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

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).

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.

AlphaFold and Structural Mass Spectrometry Enable Interrogations on the Intrinsically Disordered Regions in Cyanobacterial Light-harvesting Complex Phycobilisome

Intrinsically disordered proteins/regions (IDPRs) are a very large and functionally important class of proteins that participate in weak multivalent interactions in protein complexes. They are recalcitrant for interrogations using X-ray crystallography and cryo-EM. The IDPRs observed at the interface of the photosynthetic pigment protein complexes (PPCs) remain much less clear, e.g., the major cyanobacterial light-harvesting complex (PBS) contains an unstructured PB-loop insertion in the phycocyanobilin domain (PB domain) of ApcE (the largest polypeptide in PBS). Here, a joint platform is built to probe such structural domains. This platform is characterized by two-round progressive justifications of in silico models by using the structural mass spectrometry data. First, the AlphaFold-generated 3D structure of the PB domain (containing PB-loop) was justified in the context of PBS. Second, docking the AlphaFold-generated ApcG (a ligand) into the first-step justified structure (a receptor). The final ligand-receptor complex was then subjected to a second-round justification, again, by using unequivocal isotopically-encoded cross-links identified in LC-MS/MS. This work reveals a full-length PB-loop structure modelled in the PBS basal cylinder, free from any spatial conflicts against the other subunits in PBS. The structure of PB domain highlights the close associations of the intrinsically disordered PB-loop with its binding partners in PBS, including ApcG, another IDPR. The PB-loop region involved in the binding of photosystem II (PSII) is also discussed in the context of excitation energy transfer regulation. This work calls attention to the highly disordered, yet interrogatable interface between the light-harvesting antenna complexes and the reaction centers.

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.

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.