Structural interpretation of hydrogen exchange protection factors in proteins: characterization of the native state fluctuations of CI2

The evolving role of hydrogen/deuterium exchange mass spectrometry in early-stage drug discovery

Protein function relies on protein dynamics and therefore dynamical information can be crucial for drug discovery of challenging protein targets. Assessing protein dynamics experimentally has traditionally been nontrivial. However, amide hydrogen/deuterium exchange mass spectrometry (HDX-MS) is now an established technique that can expose details about changes in protein dynamics, binding sites and allostery at the peptide level. HDX-MS is a versatile and powerful biophysical tool to gain insights into the workings of numerous protein systems and complexes. Advances in instrumentation, automation, data analysis, and interpretation over the past two decades have led to increased uptake and democratization of HDX-MS in both academic and industry settings. Here, we outline the current uses of HDX-MS in early-stage drug discovery and illustrate the interplay with computational chemistry to maximize the value of data obtained from HDX-MS experiments. Finally, we consider approaches which may aid structural interpretation of HDX-MS data in the coming years.

Structural dynamics of the dengue virus non-structural 5 (NS5) interactions with promoter stem-loop A (SLA)

The dengue virus (DENV) NS5 protein, essential for viral RNA synthesis, is an attractive antiviral drug target. DENV NS5 interacts with the stem-loop A (SLA) promoter at the 5'-untranslated region of the viral genome to initiate negative-strand synthesis. However, the conformational dynamics of this interaction remains unclear. Our study explores the structural dynamics of DENV serotype 2 NS5 (DENV2 NS5) in complex with SLA, employing surface plasmon resonance (SPR), hydrogen-deuterium exchange mass spectrometry (HDX-MS), computational modeling, and cryoEM. Our findings reveal that DENV2 NS5 binds SLA in a closed conformation, with interdomain cooperation between its methyltransferase (MTase) and RNA-dependent RNA polymerase (RdRp) domains, critical for the interaction. SLA binding induces conformational changes in both domains, highlighting NS5's multifunctional role in viral replication. Our cryoEM results visualizes the DENV2 NS5-SLA complex, confirming a conserved SLA binding across DENV serotypes and provides key insights for antiviral strategies targeting NS5's conformational states.

Structural Dynamics of the Dengue Virus Non-structural 5 (NS5) Interactions with Promoter Stem Loop A (SLA)

The dengue virus (DENV) NS5 protein plays a central role in dengue viral RNA synthesis which makes it an attractive target for antiviral drug development. DENV NS5 is known to interact with the stem-loop A (SLA) promoter at the 5'-untranslated region (5'-UTR) of the viral genome as a molecular recognition signature for the initiation of negative strand synthesis at the 3' end of the viral genome. However, the conformational dynamics involved in these interactions are yet to be fully elucidated. Our study explores the structural dynamics of NS5 from DENV serotype 2 (DENV2 NS5) in complex with SLA, employing surface plasmon resonance (SPR), hydrogen - deuterium exchange coupled to mass spectrometry (HDX-MS), computational modeling, and cryoEM single particle analysis to delineate the molecular details of their interaction. Our findings indicate that DENV2 NS5 binds SLA in a closed conformation with significant interdomain cooperation between the methyltransferase (MTase) and RNA-dependent RNA polymerase (RdRp) domains, a feature integral to the interaction. Our HDX-MS studies reveal SLA-induced conformational changes in both domains of DENV2 NS5, reflecting a potential mechanism for dengue NS5's multifunctional role in viral replication. Lastly, our cryoEM structure provides the first visualization of the DENV2 NS5-SLA complex, confirming a conserved SLA binding mode across DENV serotypes. These insights obtained from our study enhance our understanding of dengue NS5's complex conformational landscape, supporting the potential development of antiviral strategies targeting dengue NS5's conformational states.

Free energy landscape of the PI3Kα C-terminal activation

The gene PIK3CA, encoding the catalytic subunit p110α of PI3Kα, is the second most frequently mutated gene in cancer, with the highest frequency oncogenic mutants occurring in the C-terminus of the kinase domain. The C-terminus has a dual function in regulating the kinase, playing a putative auto-inhibitory role for kinase activity and being absolutely essential for binding to the cell membrane. However, the molecular mechanisms by which these C-terminal oncogenic mutations cause PI3Kα overactivation remain unclear. To understand how a spectrum of C-terminal mutations of PI3Kα alter kinase activity compared to the WT, we perform unbiased and biased Molecular Dynamics simulations of several C-terminal mutants and report the free energy landscapes for the C-terminal "closed-to-open" transition in the WT, H1047R, G1049R, M1043L and N1068KLKR mutants. Results are consistent with HDX-MS experimental data and provide a molecular explanation why H1047R and G1049R reorient the C-terminus with a different mechanism compared to the WT and M1043L and N1068KLKR mutants. Moreover, we show that in the H1047R mutant, the cavity, where the allosteric ligands STX-478 and RLY-2608 bind, is more accessible contrary to the WT. This study provides insights into the molecular mechanisms underlying activation of oncogenic PI3Kα by C-terminal mutations and represents a valuable resource for continued efforts in the development of mutant selective inhibitors as therapeutics.

Computational Tools for Hydrogen-Deuterium Exchange Mass Spectrometry Data Analysis

Hydrogen-deuterium exchange (HDX) has become a pivotal method for investigating the structural and dynamic properties of proteins. The versatility and sensitivity of mass spectrometry (MS) made the technique the ideal companion for HDX, and today HDX-MS is addressing a growing number of applications in both academic research and industrial settings. The prolific generation of experimental data has spurred the concurrent development of numerous computational tools, designed to automate parts of the workflow while employing different strategies to achieve common objectives. Various computational methods are available to perform automated peptide searches and identification; different statistical tests have been implemented to quantify differences in the exchange pattern between two or more experimental conditions; alternative strategies have been developed to deconvolve and analyze peptides showing multimodal behavior; and different algorithms have been proposed to computationally increase the resolution of HDX-MS data, with the ultimate aim to provide information at the level of the single residue. This review delves into a comprehensive examination of the merits and drawbacks associated with the diverse strategies implemented by software tools for the analysis of HDX-MS data.

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.

Hydrogen/Deuterium Exchange Mass Spectrometry: Fundamentals, Limitations, and Opportunities

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) probes dynamic motions of proteins by monitoring the kinetics of backbone amide deuteration. Dynamic regions exhibit rapid HDX, while rigid segments are more protected. Current data readouts focus on qualitative comparative observations (such as "residues X to Y become more protected after protein exposure to ligand Z"). At present, it is not possible to decode HDX protection patterns in an atomistic fashion. In other words, the exact range of protein motions under a given set of conditions cannot be uncovered, leaving space for speculative interpretations. Amide back exchange is an under-appreciated problem, as the widely used (m-m0)/(m100-m0) correction method can distort HDX kinetic profiles. Future data analysis strategies require a better fundamental understanding of HDX events, going beyond the classical Linderstrøm-Lang model. Combined with experiments that offer enhanced spatial resolution and suppressed back exchange, it should become possible to uncover the exact range of motions exhibited by a protein under a given set of conditions. Such advances would provide a greatly improved understanding of protein behavior in health and disease.

Conformational dynamics underlying atypical chemokine receptor 3 activation

Atypical Chemokine Receptor 3 (ACKR3) belongs to the G protein-coupled receptor family but it does not signal through G proteins. The structural properties that govern the functional selectivity and the conformational dynamics of ACKR3 activation are poorly understood. Here, we combined hydrogen/deuterium exchange mass spectrometry, site-directed mutagenesis, and molecular dynamics simulations to examine the binding mode and mechanism of action of ACKR3 ligands of different efficacies. Our results show that activation or inhibition of ACKR3 is governed by intracellular conformational changes of helix 6, intracellular loop 2, and helix 7, while the DRY motif becomes protected during both processes. Moreover, we identified the binding sites and the allosteric modulation of ACKR3 upon β-arrestin 1 binding. In summary, this study highlights the structure-function relationship of small ligands, the binding mode of β-arrestin 1, the activation dynamics, and the atypical dynamic features in ACKR3 that may contribute to its inability to activate G proteins.

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.

Gαs slow conformational transition upon GTP binding and a novel Gαs regulator

G proteins are major signaling partners for G protein-coupled receptors (GPCRs). Although stepwise structural changes during GPCR-G protein complex formation and guanosine diphosphate (GDP) release have been reported, no information is available with regard to guanosine triphosphate (GTP) binding. Here, we used a novel Bayesian integrative modeling framework that combines data from hydrogen-deuterium exchange mass spectrometry, tryptophan-induced fluorescence quenching, and metadynamics simulations to derive a kinetic model and atomic-level characterization of stepwise conformational changes incurred by the β2-adrenergic receptor (β2AR)-Gs complex after GDP release and GTP binding. Our data suggest rapid GTP binding and GTP-induced dissociation of Gαs from β2AR and Gβγ, as opposed to a slow closing of the Gαs α-helical domain (AHD). Yeast-two-hybrid screening using Gαs AHD as bait identified melanoma-associated antigen D2 (MAGE D2) as a novel AHD-binding protein, which was also shown to accelerate the GTP-induced closing of the Gαs AHD.

New experimental evidence for pervasive dynamics in proteins

There is ample computational, but only sparse experimental data suggesting that pico-ns motions with 1 Å amplitude are pervasive in proteins in solution. Such motions, if present in reality, must deeply affect protein function and protein entropy. Several NMR relaxation experiments have provided insights into motions of proteins in solution, but they primarily report on azimuthal angle variations of vectors of covalently-linked atoms. As such, these measurements are not sensitive to distance fluctuations, and cannot but under-represent the dynamical properties of proteins. Here we analyze a novel NMR relaxation experiment to measure amide proton transverse relaxation rates in uniformly 15 N labeled proteins, and present results for protein domain GB1 at 283 and 303 K. These relaxation rates depend on fluctuations of dipolar interactions between 1 HN and many nearby protons on both the backbone and sidechains. Importantly, they also report on fluctuations in the distances between these protons. We obtained a large mismatch between rates computed from the crystal structure of GB1 and the experimental rates. But when the relaxation rates were calculated from a 200 ns molecular dynamics trajectory using a novel program suite, we obtained a substantial improvement in the correspondence of experimental and theoretical rates. As such, this work provides novel experimental evidence of widespread motions in proteins. Since the improvements are substantial, but not sufficient, this approach may also present a new benchmark to help improve the theoretical forcefields underlying the molecular dynamics calculations.

The conformational landscape of a serpin N-terminal subdomain facilitates folding and in-cell quality control

Many multi-domain proteins including the serpin family of serine protease inhibitors contain non-sequential domains composed of regions that are far apart in sequence. Because proteins are translated vectorially from N- to C-terminus, such domains pose a particular challenge: how to balance the conformational lability necessary to form productive interactions between early and late translated regions while avoiding aggregation. This balance is mediated by the protein sequence properties and the interactions of the folding protein with the cellular quality control machinery. For serpins, particularly α 1 -antitrypsin (AAT), mutations often lead to polymer accumulation in cells and consequent disease suggesting that the lability/aggregation balance is especially precarious. Therefore, we investigated the properties of progressively longer AAT N-terminal fragments in solution and in cells. The N-terminal subdomain, residues 1-190 (AAT190), is monomeric in solution and efficiently degraded in cells. More β -rich fragments, 1-290 and 1-323, form small oligomers in solution, but are still efficiently degraded, and even the polymerization promoting Siiyama (S53F) mutation did not significantly affect fragment degradation. In vitro, the AAT190 region is among the last regions incorporated into the final structure. Hydrogen-deuterium exchange mass spectrometry and enhanced sampling molecular dynamics simulations show that AAT190 has a broad, dynamic conformational ensemble that helps protect one particularly aggregation prone β -strand from solvent. These AAT190 dynamics result in transient exposure of sequences that are buried in folded, full-length AAT, which may provide important recognition sites for the cellular quality control machinery and facilitate degradation and, under favorable conditions, reduce the likelihood of polymerization.

Recent Advances in NMR Protein Structure Prediction with ROSETTA

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.

Integrating Hydrogen Deuterium Exchange-Mass Spectrometry with Molecular Simulations Enables Quantification of the Conformational Populations of the Sugar Transporter XylE

A yet unresolved challenge in structural biology is to quantify the conformational states of proteins underpinning function. This challenge is particularly acute for membrane proteins owing to the difficulties in stabilizing them for in vitro studies. To address this challenge, we present an integrative strategy that combines hydrogen deuterium exchange-mass spectrometry (HDX-MS) with ensemble modeling. We benchmark our strategy on wild-type and mutant conformers of XylE, a prototypical member of the ubiquitous Major Facilitator Superfamily (MFS) of transporters. Next, we apply our strategy to quantify conformational ensembles of XylE embedded in different lipid environments. Further application of our integrative strategy to substrate-bound and inhibitor-bound ensembles allowed us to unravel protein-ligand interactions contributing to the alternating access mechanism of secondary transport in atomistic detail. Overall, our study highlights the potential of integrative HDX-MS modeling to capture, accurately quantify, and subsequently visualize co-populated states of membrane proteins in association with mutations and diverse substrates and inhibitors.

Exploring the state- and allele-specific conformational landscapes of Ras: understanding their respective druggabilities

Recent advances in direct inhibition of Ras benefit from the protein's intrinsic dynamic nature that derives therapeutically vulnerable conformers bearing transiently formed cryptic pockets. Hotspot mutants of Ras are major tumor drivers and are hyperactivated in cells at variable levels, which may require allele-specific strategies for effective targeting. However, it remains unclear how the prevalent oncogenic mutations and activation states perturb the free energy landscape governing the protein dynamics and druggability. Here we characterized the nucleotide state- and allele-dependent alterations of Ras conformational dynamics using a combined NMR experimental and computational approach and constructed quantitative ensembles revealing the conservation of the cryptic SI/II-P and SII-P pockets in different states and alleles. Highly local but critical conformational reorganizations that undermine the SII-P accessibility to residue 12 have been identified as a common mechanism resulting in the low reactivities of Ras·GTP as well as Ras(G12D)·GDP with covalent SII-P inhibitors. Our results strongly support the conformational selection scenario for interactions between Ras and the previously reported binders and offer insights for the future development of state- and allele-specific, as well as pan-Ras, inhibitors.

Insights into the evolution of enzymatic specificity and catalysis: From Asgard archaea to human adenylate kinases

Enzymatic catalysis is critically dependent on selectivity, active site architecture, and dynamics. To contribute insights into the interplay of these properties, we established an approach with NMR, crystallography, and MD simulations focused on the ubiquitous phosphotransferase adenylate kinase (AK) isolated from Odinarchaeota (OdinAK). Odinarchaeota belongs to the Asgard archaeal phylum that is believed to be the closest known ancestor to eukaryotes. We show that OdinAK is a hyperthermophilic trimer that, contrary to other AK family members, can use all NTPs for its phosphorylation reaction. Crystallographic structures of OdinAK-NTP complexes revealed a universal NTP-binding motif, while 19F NMR experiments uncovered a conserved and rate-limiting dynamic signature. As a consequence of trimerization, the active site of OdinAK was found to be lacking a critical catalytic residue and is therefore considered to be "atypical." On the basis of discovered relationships with human monomeric homologs, our findings are discussed in terms of evolution of enzymatic substrate specificity and cold adaptation.

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.

High-Resolution Hydrogen-Deuterium Protection Factors from Sparse Mass Spectrometry Data Validated by Nuclear Magnetic Resonance Measurements

Experimental measurement of time-dependent spontaneous exchange of amide protons with deuterium of the solvent provides information on the structure and dynamical structural variation in proteins. Two experimental techniques are used to probe the exchange: NMR, which relies on different magnetic properties of hydrogen and deuterium, and MS, which exploits the change in mass due to deuteration. NMR provides residue-specific information, that is, the rate of exchange or, analogously, the protection factor (i.e., the unitless ratio between the rate of exchange for a completely unstructured state and the observed rate). MS provides information that is specific to peptides obtained by proteolytic digestion. The spatial resolution of HDX-MS measurements depends on the proteolytic pattern of the protein, the fragmentation method used, and the overlap between peptides. Different computational approaches have been proposed to extract residue-specific information from peptide-level HDX-MS measurements. Here, we demonstrate the advantages of a method recently proposed that exploits self-consistency and classifies the possible sets of protection factors into a finite number of alternative solutions compatible with experimental data. The degeneracy of the solutions can be reduced (or completely removed) by exploiting the additional information encoded in the shape of the isotopic envelopes. We show how sparse and noisy MS data can provide high-resolution protection factors that correlate with NMR measurements probing the same protein under the same conditions.

Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data

Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling.

Double Mutant of Chymotrypsin Inhibitor 2 Stabilized through Increased Conformational Entropy

The conformational heterogeneity of a folded protein can affect not only its function but also stability and folding. We recently discovered and characterized a stabilized double mutant (L49I/I57V) of the protein CI2 and showed that state-of-the-art prediction methods could not predict the increased stability relative to the wild-type protein. Here, we have examined whether changed native-state dynamics, and resulting entropy changes, can explain the stability changes in the double mutant protein, as well as the two single mutant forms. We have combined NMR relaxation measurements of the ps-ns dynamics of amide groups in the backbone and the methyl groups in the side chains with molecular dynamics simulations to quantify the native-state dynamics. The NMR experiments reveal that the mutations have different effects on the conformational flexibility of CI2: a reduction in conformational dynamics (and entropy estimated from this) of the native state of the L49I variant correlates with its decreased stability, while increased dynamics of the I57V and L49I/I57V variants correlates with their increased stability. These findings suggest that explicitly accounting for changes in native-state entropy might be needed to improve the predictions of the effect of mutations on protein stability.

Molecular Basis of Selective Cytokine Signaling Inhibition by Antibodies Targeting a Shared Receptor

Interleukin-1 (IL-1) family cytokines are potent mediators of inflammation, acting to coordinate local and systemic immune responses to a wide range of stimuli. Aberrant signaling by IL-1 family cytokine members, however, is linked to myriad inflammatory syndromes, autoimmune conditions and cancers. As such, blocking the inflammatory signals inherent to IL-1 family signaling is an established and expanding therapeutic strategy. While several FDA-approved IL-1 inhibitors exist, including an Fc fusion protein, a neutralizing antibody, and an antagonist cytokine, none specifically targets the co-receptor IL-1 receptor accessory protein (IL-1RAcP). Most IL-1 family cytokines form productive signaling complexes by binding first to their cognate receptors – IL-1RI for IL-1α and IL-1β; ST2 for IL-33; and IL-36R for IL-36α, IL-36β and IL-36γ – after which they recruit the shared secondary receptor IL-1RAcP to form a ternary cytokine/receptor/co-receptor complex. Recently, IL-1RAcP was identified as a biomarker for both AML and CML. IL-1RAcP has also been implicated in tumor progression in solid tumors and an anti-IL1RAP antibody (nadunolimab, CAN04) is in phase II clinical studies in pancreatic cancer and non-small cell lung cancer (NCT03267316). As IL-1RAcP is common to all of the abovementioned IL-1 family cytokines, targeting this co-receptor raises the possibility of selective signaling inhibition for different IL-1 family cytokines. Indeed, previous studies of IL-1β and IL-33 signaling complexes have revealed that these cytokines employ distinct mechanisms of IL-1RAcP recruitment even though their overall cytokine/receptor/co-receptor complexes are structurally similar. Here, using functional, biophysical, and structural analyses, we show that antibodies specific for IL-1RAcP can differentially block signaling by IL-1 family cytokines depending on the distinct IL-1RAcP epitopes that they engage. Our results indicate that targeting a shared cytokine receptor is a viable therapeutic strategy for selective cytokine signaling inhibition.

Protein Chemistry Combined with Mass Spectrometry for Protein Structure Determination

The advent of soft-ionization mass spectrometry for biomolecules has opened up new possibilities for the structural analysis of proteins. Combining protein chemistry methods with modern mass spectrometry has led to the emergence of the distinct field of structural proteomics. Multiple protein chemistry approaches, such as surface modification, limited proteolysis, hydrogen-deuterium exchange, and cross-linking, provide diverse and often orthogonal structural information on the protein systems studied. Combining experimental data from these various structural proteomics techniques provides a more comprehensive examination of the protein structure and increases confidence in the ultimate findings. Here, we review various types of experimental data from structural proteomics approaches with an emphasis on the use of multiple complementary mass spectrometric approaches to provide experimental constraints for the solving of protein structures.

Prediction and Validation of a Protein's Free Energy Surface Using Hydrogen Exchange and (Importantly) Its Denaturant Dependence

The denaturant dependence of hydrogen-deuterium exchange (HDX) is a powerful measurement to identify the breaking of individual H-bonds and map the free energy surface (FES) of a protein including the very rare states. Molecular dynamics (MD) can identify each partial unfolding event with atomic-level resolution. Hence, their combination provides a great opportunity to test the accuracy of simulations and to verify the interpretation of HDX data. For this comparison, we use Upside, our new and extremely fast MD package that is capable of folding proteins with an accuracy comparable to that of all-atom methods. The FESs of two naturally occurring and two designed proteins are so generated and compared to our NMR/HDX data. We find that Upside's accuracy is considerably improved upon modifying the energy function using a new machine-learning procedure that trains for proper protein behavior including realistic denatured states in addition to stable native states. The resulting increase in cooperativity is critical for replicating the HDX data and protein stability, indicating that we have properly encoded the underlying physiochemical interactions into an MD package. We did observe some mismatch, however, underscoring the ongoing challenges faced by simulations in calculating accurate FESs. Nevertheless, our ensembles can identify the properties of the fluctuations that lead to HDX, whether they be small-, medium-, or large-scale openings, and can speak to the breadth of the native ensemble that has been a matter of debate.

HDX-MS and MD Simulations Provide Evidence for Stabilization of the IgG1-FcγRIa (CD64a) Immune Complex Through Intermolecular Glycoprotein Bonds

Previous reports present different models for the stabilization of the Fc-FcγRI immune complex. Although accord exists on the importance of L235 in IgG1 and some hydrophobic contacts for complex stabilization, discord exists regarding the existence of stabilizing glycoprotein contacts between glycans of IgG1 and a conserved FG-loop (¹⁷¹MGKHRY¹⁷⁶) of FcγRIa. Complexes formed from the FcγRIa receptor and IgG1s containing biantennary glycans with N-acetylglucosamine, galactose, and α2,6-N-acetylneuraminic terminations were measured by hydrogen-deuterium exchange mass spectrometry (HDX-MS), classified for dissimilarity with Welch's ANOVA and Games-Howell post hoc procedures, and modeled with molecular dynamics (MD) simulations. For each glycoform of the IgG1-FcγRIa complex peptic peptides of Fab, Fc and FcγRIa report distinct H/D exchange rates. MD simulations corroborate the differences in the peptide deuterium content through calculation of the percent of time that transient glycan-peptide bonds exist. These results indicate that stability of IgG1-FcγRIa complexes correlate with the presence of intermolecular glycoprotein interactions between the IgG1 glycans and the ¹⁷³KHR¹⁷⁵ motif within the FG-loop of FcγRIa. The results also indicate that intramolecular glycan-protein bonds stabilize the Fc region in isolated and complexed IgG1. Moreover, HDX-MS data evince that the Fab domain has glycan-protein binding contacts within the IgG1-FcγRI complex.

Modeling the native ensemble of PhuS using enhanced sampling MD and HDX-ensemble reweighting

The cytoplasmic heme binding protein from Pseudomonas aeruginosa, PhuS, plays two essential roles in regulating heme uptake and iron homeostasis. First, PhuS shuttles exogenous heme to heme oxygenase (HemO) for degradation and iron release. Second, PhuS binds DNA and modulates the transcription of the prrF/H small RNAs (sRNAs) involved in the iron-sparing response. Heme binding to PhuS regulates this dual function, as the unliganded form binds DNA, whereas the heme-bound form binds HemO. Crystallographic studies revealed nearly identical structures for apo- and holo-PhuS, and yet numerous solution-based measurements indicate that heme binding is accompanied by large conformational rearrangements. In particular, hydrogen-deuterium exchange mass spectrometry (HDX-MS) of apo- versus holo-PhuS revealed large differences in deuterium uptake, notably in α-helices 6, 7, and 8 (α6,7,8), which contribute to the heme binding pocket. These helices were mostly labile in apo-PhuS but largely protected in holo-PhuS. In contrast, in silico-predicted deuterium uptake levels of α6,7,8 from molecular dynamics (MD) simulations of the apo- and holo-PhuS structures are highly similar, consistent only with the holo-PhuS HDX-MS data. To rationalize this discrepancy between crystal structures, simulations, and observed HDX-MS, we exploit a recently developed computational approach (HDXer) that fits the relative weights of conformational populations within an ensemble of structures to conform to a target set of HDX-MS data. Here, a combination of enhanced sampling MD, HDXer, and dimensionality reduction analysis reveals an apo-PhuS conformational landscape in which α6, 7, and 8 are significantly rearranged compared to the crystal structure, including a loss of secondary structure in α6 and the displacement of α7 toward the HemO binding interface. Circular dichroism analysis confirms the loss of secondary structure, and the extracted ensembles of apo-PhuS and of heme-transfer-impaired H212R mutant, are consistent with known heme binding and transfer properties. The proposed conformational landscape provides structural insights into the modulation by heme of the dual function of PhuS.

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.

Dynamics and Conformational Changes in Human NEIL2 DNA Glycosylase Analyzed by Hydrogen/Deuterium Exchange Mass Spectrometry

Base excision DNA repair (BER) is necessary for removal of damaged nucleobases from the genome and their replacement with normal nucleobases. BER is initiated by DNA glycosylases, the enzymes that cleave the N-glycosidic bonds of damaged deoxynucleotides. Human endonuclease VIII-like protein 2 (hNEIL2), belonging to the helix-two-turn-helix structural superfamily of DNA glycosylases, is an enzyme uniquely specific for oxidized pyrimidines in non-canonical DNA substrates such as bubbles and loops. The structure of hNEIL2 has not been solved; its closest homologs with known structures are NEIL2 from opossum and from giant mimivirus. Here we analyze the conformational dynamics of free hNEIL2 using a combination of hydrogen/deuterium exchange mass spectrometry, homology modeling and molecular dynamics simulations. We show that a prominent feature of vertebrate NEIL2 - a large insert in its N-terminal domain absent from other DNA glycosylases - is unstructured in solution. It was suggested that helix-two-turn-helix DNA glycosylases undergo open-close transition upon DNA binding, with the large movement of their N- and C-terminal domains, but the open conformation has been elusive to capture. Our data point to the open conformation as favorable for free hNEIL2 in solution. Overall, our results are consistent with the view of hNEIL2 as a conformationally flexible protein, which may be due to its participation in the repair of non-canonical DNA structures and/or to the involvement in functional and regulatory protein-protein interactions.

IMProv: A Resource for Cross-link-Driven Structure Modeling that Accommodates Protein Dynamics

Proteomics methodology has expanded to include protein structural analysis, primarily through cross-linking mass spectrometry (XL-MS) and hydrogen-deuterium exchange mass spectrometry (HX-MS). However, while the structural proteomics community has effective tools for primary data analysis, there is a need for structure modeling pipelines that are accessible to the proteomics specialist. Integrative structural biology requires the aggregation of multiple distinct types of data to generate models that satisfy all inputs. Here, we describe IMProv, an app in the Mass Spec Studio that combines XL-MS data with other structural data, such as cryo-EM densities and crystallographic structures, for integrative structure modeling on high-performance computing platforms. The resource provides an easily deployed bundle that includes the open-source Integrative Modeling Platform program (IMP) and its dependencies. IMProv also provides functionality to adjust cross-link distance restraints according to the underlying dynamics of cross-linked sites, as characterized by HX-MS. A dynamics-driven conditioning of restraint values can improve structure modeling precision, as illustrated by an integrative structure of the five-membered Polycomb Repressive Complex 2. IMProv is extensible to additional types of data.

Insights into substrate recognition and specificity for IgG by Endoglycosidase S2

Antibodies bind foreign antigens with high affinity and specificity leading to their neutralization and/or clearance by the immune system. The conserved N-glycan on IgG has significant impact on antibody effector function, with the endoglycosidases of Streptococcus pyogenes deglycosylating the IgG to evade the immune system, a process catalyzed by the endoglycosidase EndoS2. Studies have shown that two of the four domains of EndoS2, the carbohydrate binding module (CBM) and the glycoside hydrolase (GH) domain are critical for catalytic activity. To yield structural insights into contributions of the CBM and the GH domains as well as the overall flexibility of EndoS2 to the proteins’ catalytic activity, models of EndoS2-Fc complexes were generated through enhanced-sampling molecular-dynamics (MD) simulations and site-identification by ligand competitive saturation (SILCS) docking followed by reconstruction and multi-microsecond MD simulations. Modeling results predict that EndoS2 initially interacts with the IgG through its CBM followed by interactions with the GH yielding catalytically competent states. These may involve the CBM and GH of EndoS2 simultaneously interacting with either the same Fc CH2/CH3 domain or individually with the two Fc CH2/CH3 domains, with EndoS2 predicted to assume closed conformations in the former case and open conformations in the latter. Apo EndoS2 is predicted to sample both the open and closed states, suggesting that either complex can directly form following initial IgG-EndoS2 encounter. Interactions of the CBM and GH domains with the IgG are predicted to occur through both its glycan and protein regions. Simulations also predict that the Fc glycan can directly transfer from the CBM to the GH, facilitating formation of catalytically competent complexes and how the 734 to 751 loop on the CBM can facilitate extraction of the glycan away from the Fc CH2/CH3 domain. The predicted models are compared and consistent with Hydrogen/Deuterium Exchange data. In addition, the complex models are consistent with the high specificity of EndoS2 for the glycans on IgG supporting the validity of the predicted models.

The pathogen Streptococcus pyogenes uses the endoglycosidases S and S2 to cleave the glycans on the Fc portion of IgG antibodies, leading to a decreased cytotoxicity of the antibodies, thereby evading the host immune response. To identify potential structures of the complex of EndoS2 with IgG that could lead to the catalytic hydrolysis of the IgG glycan, molecular modeling and molecular dynamics simulations were applied. The resulting structural models predict that EndoS2 initially interacts through its carbohydrate binding module (CBM) with the IgG with subsequent interactions with the catalytic glycoside hydrolase (GH) domain yielding stable complexes. In the modeled complexes the CBM and the GH interact either simultaneously with the same Fc CH2/CH3 domain or with the two individual Fc CH2/CH3 domains separately to yield potentially catalytically competent species. In addition, apo EndoS2 is shown to assume both open and closed conformations allowing it to directly form either type of complex from which deglycosylation of either mono- or diglycosylated IgG species may occur.

A Review on Computational Approaches for Analyzing Hydrogen- Deuterium (H/D) Exchange of Proteins

Native state Hydrogen-Deuterium (H/D) exchange method has been used to study the structures and the unfolding pathways for quite a number of proteins. The H/D exchange method is generally monitored using nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) techniques. NMR-assisted H/D exchange methods primarily monitor the residue level fluctuation of proteins, whereas MS-assisted H/D exchange methods analyze multifold ensemble conformations of proteins. In this connection, quite a large number of computational tools and algorithms have been developed for processing and analyzing huge amount of the H/D exchange data generated from these techniques. In this review, most of the freely available computational tools associated with the H/D exchange of proteins have been comprehensively reviewed and scopes to improve/ develop novel computational approaches for analyzing the H/D exchange data of proteins have also been brought into fore.

Reconciling Simulations and Experiments With BICePs: A Review

Bayesian Inference of Conformational Populations (BICePs) is an algorithm developed to reconcile simulated ensembles with sparse experimental measurements. The Bayesian framework of BICePs enables population reweighting as a post-simulation processing step, with several advantages over existing methods, including the proper use of reference potentials, and the estimation of a Bayes factor-like quantity called the BICePs score for model selection. Here, we summarize the theory underlying this method in context with related algorithms, review the history of BICePs applications to date, and discuss current shortcomings along with future plans for improvement.

Hydrogen-Deuterium Exchange within Adenosine Deaminase, a TIM Barrel Hydrolase, Identifies Networks for Thermal Activation of Catalysis

Proteins are intrinsically flexible macromolecules that undergo internal motions with time scales spanning femtoseconds to milliseconds. These fluctuations are implicated in the optimization of reaction barriers for enzyme catalyzed reactions. Time, temperature, and mutation dependent hydrogen-deuterium exchange coupled to mass spectrometry (HDX-MS) has been previously employed to identify spatially resolved, catalysis-linked dynamical regions of enzymes. We now extend this technique to pursue the correlation of protein flexibility and chemical reactivity within the diverse and widespread TIM barrel proteins, targeting murine adenosine deaminase (mADA) that catalyzes the irreversible deamination of adenosine to inosine and ammonia. Following a structure-function analysis of rate and activation energy for a series of mutations at a second sphere phenylalanine positioned in proximity to the bound substrate, the catalytically impaired Phe61Ala with an elevated activation energy (Ea = 7.5 kcal/mol) and the wild type (WT) mADA (Ea = 5.0 kcal/mol) were selected for HDX-MS experiments. The rate constants and activation energies of HDX for peptide segments are quantified and used to assess mutation-dependent changes in local and distal motions. Analyses reveal that approximately 50% of the protein sequence of Phe61Ala displays significant changes in the temperature dependence of HDX behaviors, with the dominant change being an increase in protein flexibility. Utilizing Phe61Ile, which displays the same activation energy for k_cat as WT, as a control, we were able to further refine the HDX analysis, highlighting the regions of mADA that are altered in a functionally relevant manner. A map is constructed that illustrates the regions of protein that are proposed to be essential for the thermal optimization of active site configurations that dominate reaction barrier crossings in the native enzyme.

Structural dynamics of pentapeptide repeat proteins

Pentapeptide repeat proteins (PRPs) represent a large superfamily with more than 38 000 sequences in nearly 3500 species, the majority belonging to cyanobacteria but represented among all branches of life. PRPs contain at least eight consecutive pentapeptide repeats with the consensus (A/C/S/V/T/L/I)(D/N/S/K/E/I/R)(L/F)(S/T/R/E/Q/K/V/D)(G/D/E/N/R/Q/K). PRPs fold into right-handed quadrilateral β helices, also known as repeat-five-residue (Rfr)-folds, with four consecutive pentapeptide repeats comprising a single coil, the ~90° change in polypeptide direction in square-shaped coils achieved by type I, II and IV β turns, and hydrogen bonds between coils establishing β ladders on each Rfr-fold face. PRPs are broadly categorized into group 1 and 2 involved in antibiotic resistance and group 3 currently having unknown functions. Motivated by their intriguing structures, we are investigating PRP biophysical characteristics, including Rfr-fold thermal stability, β turn and β ladder hydrogen bond amide exchange rates and backbone dynamics. Here, we present analysis of 20 ns molecular dynamics (MD) simulations and all atom normal mode analysis (aaNMA) calculations for four group 1 and group 2 and four group 3 PRPs whose structures have been determined by X-ray crystallography. The MD cross-correlation matrices and aaNMA indicated strong correlated motion between adjacent coils and weak coupled motion between coils separated by one or more intervening coils. Slow anticorrelated motions were detected between adjacent coils in aaNMA modes that we hypothesize are requisite to access exchange-competent states necessary to permit solvent exchange of amide hydrogens involved in β-ladder and β-turns hydrogen bonds, which can have lifetimes on the order of months.

Structural predictions of the functions of membrane proteins from HDX-MS

HDX-MS has emerged as a powerful tool to interrogate the structure and dynamics of proteins and their complexes. Recent advances in the methodology and instrumentation have enabled the application of HDX-MS to membrane proteins. Such targets are challenging to investigate with conventional strategies. Developing new tools are therefore pertinent for improving our fundamental knowledge of how membrane proteins function in the cell. Importantly, investigating this central class of biomolecules within their native lipid environment remains a challenge but also a key goal ahead. In this short review, we outline recent progresses in dissecting the conformational mechanisms of membrane proteins using HDX-MS. We further describe how the use of computational strategies can aid the interpretation of experimental data and enable visualisation of otherwise intractable membrane protein states. This unique integration of experiments with computations holds significant potential for future applications.

Interpretation of HDX Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles

Hydrogen-deuterium exchange combined with mass spectrometry (HDX-MS) is a widely applied biophysical technique that probes the structure and dynamics of biomolecules without the need for site-directed modifications or bio-orthogonal labels. The mechanistic interpretation of HDX data, however, is often qualitative and subjective, owing to a lack of quantitative methods to rigorously translate observed deuteration levels into atomistic structural information. To help address this problem, we have developed a methodology to generate structural ensembles that faithfully reproduce HDX-MS measurements. In this approach, an ensemble of protein conformations is first generated, typically using molecular dynamics simulations. A maximum-entropy bias is then applied post hoc to the resulting ensemble such that averaged peptide-deuteration levels, as predicted by an empirical model, agree with target values within a given level of uncertainty. We evaluate this approach, referred to as HDX ensemble reweighting (HDXer), for artificial target data reflecting the two major conformational states of a binding protein. We demonstrate that the information provided by HDX-MS experiments and by the model of exchange are sufficient to recover correctly weighted structural ensembles from simulations, even when the relevant conformations are rarely observed. Degrading the information content of the target data—e.g., by reducing sequence coverage, by averaging exchange levels over longer peptide segments, or by incorporating different sources of uncertainty—reduces the structural accuracy of the reweighted ensemble but still allows for useful insights into the distinctive structural features reflected by the target data. Finally, we describe a quantitative metric to rank candidate structural ensembles according to their correspondence with target data and illustrate the use of HDXer to describe changes in the conformational ensemble of the membrane protein LeuT. In summary, HDXer is designed to facilitate objective structural interpretations of HDX-MS data and to inform experimental approaches and further developments of theoretical exchange models.

Reconciling Simulated Ensembles of Apomyoglobin with Experimental Hydrogen/Deuterium Exchange Data Using Bayesian Inference and Multiensemble Markov State Models

Hydrogen/deuterium exchange (HDX) is a powerful technique to investigate protein conformational dynamics at amino acid resolution. Because HDX provides a measurement of solvent exposure of backbone hydrogens, ensemble-averaged over potentially slow kinetic processes, it has been challenging to use HDX protection factors to refine structural ensembles obtained from molecular dynamics simulations. This entails dual challenges: (1) identifying structural observables that best correlate with backbone amide protection from exchange and (2) restraining these observables in molecular simulations to model ensembles consistent with experimental measurements. Here, we make significant progress on both fronts. First, we describe an improved predictor of HDX protection factors from structural observables in simulated ensembles, parametrized from ultralong molecular dynamics simulation trajectory data, with a Bayesian inference approach used to retain the full posterior distribution of model parameters. We next present a new method for obtaining simulated ensembles in agreement with experimental HDX protection factors, in which molecular simulations are performed at various temperatures and restraint biases and used to construct multiensemble Markov State Models (MSMs). Finally, the BICePs (Bayesian Inference of Conformational Populations) algorithm is then used with our HDX protection factor predictor to infer which thermodynamic ensemble agrees best with the experiment and estimate populations of each conformational state in the MSM. To illustrate the approach, we use a combination of HDX protection factor restraints and chemical shift restraints to model the conformational ensemble of apomyoglobin at pH 6. The resulting ensemble agrees well with the experiment and gives insight into the all-atom structure of disordered helices F and H in the absence of heme.

Structure of an Unfolding Intermediate of an RRM Domain of ETR-3 Reveals Its Native-like Fold

Prevalence of one or more partially folded intermediates during protein unfolding with different secondary and ternary conformations has been identified as an integral character of protein unfolding. These transition-state species need to be characterized structurally for elucidation of their folding pathways. We have determined the three-dimensional structure of an intermediate state with increased conformational space sampling under urea-denaturing condition. The protein unfolds completely at 10 M urea but retains residual secondary structural propensities with restricted motion. Here, we describe the native state, observable intermediate state, and unfolded state for ETR-3 RRM-3, which has canonical RRM fold. These observations can shed more light on unfolding events for RRM-containing proteins.

Prediction of the initial folding sites and the entire folding processes for Ig-like beta-sandwich proteins

Describing the whole story of protein folding is currently the main enigmatic problem in molecular bioinformatics study. Protein folding mechanisms have been intensively investigated with experimental as well as simulation techniques. Since a protein folds into its specific 3D structure from a unique amino acid sequence, it is interesting to extract as much information as possible from the amino acid sequence of a protein. Analyses based on inter-residue average distance statistics and a coarse-grained Gō-model simulation were conducted on Ig and FN3 domains of a titin protein to decode the folding mechanisms from their sequence data and native structure data, respectively. The central region of all domains was predicted to be an initial folding unit, that is, stable in an early state of folding. This common feature coincides well with the experimental results and underscores the significance of the β-sandwich proteins' common structure, namely, the key strands for folding and the Greek-key motif, which is located in the central region. We confirmed that our sequence-based techniques were able to predict the initial folding event just next to the denatured state and that a 3D-based Gō-model simulation can be used to investigate the whole process of protein folding.

Ligand-induced disorder-to-order transitions characterized by structural proteomics and molecular dynamics simulations

For disordered proteins, ligand binding can be a critical event that changes their structural dynamics. The ability to characterize such changes would facilitate the development of drugs designed to stabilize disordered proteins, whose mis-folding is important for a number of pathologies, including neurodegenerative diseases such as Parkinson's and Alzheimer's diseases. In this study, we used hydrogen/deuterium exchange, differential crosslinking, differential surface modification, and molecular dynamics (MD) simulations to characterize the structural changes in disordered proteins that result from ligand binding. We show here that both an ATP-independent protein chaperone, Spy L32P, and the FK506 binding domain of a prolyl isomerase, FKBP-25 F145A/I223P, are disordered, yet exhibit structures that are distinct from chemically denatured unfolded states in solution, and that they undergo transitions to a more structured state upon ligand binding. These systems may serve as models for the characterization of ligand-induced disorder-to-order transitions in proteins using structural proteomics approaches. SIGNIFICANCE: In this study, we used hydrogen/deuterium exchange, differential crosslinking, differential surface modification, and molecular-dynamics simulations to characterize the structural changes in disordered proteins that result from ligand binding. The protein-ligand systems studied here (the ATP-independent protein chaperone, Spy L32P, and the FK506 binding domain of a prolyl isomerase, FKBP-25 F145A/I223P) may serve as models for understanding ligand-induced disorder-to-order transitions in proteins. Additionally, the structural proteomic techniques demonstrated here are shown to be effective tools for the characterization of disorder-to-order transitions and can be used to facilitate study of other systems in which this class of structural transition can be used for modulating major pathological features of disease, such as the abnormal protein aggregation that occurs with Parkinson's disease and Alzheimer's disease.

PSX, Protein–Solvent Exchange: software for calculation of deuterium-exchange effects in small-angle neutron scattering measurements from protein coordinates

Recent developments in neutron scattering instrumentation and sample handling have enabled studies of more complex biological samples and measurements at shorter exposure times. The experiments are typically conducted in D2O-based buffers to emphasize or diminish scattering from a particular component or to minimize background noise in the experiment. To extract most information from such experiments it is thus desirable to determine accurate estimates of how and when closely bound hydrogen atoms from the biomolecule exchange with the deuterium in the solvent. This article introduces and documents software, PSX, for exploring the effect of hydrogen–deuterium exchange for proteins solubilized in D2O as well as the underlying bioinformatical models. The software aims to be generally applicable for any atomistic structure of a protein and its surrounding environment, and thus captures effects of both heterogenous exchange rates throughout the protein structure and varying the experimental conditions such as pH and temperature. The paper concludes with examples of applications and estimates of the effect in typical scenarios emerging in small-angle neutron scattering on biological macromolecules in solution. The analysis presented here suggests that the common assumption of 90% exchange is in many cases an overestimate with the rapid sample handling systems currently available, which leads to fitting and calibration issues when analysing the data. Source code for the presented software is available from an online repository in which it is published under version 3 of the GNU publishing licence.

Estimating Constraints for Protection Factors from HDX-MS Data

Hydrogen/deuterium exchange monitored by mass spectrometry is a promising technique for rapidly fingerprinting structural and dynamical properties of proteins. The time-dependent change in the mass of any fragment of the polypeptide chain depends uniquely on the rate of exchange of its amide hydrogens, but determining the latter from the former is generally not possible. Here, we show that, if time-resolved measurements are available for a number of overlapping peptides that cover the whole sequence, rate constants for each amide hydrogen exchange (or equivalently, their protection factors) may be extracted and the uniqueness of the solutions obtained depending on the degree of peptide overlap. However, in most cases, the solution is not unique, and multiple alternatives must be considered. We provide a statistical method that clusters the solutions to further reduce their number. Such analysis always provides meaningful constraints on protection factors and can be used in situations in which obtaining more refined experimental data is impractical. It also provides a systematic way to improve data collection strategies to obtain unambiguous information at single-residue level (e.g., for assessing protein structure predictions at atomistic level).

POPPeT: a New Method to Predict the Protection Factor of Backbone Amide Hydrogens

Hydrogen exchange (HX) has become an important tool to monitor protein structure and dynamics. The interpretation of HX data with respect to protein structure requires understanding of the factors that influence exchange. Simulated protein structures can be validated by comparing experimental deuteration profiles with the profiles derived from the modeled protein structure. To do this, we propose here a new method, POPPeT, for protection factor prediction based on protein motions that enable HX. By comparing POPPeT with two existing methods, the phenomenological approximation and COREX, we show enhanced predictability measured at both protection factor and deuteration level. This method can be subsequently used by modeling strategies for protein structure prediction. Graphical Abstract ᅟ.

Quantitative Evaluation of Native Protein Folds and Assemblies by Hydrogen Deuterium Exchange Mass Spectrometry (HDX-MS)

Hydrogen deuterium exchange mass spectrometry (HDX-MS) has significant potential for protein structure initiatives but its relationship with protein conformations is unclear. We report on the efficacy of HDX-MS to distinguish between native and non-native proteins using a popular approach to calculate HDX protection factors (PFs) from protein structures. The ability of HDX-MS to identify native protein conformations is quantified by binary structural classification such that merits of the approach for protein modelling can be quantified and better understood. We show that highly accurate PF calculations are not a prerequisite for HDX-MS simulations that are capable of effectively discriminating between native and non-native protein folds. The simulations can also be performed directly on unique structures facilitating high-throughput evaluation of many alternate conformations. The ability of HDX-MS to classify the conformations of homo-protein assemblies is also investigated. In contrast to protein monomers, we show a significant lack of correspondence between the simulated and experimental HDX-MS data for these systems with a subsequent decrease in the ability of HDX-MS to identify native states. However, we demonstrate surprisingly high diagnostic ability of the simulated data for assemblies in which a significant proportion of the individual chains occupy protein-protein interfaces. We relate this to the number of peptides that can sample alternate subunit orientations and discuss these observations within the larger context of applying HDX-MS to evaluate protein structures. Graphical Abstract.

Prediction of folding mechanisms for Ig-like beta sandwich proteins based on inter-residue average distance statistics methods

To understand the folding mechanism of a protein is one of the goals in bioinformatics study. Nowadays, it is enigmatic and difficult to extract folding information from amino acid sequence using standard bioinformatics techniques or even experimental protocols which can be time consuming. To overcome these problems, we aim to extract the initial folding unit for titin protein (Ig and fnIII domains) by means of inter-residue average distance statistics, Average Distance Map (ADM) and contact frequency analysis (F-value). TI I27 and TNfn3 domains are used to represent the Ig-domain and fnIII-domain, respectively. Beta-strands 2, 3, 5, and 6 are significant for the initial folding processes of TI I27. The central strands of TNfn3 were predicted as a primary folding segment. Known 3D structure and unknown 3D structure domains were investigated by structure or non-structure based multiple sequence alignment, respectively, to learn the conserved hydrophobic residues and predicted compact region relevant to evolution. Our results show good correspondence to experimental data, phi-value and protection factor from H-D exchange experiments. The significance of conserved hydrophobic residues near F-value peaks for structural stability using hydrophobic packing is confirmed. Our prediction methods once again could extract a folding mechanism only knowing the amino acid sequence.

Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data

Both experimental and computational methods are available to gather information about a protein's conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining computational and experimental techniques relying on each other to overcome their respective limitations. Indeed, despite its advantages, an experimental technique such as hydrogen-exchange monitoring cannot produce structural models because of its low resolution. Additionally, the computational methods that can generate such models suffer from the curse of dimensionality when applied to large proteins. Adopting a common solution to this issue, we have recently proposed a framework in which our computational method for protein conformational sampling is biased by experimental hydrogen-exchange data. In this paper, we present our latest application of this computational framework: generating an atomic-resolution structural model for an unknown protein state. For that, starting from an available protein structure, we explore the conformational space of this protein, using hydrogen-exchange data on this unknown state as a guide. We have successfully used our computational framework to generate models for three proteins of increasing size, the biggest one undergoing large-scale conformational changes.

Interpreting Hydrogen-Deuterium Exchange Events in Proteins Using Atomistic Simulations: Case Studies on Regulators of G-Protein Signaling Proteins

Hydrogen-deuterium exchange (HDX) experiments are widely used in studies of protein dynamics. To predict the propensity of amide hydrogens for exchange with deuterium, several models have been reported in which computations of amide-hydrogen protection factors are carried out using molecular dynamics (MD) simulations. Given significant variation in the criteria used in different models, the robustness and broader applicability of these models to other proteins, especially homologous proteins showing distinct amide-exchange patterns, remains unknown. The sensitivity of the predictions when MD simulations are conducted with different force-fields is yet to tested and quantified. Using MD simulations and experimental HDX data on three homologous signaling proteins, we report detailed studies quantifying the performance of seven previously reported models (M1-M7) of two general types: empirical and fractional-population models. We find that the empirical models show inconsistent predictions but predictions of the fractional population models are robust. Contrary to previously reported work, we find that the solvent-accessible surface area of amide hydrogens is a useful metric when combined with a new metric defining the distances of amide hydrogens from the first polar atoms in proteins. On the basis of this, we report two new models, one empirical (M8) and one population-based (M9). We find strong protection of amide hydrogens from solvent exchange both within the stable helical motifs and also in the interhelical loops. We further observe that the exchange-competent states of amide hydrogens occur on the sub 100 ps time-scale via localized fluctuations, and such states among amides of a given protein do not appear to show any cooperativity or allosteric coupling.

Native State of Complement Protein C3d Analysed via Hydrogen Exchange and Conformational Sampling

Hydrogen/deuterium exchange detected by mass spectrometry (HDXMS) provides valuable information on protein structure and dynamics. Although HDX-MS data is often interpreted using crystal structures, it was suggested that conformational ensembles produced by molecular dynamics simulations yield more accurate interpretations. In this paper, we analyse the complement protein C3d by performing an HDX-MS experiment, and evaluate several interpretation methodologies using an existing prediction model to derive HDX-MS data from protein structure. To interpret and refine C3d's HDX-MS data, we look for a conformation (or conformational ensemble) of C3d that allows computationally replicating this data. We confirm that crystal structures are not a good choice and suggest that conformational ensembles produced by molecular dynamics simulations might not always be satisfactory either. Finally, we show that coarse-grained conformational sampling of C3d produces a conformation from which its HDX-MS data can be replicated and refined.