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

Exploring preferred binding domains of IgG1 mAbs to multimodal adsorbents using a combined biophysics and simulation approach

In this work, we employ a recently developed biophysical technique that uses diethylpyrocarbonate (DEPC) covalent labeling and mass spectrometry for the identification of mAb binding patches to two multimodal cation exchange resins at different pH. This approach compares the labeling results obtained in the bound and unbound states to identify residues that are sterically shielded and thus located in the mAb binding domains. The results at pH 6 for one mAb (mAb B) indicated that while the complementarity determining region (CDR) had minimal interactions with both resins, the FC domain was actively involved in binding. In contrast, DEPC/MS data with another mAb (mAb C) indicated that both the CDR and FC domains were actively involved in binding. These results corroborated chromatographic retention data with these two mAbs and their fragments and helped to explain the significantly stronger retention of both the intact mAb C and its Fab fragment. In contrast, labeling results with mAb C at pH 7, indicated that only the CDR played a significant role in resin binding, again corroborating chromatographic data. The binding domains identified from the DEPC/MS experiments were also examined using protein surface hydrophobicity maps obtained using a recently developed sparse sampling molecular dynamics (MD) approach in concert with electrostatic potential maps. These results demonstrate that the DEPC covalent labeling/mass spectrometry technique can provide important information about the domain contributions of multidomain proteins such as monoclonal antibodies when interacting with multimodal resins over a range of pH conditions.

Probing the functional hotspots inside protein hydrophobic pockets by in situ photochemical trifluoromethylation and mass spectrometry

Due to the complex high-order structures and interactions of proteins within an aqueous solution, a majority of chemical functionalizations happen on the hydrophilic sites of protein external surfaces which are naturally exposed to the solution. However, the hydrophobic pockets inside proteins are crucial for ligand binding and function as catalytic centers and transporting tunnels. Herein, we describe a reagent pre-organization and in situ photochemical trifluoromethylation strategy to profile the functional sites inside the hydrophobic pockets of native proteins. Unbiased mass spectrometry profiling was applied for the characterization of trifluoromethylated sites with high sensitivity. Native proteins including myoglobin, trypsin, haloalkane dehalogenase, and human serum albumin have been engaged in this mild photochemical process and substantial hydrophobic site-specific and structure-selective trifluoromethylation substitutes are obtained without significant interference to their bioactivity and structures. Sodium triflinate is the only reagent required to functionalize the unprotected proteins with wide pH-range tolerance and high biocompatibility. This "in-pocket" activation model provides a general strategy to modify the potential binding pockets and gain essential structural insights into the functional hotspots inside protein hydrophobic pockets.

Site-Specific Structural Changes in Long-Term-Stressed Monoclonal Antibody Revealed with DEPC Covalent-Labeling and Quantitative Mass Spectrometry

Studies of structural changes in mAbs under forced stress and storage conditions are essential for the recognition of degradation hotspots, which can be further remodeled to improve the stability of the respective protein. Herein, we used diethyl pyrocarbonate (DEPC)-based covalent labeling mass spectrometry (CL-MS) to assess structural changes in a model mAb (SILuMAb). Structural changes in the heat-stressed mAb samples were confirmed at specific amino acid positions from the DEPC label mass seen in the fragment ion mass spectrum. The degree of structural change was also quantified by increased or decreased DEPC labeling at specific sites; an increase or decrease indicated an unfolded or aggregated state of the mAb, respectively. Strikingly, for heat-stressed SILuMAb samples, an aggregation-prone area was identified in the CDR region. In the case of longterm stress, the structural consequences for SILuMAb samples stored for up to two years at 2-8 °C were studied with SEC-UV and DEPC-based CL-MS. While SEC-UV analysis only indicated fragmentation of SILuMAb, DEPC-based CL-MS analysis further pinpointed the finding to structural disturbances of disulfide bonds at specific cysteines. This emphasized the utility of DEPC CL-MS for studying disulfide rearrangement. Taken together, our data suggests that DEPC CL-MS can complement more technically challenging methods in the evaluation of the structural stability of mAbs.

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).

Workflow for Validating Specific Amino Acid Footprinting Reagents for Protein Higher Order Structure Elucidation

Protein footprinting mass spectrometry probes protein higher order structure and dynamics by labeling amino acid side-chains or backbone amides as a function of solvent accessibility. One category of footprinting uses residue-specific, irreversible covalent modifications, affording flexibility of sample processing for bottom-up analysis. Although several specific amino acid footprinting technologies are becoming established in structural proteomics, there remains a need to assess fundamental properties of new reagents before their application. Often, footprinting reagents are applied to complex or novel protein systems soon after their discovery and sometimes without a thorough investigation of potential downsides of the reagent. In this work, we assemble and test a validation workflow that utilizes cyclic peptides and a model protein to characterize benzoyl fluoride, a recently published, next-generation nucleophile footprinter. The workflow includes the characterization of potential side-chain reactive groups, reaction "quench" efficacies, reagent considerations and caveats (e.g., buffer pH), residue-specific kinetics compared to those of established reagents, and protein-wide characterization of modification sites with considerations for proteolysis. The proposed workflow serves as a starting point for improved footprinting reagent discovery, validation, and introduction, the aspects of which we recommend before applying to unknown protein systems.

Membrane Protein Binding Interactions Studied in Live Cells via Diethylpyrocarbonate Covalent Labeling Mass Spectrometry

Membrane proteins are vital in the human proteome for their cellular functions and make up a majority of drug targets in the U.S. However, characterizing their higher-order structures and interactions remains challenging. Most often membrane proteins are studied in artificial membranes, but such artificial systems do not fully account for the diversity of components present in cell membranes. In this study, we demonstrate that diethylpyrocarbonate (DEPC) covalent labeling mass spectrometry can provide binding site information for membrane proteins in living cells using membrane-bound tumor necrosis factor α (mTNFα) as a model system. Using three therapeutic monoclonal antibodies that bind TNFα, our results show that residues that are buried in the epitope upon antibody binding generally decrease in DEPC labeling extent. Additionally, serine, threonine, and tyrosine residues on the periphery of the epitope increase in labeling upon antibody binding because of a more hydrophobic microenvironment that is created. We also observe changes in labeling away from the epitope, indicating changes to the packing of the mTNFα homotrimer, compaction of the mTNFα trimer against the cell membrane, and/or previously uncharacterized allosteric changes upon antibody binding. Overall, DEPC-based covalent labeling mass spectrometry offers an effective means of characterizing structure and interactions of membrane proteins in living cells.

Evaluation of preferred binding regions on ubiquitin and IgG1 FC for interacting with multimodal cation exchange resins using DEPC labeling/mass spectrometry

There is significant interest in identifying the preferred binding domains of biological products to various chromatographic materials. In this work, we develop a biophysical technique that uses diethyl pyrocarbonate (DEPC) based covalent labeling in concert with enzymatic digestion and mass spectrometry to identify the binding patches for proteins bound to commercially available multimodal (MM) cation exchange chromatography resins. The technique compares the changes in covalent labeling of the protein in solution and in the bound state and uses the differences in this labeling to identify residues that are sterically shielded upon resin binding and, therefore, potentially involved in the resin binding process. Importantly, this approach enables the labeling of many amino acids and can be carried out over a pH range of 5.5-7.5, thus enabling the protein surface mapping at conditions of interest in MM cation exchange systems. The protocol is first developed using the model protein ubiquitin and the results indicate that lysine residues located on the front face of the protein show dramatic changes in DEPC labeling while residues present on other regions have minimal or no reductions. This indicates that the front face of ubiquitin is likely involved in resin binding. In addition, surface property maps indicate that the hypothesized front face binding region consists of overlapping positively charged and hydrophobic patches. The technique is then employed with an IgG1 F_C and the results indicate that residues on the C_H 2-C_H 3 interface and the hinge are significantly sterically shielded upon binding to the resin. Further, these regions are again associated with significant overlap of positively charged and hydrophobic patches. On the other hand, while, residues on the C_H 2 and the front face of the IgG1 F_C also exhibited some changes in DEPC labeling upon binding, these regions have less distinct charged and hydrophobic patches. Importantly, the hypothesized binding patches identified for both ubiquitin and F_C using this approach are shown to be consistent with previously reported NMR studies. In contrast to NMR, this new approach enables the identification of preferred binding regions without the need for isotopically labeled proteins or chemical shift assignments. The technique developed in this work sets the stage for the evaluation of the binding domains of a wide range of biological products to chromatographic surfaces, with important implications for designing biomolecules with improved biomanufacturability properties.

Diethylpyrocarbonate-Based Covalent Labeling Mass Spectrometry of Protein Interactions in a Membrane Complex System

Membrane-associated proteins are important because they mediate interactions between a cell's external and internal environment and they are often targets of therapeutics. Characterizing their structures and binding interactions, however, is challenging because they typically must be solubilized using artificial membrane systems that can make measurements difficult. Mass spectrometry (MS) is emerging as a valuable tool for studying membrane-associated proteins, and covalent labeling MS has unique potential to provide higher order structure and binding information for these proteins in complicated membrane systems. Here, we demonstrate that diethylpyrocarbonate (DEPC) can be effectively used as a labeling reagent to characterize the binding interactions between a membrane-associated protein and its binding partners in an artificial membrane system. Using chemotaxis histidine kinase (CheA) as a model system, we demonstrate that DEPC-based covalent labeling MS can provide structural and binding information about the ternary complex of CheA with two other proteins that is consistent with structural models of this membrane-associated chemoreceptor system. Despite the moderate hydrophobicity of DEPC, we find that its reactivity with proteins is not substantially influenced by the presence of the artificial membranes. However, correct structural information for this multiprotein chemoreceptor system requires measurements of DEPC labeling at multiple reagent concentrations to enable an accurate comparison between CheA and its ternary complex in the chemoreceptor system. In addition to providing structural information that is consistent with the model of this complex system, the labeling data supplements structural information that is not sufficiently refined in the chemoreceptor model.

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.

Modeling of protein conformational changes with Rosetta guided by limited experimental data

Conformational changes are an essential component of functional cycles of many proteins, but their characterization often requires an integrative structural biology approach. Here, we introduce and benchmark ConfChangeMover (CCM), a new method built into the widely used macromolecular modeling suite Rosetta that is tailored to model conformational changes in proteins using sparse experimental data. CCM can rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with simulated Cα-Cα distance restraints and sparse experimental double electron-electron resonance (DEER) restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. This method will contribute to the biophysical characterization of protein dynamics.

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 (CCS_IM). 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 CCS_IM 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.

Collision cross sections (CCS) from ion mobility mass spectrometry provide information about protein shape and size. Here, the authors develop an algorithm to predict CCS and integrate experimental ion mobility data into Rosetta-based molecular modelling to predict protein structures from sequence.

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.

Complementary Structural Information for Antibody-Antigen Complexes from Hydrogen-Deuterium Exchange and Covalent Labeling Mass Spectrometry

Characterizing antibody-antigen interactions is necessary for properly developing therapeutic antibodies, understanding their mechanisms of action, and patenting new drug molecules. Here, we demonstrate that hydrogen-deuterium exchange (HDX) mass spectrometry (MS) measurements together with diethylpyrocarbonate (DEPC) covalent labeling (CL) MS measurements provide higher order structural information about antibody-antigen interactions that is not available from either technique alone. Using the well-characterized model system of tumor necrosis factor α (TNFα) in complex with three different monoclonal antibodies (mAbs), we show that two techniques offer a more complete overall picture of TNFα's structural changes upon binding different mAbs, sometimes providing synergistic information about binding sites and changes in protein dynamics upon binding. Labeling decreases in CL generally occur near the TNFα epitope, whereas decreases in HDX can span the entire protein due to substantial stabilization that occurs when mAbs bind TNFα. Considering both data sets together clarifies the TNFα regions that undergo a decrease in solvent exposure due to mAb binding and that undergo a change in dynamics due to mAb binding. Moreover, the single-residue level resolution of DEPC-CL/MS can clarify HDX/MS data for long peptides. We feel that the two techniques should be used together when studying the mAb-antigen interactions because of the complementary information they provide.

Advances in mass spectrometry-based footprinting of membrane proteins

Structural biology is entering an exciting time where many new high-resolution structures of large complexes and membrane proteins (MPs) are determined regularly. These advances have been driven by over 15 years of technological improvements, first in macromolecular crystallography, and recently in cryo-electron microscopy. Obtaining information about MP higher order structure and interactions is also a frontier, important but challenging owing to their unique properties and the need to choose suitable detergents/lipids for their study. The development of mass spectrometry (MS), both instruments and methodology in the past 10 years, has also advanced it as a complementary method to study MP structure and interactions. In this review, we discuss advances in MS-based footprinting for MPs and highlight recent methodologies that offer new promise for MP study by chemical footprinting and mass spectrometry.

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.

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.