Hydrogen/deuterium Exchange Mass Spectrometry Data Research Articles

RosettaHDX: Predicting antibody-antigen interaction from hydrogen-deuterium exchange mass spectrometry data.

High-throughput characterization of antibody-antigen complexes at the atomic level is critical for understanding antibody function and enabling therapeutic development. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) enables rapid epitope mapping, but its data are too sparse for independent structure determination. In this study, we introduce RosettaHDX, a hybrid method that combines computational docking with differential HDX-MS data to enhance the accuracy of antibody-antigen complex models beyond what either method can achieve individually. By incorporating HDX data as both distance restraints and a scoring term in the RosettaDock algorithm, RosettaHDX successfully generated near-native models (interface root-mean square deviation≤4 Å) for all 9 benchmark complexes examined, averaging 3.6 times more near-native models than Rosetta alone. Near-native models among the top 10 scoring were identified in 3/9 cases, compared to 1/9 with Rosetta alone. Additionally, we developed a predictive metric based on docking results with HDX restraints to identify allosteric peptides in HDX datasets.

Integration of Hydrogen-Deuterium Exchange Mass Spectrometry with Molecular Dynamics Simulations and Ensemble Reweighting Enables High Resolution Protein-Ligand Modeling.

Hydrogen-Deuterium exchange mass spectrometry's (HDX-MS) utility in identifying and characterizing protein-small molecule interaction sites has been established. The regions that are seen to be protected from exchange upon ligand binding indicate regions that may be interacting with the ligand, giving a qualitative understanding of the ligand binding pocket. However, quantitatively deriving an accurate high-resolution structure of the protein-ligand complex from the HDX-MS data remains a challenge, often limiting its use in applications such as small molecule drug design. Recent efforts have focused on the development of methods to quantitatively model Hydrogen-Deuterium exchange (HDX) data from computationally modeled structures to garner atomic level insights from peptide-level resolution HDX-MS. One such method, HDX ensemble reweighting (HDXer), employs maximum entropy reweighting of simulated HDX data to experimental HDX-MS to model structural ensembles. In this study, we implement and validate a workflow which quantitatively leverages HDX-MS data to accurately model protein-small molecule ligand interactions. To that end, we employ a strategy combining computational protein-ligand docking, molecular dynamics simulations, HDXer, and dimensional reduction and clustering approaches to extract high-resolution drug binding poses that most accurately conform with HDX-MS data. We apply this workflow to model the interaction of ERK2 and FosA with small molecule compounds and inhibitors they are known to bind. In five out of six of the protein-ligand pairs tested, the HDX derived protein-ligand complexes result in a ligand root-mean-square deviation (RMSD) within 2.5 Å of the known crystal structure ligand.

HDXBoxeR: an R package for statistical analysis and visualization of multiple Hydrogen-Deuterium Exchange Mass-Spectrometry datasets of different protein states.

Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS) is a powerful protein characterization technique that provides insights into protein dynamics and flexibility at the peptide level. However, analyzing HDX-MS data presents a significant challenge due to the wealth of information it generates. Each experiment produces data for hundreds of peptides, often measured in triplicate across multiple time points. Comparisons between different protein states create distinct datasets containing thousands of peptides that require matching, rigorous statistical evaluation, and visualization. Our open-source R package, HDXBoxeR, is a comprehensive tool designed to facilitate statistical analysis and comparison of multiple sets among samples and time points for different protein states, along with data visualization. HDXBoxeR is accessible as the R package (https://cran.r-project.org/web//packages/HDXBoxeR) and GitHub: mkajano/HDXBoxeR.

Interpretation of Hydrogen/Deuterium Exchange Mass Spectrometry.

This paper sheds light on the meaning of hydrogen/deuterium exchange-mass spectrometry (HDX-MS) data. HDX-MS data provide not structural information but dynamic information on an analyte protein. First, the reaction mechanism of backbone amide HDX reaction is considered and the correlation between the parameters from an X-ray crystal structure and the protection factors of HDX reactions of cytochrome c is evaluated. The presence of H-bonds in a protein structure has a strong influence on HDX rates which represent protein dynamics, while the solvent accessibility only weakly affects the HDX rates. Second, the energy diagrams of the HDX reaction at each residue in the presence and absence of perturbation are described. Whereas the free energy change upon mutation can be directly measured by the HDX rates, the free energy change upon ligand binding may be complicated due to the presence of unbound analyte protein in the protein-ligand mixture. Third, the meanings of HDX and other biophysical techniques are explained using a hypothetical protein folding well. The shape of the protein folding well describes the protein dynamics and provides Boltzmann distribution of open and closed states which yield HDX protection factors, while a protein's crystal structure represents a snapshot near the bottom of the well. All biophysical data should be consistent yet provide different information because they monitor different parts of the same protein folding well.

Cyclic Ion Mobility for Hydrogen/Deuterium Exchange-Mass Spectrometry Applications.

Hydrogen/deuterium exchange-mass spectrometry (HDX-MS) has emerged as a powerful tool to probe protein dynamics. As a bottom-up technique, HDX-MS provides information at peptide-level resolution, allowing structural localization of dynamic changes. Consequently, the HDX-MS data quality is largely determined by the number of peptides that are identified and monitored after deuteration. Integration of ion mobility (IM) into HDX-MS workflows has been shown to increase the data quality by providing an orthogonal mode of peptide ion separation in the gas phase. This is of critical importance for challenging targets such as integral membrane proteins (IMPs), which often suffer from low sequence coverage or redundancy in HDX-MS analyses. The increasing complexity of samples being investigated by HDX-MS, such as membrane mimetic reconstituted and in vivo IMPs, has generated need for instrumentation with greater resolving power. Recently, Giles et al. developed cyclic ion mobility (cIM), an IM device with racetrack geometry that enables scalable, multipass IM separations. Using one-pass and multipass cIM routines, we use the recently commercialized SELECT SERIES Cyclic IM spectrometer for HDX-MS analyses of four detergent solubilized IMP samples and report its enhanced performance. Furthermore, we develop a novel processing strategy capable of better handling multipass cIM data. Interestingly, use of one-pass and multipass cIM routines produced unique peptide populations, with their combined peptide output being 31 to 222% higher than previous generation SYNAPT G2-Si instrumentation. Thus, we propose a novel HDX-MS workflow with integrated cIM that has the potential to enable the analysis of more complex systems with greater accuracy and speed.

Molecular regulation of Mycobacterium tuberculosis Sigma factor H with Anti-sigma factor RshA under stress condition

Mycobacterium tuberculosis is the causative agent of tuberculosis, the leading fatal infectious disease that claims millions of lives every year. M. tuberculosis regulates its stress condition response using its regulatory protein, Sigma Factor H, which binds with its cognate anti-sigma factor RshA in normal conditions, forming a complex inhibiting transcription. During oxidative stress, SigH is released from the complex and binds to RNA Polymerase (RNAP) to initiate transcription. Thus, it is important to understand the molecular conformational state of SigH in complex with different protein partners under different cellular or environmental contexts. This work intends to analyze the SigH-RshA complex, which revealed the variation in SigH shown during complex formation with RNAP and RshA, respectively. Previously, Hydrogen Deuterium Exchange-Mass Spectrometry (HDX-MS) analysis of SigH-RshA interaction provided a detailed insight into the critical residues participating in the interaction. The HDX-MS data were used to dock RshA on the open conformation of SigH from the SigH-RNAP complex structure (PDB: 5ZX2), and closed conformation was obtained from protein modelling. The docking revealed that closed conformation of SigH complexing with RshA in terms of HDX-MS data revealed a major structural shift in SigH while interacting with two different binding partners, RshA and RNAP, under variable environmental conditions. This structural shift of SigH with RshA and RNAP has significance in understanding the stress response of M. tuberculosis, and SigH could prove to be a potential drug target.

Deep Learning Enables Automatic Correction of Experimental HDX-MS Data with Applications in Protein Modeling.

Observed mass shifts associated with deuterium incorporation in hydrogen-deuterium exchange mass spectrometry (HDX-MS) frequently deviate from the initial signals due to back and forward exchange. In typical HDX-MS experiments, the impact of these disparities on data interpretation is generally low because relative and not absolute mass changes are investigated. However, for more advanced data processing including optimization, experimental error correction is imperative for accurate results. Here the potential for automatic HDX-MS data correction using models generated by deep neural networks is demonstrated. A multilayer perceptron (MLP) is used to learn a mapping between uncorrected HDX-MS data and data with mass shifts corrected for back and forward exchange. The model is rigorously tested at various levels including peptide level mass changes, residue level protection factors following optimization, and ability to correctly identify native protein folds using HDX-MS guided protein modeling. AI is shown to demonstrate considerable potential for amending HDX-MS data and improving fidelity across all levels. With access to big data, online tools may eventually be able to predict corrected mass shifts in HDX-MS profiles. This should improve throughput in workflows that require the reporting of real mass changes as well as allow retrospective correction of historic profiles to facilitate new discoveries with these data.

Toxoplasma gondii mitochondrial association factor 1b interactome reveals novel binding partners including Ral GTPase accelerating protein α1

The intracellular parasite, Toxoplasma gondii, has developed sophisticated molecular strategies to subvert host processes and promote growth and survival. During infection, T.gondii replicates in a parasitophorous vacuole (PV) and modulates host functions through a network of secreted proteins. Of these, Mitochondrial Association Factor 1b (MAF1b) recruits host mitochondria to the PV, a process that confers an invivo growth advantage, though the precise mechanisms remain enigmatic. To address this knowledge gap, we mapped the MAF1b interactome in human fibroblasts using a commercial Yeast-2-hybrid (Y2H) screen, which revealed several previously unidentified binding partners including the GAP domain of Ral GTPase Accelerating Protein α1 (RalGAPα1(GAP)). Recombinantly produced MAF1b and RalGAPα1(GAP) formed as a stable binary complex as shown by size exclusion chromatography with a Kd of 334nM as measured by isothermal titration calorimetry (ITC). Notably, no binding was detected between RalGAPα1(GAP) and the structurally conserved MAF1b homolog, MAF1a, which does not recruit host mitochondria. Next, we used hydrogen deuterium exchange mass spectrometry (HDX-MS) to map the RalGAPα1(GAP)-MAF1b interface, which led to identification of the "GAP-binding loop" on MAF1b that was confirmed by mutagenesis and ITC to be necessary for complex formation. A high-confidence Alphafold model predicts the GAP-binding loop to lie at the RalGAPα1(GAP)-MAF1b interface further supporting the HDX-MS data. Mechanistic implications of a RalGAPα1(GAP)-MAF1b complex are discussed in the context of T.gondii infection and indicates that MAF1b may have evolved multiple independent functions to increase T.gondii fitness.

A Functional Bayesian Model for Hydrogen-Deuterium Exchange Mass Spectrometry.

Proteins often undergo structural perturbations upon binding to other proteins or ligands or when they are subjected to environmental changes. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) can be used to explore conformational changes in proteins by examining differences in the rate of deuterium incorporation in different contexts. To determine deuterium incorporation rates, HDX-MS measurements are typically made over a time course. Recently introduced methods show that incorporating the temporal dimension into the statistical analysis improves power and interpretation. However, these approaches have technical assumptions that hinder their flexibility. Here, we propose a more flexible methodology by reframing these methods in a Bayesian framework. Our proposed framework has improved algorithmic stability, allows us to perform uncertainty quantification, and can calculate statistical quantities that are inaccessible to other approaches. We demonstrate the general applicability of the method by showing it can perform rigorous model selection on a spike-in HDX-MS experiment, improved interpretation in an epitope mapping experiment, and increased sensitivity in a small molecule case-study. Bayesian analysis of an HDX experiment with an antibody dimer bound to an E3 ubiquitin ligase identifies at least two interaction interfaces where previous methods obtained confounding results due to the complexities of conformational changes on binding. Our findings are consistent with the cocrystal structure of these proteins, demonstrating a bayesian approach can identify important binding epitopes from HDX data. We also generate HDX-MS data of the bromodomain-containing protein BRD4 in complex with GSK1210151A to demonstrate the increased sensitivity of adopting a Bayesian approach.

Mapping HDX-MS Data to Protein Conformations through Training Ensemble-Based Models.

An original approach that adopts machine learning inference to predict protein structural information using hydrogen-deuterium exchange mass spectrometry (HDX-MS) is described. The method exploits an in-house optimization program that increases the resolution of HDX-MS data from peptides to amino acids. A system is trained using Gradient Tree Boosting as a type of machine learning ensemble technique to assign a protein secondary structure. Using limited training data we generate a discriminative model that uses optimized HDX-MS data to predict protein secondary structure with an accuracy of 75%. This research could form the basis for new methods exploiting artificial intelligence to model protein conformations by HDX-MS.

Hydrogen/deuterium exchange-mass spectrometry of integral membrane proteins in native-like environments: current scenario and the way forward.

Integral membrane proteins (IMPs) perform a range of diverse functions and their dysfunction underlies numerous pathological conditions. Consequently, IMPs constitute most drug targets, and the elucidation of their mechanism of action has become an intense field of research. Historically, IMP studies have relied on their extraction from membranes using detergents, which have the potential to perturbate their structure and dynamics. To circumnavigate this issue, an array of membrane mimetics has been developed that aim to reconstitute IMPs into native-like lipid environments that more accurately represent the biological membrane. Hydrogen/deuterium exchange-mass spectrometry (HDX-MS) has emerged as a versatile tool for probing protein dynamics in solution. The continued development of HDX-MS methodology has allowed practitioners to investigate IMPs using increasingly native-like membrane mimetics, and even pushing the study of IMPs into the in vivo cellular environment. Consequently, HDX-MS has come of age and is playing an ever-increasingly important role in the IMP structural biologist toolkit. In the present mini-review, we discuss the evolution of membrane mimetics in the HDX-MS context, focusing on seminal publications and recent innovations that have led to this point. We also discuss state-of-the-art methodological and instrumental advancements that are likely to play a significant role in the generation of high-quality HDX-MS data of IMPs in the future.

Online Fully Automated System for Hydrogen/Deuterium-Exchange Mass Spectrometry with Millisecond Time Resolution.

Amide hydrogen/deuterium-exchange mass spectrometry (HDX-MS) is a powerful tool for analyzing the conformational dynamics of proteins in a solution. Current conventional methods have a measurement limit starting from several seconds and are solely reliant on the speed of manual pipetting or a liquid handling robot. Weakly protected regions of polypeptides, such as in short peptides, exposed loops and intrinsically disordered the protein exchange on the millisecond timescale. Typical HDX methods often cannot resolve the structural dynamics and stability in these cases. Numerous academic laboratories have demonstrated the considerable utility of acquiring HDX-MS data in the sub-second regimes. Here, we describe the development of a fully automated HDX-MS apparatus to resolve amide exchange on the millisecond timescale. Like conventional systems, this instrument boasts automated sample injection with software selection of labeling times, online flow mixing and quenching, while being fully integrated with a liquid chromatography-MS system for existing standard "bottom-up" workflows. HDX-MS's rapid exchange kinetics of several peptides demonstrate the repeatability, reproducibility, back-exchange, and mixing kinetics achieved with the system. Comparably, peptide coverage of 96.4% with 273 peptides was achieved, supporting the equivalence of the system to standard robotics. Additionally, time windows of 50 ms-300 s allowed full kinetic transitions to be observed for many amide groups; especially important are short time points (50-150 ms) for regions that are likely highly dynamic and solvent- exposed. We demonstrate that information on structural dynamics and stability can be measured for stretches of weakly stable polypeptides in small peptides and in local regions of a large enzyme, glycogen phosphorylase.

HDX-MS guided ensemble reweighting approach reveals cryptic drug binding sites in the cytoplasmic heme binding protein, PhuS.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is a valuable approach to probe protein structure and dynamics. However, interpretation of HDX-MS data is often qualitative and typically limited to peptide-level resolution. Ongoing efforts aim to integrate experimental HDX-MS data with molecular dynamics (MD) approaches, to provide a high-resolution interpretation of the HDX-MS data. One such approach is an HDX-MS based maximum-entropy reweighting approach (HDXer) to reweight computationally generated ensembles to HDX-MS data. Here, HDXer is used in conjunction with enhanced MD (eMD) to characterize the structural dynamics of PhuS. PhuS is a cytoplasmic heme binding protein from P. aeruginosa which shuttles exogenous heme to Heme Oxygenase (HemO) for degradation and is essential for virulence. Through the implementation of a workflow that integrates eMD, HDXer, and dimensional reduction, we had previously modeled the native state ensemble of apo-PhuS revealing a locally diverse apo-PhuS ensemble characterized by large rearrangements of helices surrounding the heme binding. Here, the representative structures from the modeled native ensemble were used as starting points for computer aided drug design (CADD). Implementing the Site Identification by Ligand Competitive Saturation (SILCS) CADD method approximately 1.3 million compounds were screened for each representative structure as well as the published apo PhuS crystal structure, yielding a library of 50 chemically representative candidate compounds. The candidate compounds were then screened using a fluorescence quenching assay. From these assays, several hits were confirmed to bind apo PhuS at nanomolar binding affinities. Most significantly, none of the compounds were derived from CADD using the published apo PhuS crystal structure, demonstrating the utility of HDX-MS guiding ensemble reweighting in revealing cryptic drug binding site in CADD approaches.

Applications of hydrogen deuterium exchange ensemble reweighting to model native protein ensembles, macromolecular interactions and protein drug interactions.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is a powerful approach to probe the structural dynamics of a protein in solution. It simultaneously provides time-resolved information on the structural environment of the amide hydrogen of every residue of a protein except Prolines. As such, HDX-MS has been successfully applied to provide insight into the characterization of native state ensembles of proteins, macromolecular interactions, protein-small molecule interactions or protein folding and unfolding studies. However, it remains largely limited to peptide-level resolution information. On the other hand, molecular dynamics (MD) simulations provide a high-resolution view of a protein's structural dynamics by simulating the motion of a protein's atoms over time starting from a high-resolution structure. However, the time scales accessible to MD simulations are limited and can lead to inadequate exploration of a protein's conformational landscape. To bridge this gap, efforts have aimed at developing quantitative and integrative approaches that leverage HDX-MS data to assess and refine computationally generated structural ensembles. HDX ensemble reweighting (HDXer) is one such approach which uses a post hoc maximum entropy approach to adjusts the weights of individual frames of a structural ensemble to conform to HDX-MS data. We had previously applied HDXer to model the native ensemble of the cytoplasmic heme binding protein, PhuS. Here, we have validated the proposed model ensemble by using structure-based drug design approach to successfully target novel, cryptic drug binding sites that would have otherwise not been targetable using HDX-MS or MD simulations alone. Finally, we demonstrate the broader applicability of such HDX-MS guided integrative approach to model macromolecular interactions, missing electron density and protein-small molecule drug interactions

Folding stability at the anion-binding site distinguishes prestin from other SLC26 transporters as revealed by HDX-MS.

The outer hair cell protein prestin (SLC26A5) responds to transmembrane voltage fluctuations by changing its cross-sectional area. This process, known as electromotility, is fundamental to mammalian cochlear amplification. Though prestin belongs to the SLC26 family of membrane transporters, it is the only member capable of displaying electromotility. Prestin's associated charge movement is driven by the helical dipole between TM3 and TM10, the charged residue R399, and the bound anion, but the relative roles remain to be determined. Here, we investigated these issues by interrogating the dynamics of prestin and SLC26A9, a typical SLC26 anion transporter, using hydrogen-deuterium exchange mass spectrometry (HDX-MS). We show that the folding stability of the anion-binding site of prestin is at least 2.8 kcal/mol weaker than SLC26A9 while the remaining regions have similar stability. Anion binding to prestin reversibly folds the binding site by joining the TM3 and TM10 helices, ensuing allosteric modulation of the TMD dynamics. In contrast, anion binding minimally affects the stability of SLC26A9, including the TM3-TM10 dielectric gap. We find helix fraying at the binding site but cooperative unfolding for multiple peripheral helices, with potential structural and functional relevance. Our HDX-MS data not only validate recent cryo-EM structures, but also highlight the unique role of the bound anion driving the folding of prestin's anion-binding site. This phenomenon may have implications to prestin's voltage-sensing mechanism in the outer hair cells.

Optimization of hydrogen-deuterium exchange ensemble reweighting (HDXer) to aid the biophysical characterization of proteins.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) experiments provide unique insights into native protein structure and dynamics in solution but have limited spatial resolution. Molecular dynamics (MD) simulations, in contrast, allow for the exploration of protein conformational landscapes with an atomistic level of resolution. However, forcefield inaccuracies, crystallographic biases, and timescale limitations may compromise the validity of the final structural ensemble. Hydrogen Deuterium eXchange Ensemble Reweighting (HDXer) is a maximum entropy approach that was recently developed to (1) quantitatively predict hydrogen-deuterium exchange from a modeled conformational ensemble and (2) refine the candidate ensemble to best fit an experimental HDX-MS dataset. Here, we compare how various computational and experimental parameters impact the performance of HDXer, using extracellular regulated kinase 2 (ERK2) and hen egg white lysozyme (HEWL) as model proteins. Conventional molecular dynamics simulations of ERK2 and HEWL were performed in triplicate using the CHARMM36m, AMBER FF19SB, and Drude forcefields. After predicting the HDX rates of each candidate ensemble, HDXer was used to fit the simulated structures to the corresponding HDX-MS datasets. To assess the influence of simulation length, truncated and strided copies of the full-length simulations were generated. Other factors, including peptide charge state selection, ion mobility mass spectrometry, and independent HDX-MS data acquisitions, were also tested. We find that while some variables (e.g., high vs. low peptide charge states) do not have a significant effect, other parameters (e.g., early vs. late timepoint selection) directly correlate with the disagreement between calculated and experimental deuterium uptake. The results from this benchmarking inform the development of a recommended HDXer workflow and best practices for optimal data acquisition.

The Deuterium Calculator: An Open-Source Tool for Hydrogen-Deuterium Exchange Mass Spectrometry Analysis.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is a powerful protein footprinting technique to study protein dynamics and binding; however, HDX-MS data analysis is often challenging and time-consuming. Moreover, the HDX community is expanding to investigate multiprotein and highly complex protein systems which further complicates data analysis. Thus, a simple, open-source software package designed to analyze large and highly complex protein systems is needed. In this vein, we have developed "The Deuterium Calculator", a Python-based software package for HDX-MS data analysis. The Deuterium Calculator is capable of differential and nondifferential HDX-MS analysis, produces standardized data files according to recommendations from the International Conference on Hydrogen-Exchange Mass Spectrometry (IC-HDX) to increase transparency in data analysis, and generates Woods' plots for statistical analysis and data visualization. This standard output can be used to perform time dependent deuteration studies and for the study of protein folding kinetics or differential uptake. Moreover, The Deuterium Calculator is capable of performing these analyses on large HDX-MS data sets (e.g., LC-HDX-MS from cell lysate digest). The Deuterium Calculator is freely available for download at https://github.com/OUWuLab/TheDeuteriumCalculator.git. Data are available via ProteomeXchange with identifier PXD036813.

Millisecond Hydrogen/Deuterium-Exchange Mass Spectrometry Approach to Correlate Local Structure and Aggregation in α-Synuclein.

In Parkinson's disease and other synucleinopathies, α-synuclein misfolds and aggregates. Its intrinsically disordered nature, however, causes it to adopt several meta-stable conformations stabilized by internal hydrogen bonding. Because they interconvert on short timescales, monomeric conformations of disordered proteins are difficult to characterize using common structural techniques. Few techniques can measure the conformations of monomeric α-synuclein, including millisecond hydrogen/deuterium-exchange mass spectrometry (HDX-MS). Here, we demonstrate a new approach correlating millisecond HDX-MS data with aggregation kinetics to determine the localized structural dynamics that underpin the self-assembly process in full-length wild-type monomeric α-synuclein. Our custom instrumentation and software enabled measurement of the amide hydrogen-exchange rates on the millisecond timescale for wild-type α-synuclein monomer up to residue resolution and under physiological conditions, mimicking those in the extracellular, intracellular, and lysosomal cellular compartments. We applied an empirical correction to normalize measured hydrogen-exchange rates and thus allow comparison between drastically different solution conditions. We characterized the aggregation kinetics and morphology of the resulting fibrils and correlate these with structural changes in the monomer. Applying a correlative approach to connect molecular conformation to aggregation in α-synuclein for the first time, we found that the central C-terminal residues of α-synuclein are driving its nucleation and thus its aggregation. We provide a new approach to link the local structural dynamics of intrinsically disordered proteins to functional attributes, which we evidence with new details on our current understanding of the relationship between the local chemical environment and conformational ensemble bias of monomeric α-synuclein.

Millisecond Hydrogen/Deuterium-Exchange Mass Spectrometry for the Study of Alpha-Synuclein Structural Dynamics Under Physiological Conditions.

Alpha-synuclein (aSyn) is an intrinsically disordered protein whose fibrillar aggregates are abundant in Lewy bodies and neurites, which are the hallmarks of Parkinson's disease. Yet, much of its biological activity, as well as its aggregation, centrally involves the soluble monomer form of the protein. Elucidation of the molecular mechanisms of aSyn biology and pathophysiology requires structurally highly resolved methods and is sensitive to biological conditions. Its natively unfolded, meta-stable structures make monomeric aSyn intractable to many structural biology techniques. Here, the application of one such approach is described: hydrogen/deuterium-exchange mass spectrometry (HDX-MS) on the millisecond timescale for the study of proteins with low thermodynamic stability and weak protection factors, such as aSyn. At the millisecond timescale, HDX-MS data contain information on the solvent accessibility and hydrogen-bonded structure of aSyn, which are lost at longer labeling times, ultimately yielding structural resolution up to the amino acid level. Therefore, HDX-MS can provide information at high structural and temporal resolutions on conformational dynamics and thermodynamics, intra- and inter-molecular interactions, and the structural impact of mutations or alterations to environmental conditions. While broadly applicable, it is demonstrated how to acquire, analyze, and interpret millisecond HDX-MS measurements in monomeric aSyn.

Computational Structure Prediction for Antibody-Antigen Complexes From Hydrogen-Deuterium Exchange Mass Spectrometry: Challenges and Outlook.

Although computational structure prediction has had great successes in recent years, it regularly fails to predict the interactions of large protein complexes with residue-level accuracy, or even the correct orientation of the protein partners. The performance of computational docking can be notably enhanced by incorporating experimental data from structural biology techniques. A rapid method to probe protein-protein interactions is hydrogen-deuterium exchange mass spectrometry (HDX-MS). HDX-MS has been increasingly used for epitope-mapping of antibodies (Abs) to their respective antigens (Ags) in the past few years. In this paper, we review the current state of HDX-MS in studying protein interactions, specifically Ab-Ag interactions, and how it has been used to inform computational structure prediction calculations. Particularly, we address the limitations of HDX-MS in epitope mapping and techniques and protocols applied to overcome these barriers. Furthermore, we explore computational methods that leverage HDX-MS to aid structure prediction, including the computational simulation of HDX-MS data and the combination of HDX-MS and protein docking. We point out challenges in interpreting and incorporating HDX-MS data into Ab-Ag complex docking and highlight the opportunities they provide to build towards a more optimized hybrid method, allowing for more reliable, high throughput epitope identification.