, and the charge states of UVPD fragments enabled monitoring of charge migration to the unfolded regions. Interestingly, UVPD fragments also indicated that the charge at the "unfolding" N-terminus of ADH decreased at high in-source activation energies after the initial increase. We proposed a possible "refolding-after-unfolding" mechanism, as further supported by monitoring hydrogen elimination from radical a-ions produced by UVPD at the N-terminus of ADH. However, "refolding-after-unfolding" with increasing in-source activation was not observed for charge-reduced ADH, which likely adopted compact structures that are resistant to both charge migration and unfolding. When combined, these results support a charge-directed unfolding mechanism for protein complexes. Overall, an experimental framework was outlined for utilizing native TD to generate structure-informative mass spectral signatures for protein complexes that complement other structure characterization techniques, such as ion mobility and computational modeling.

Native mass spectrometry: a powerful tool for structural biology?

Mass spectrometry has been used for decades and continues to be an integral part of analytical research. This feature explores its latest applications.