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

Conformational free-energy landscapes of a Na+/Ca2+ exchanger explain its alternating-access mechanism and functional specificity

Secondary-active transporters catalyze the movement of myriad substances across all cellular membranes, typically against opposing concentration gradients, and without consuming any ATP. To do so, these proteins employ an intriguing structural mechanism evolved to be activated only upon recognition or release of the transported species. We examine this self-regulated mechanism using a homolog of the cardiac Na+/Ca2+ exchanger as a model system. Using advanced computer simulations, we map out the complete functional cycle of this transporter, including unknown conformations that we validate against existing experimental data. Calculated free-energy landscapes reveal why this transporter functions as an antiporter rather than a symporter, why it specifically exchanges Na+ and Ca2+, and why the stoichiometry of this exchange is exactly 3:1. We also rationalize why the protein does not exchange H+ for either Ca2+ or Na+, despite being able to bind H+ and its high similarity with H+/Ca2+ exchangers. Interestingly, the nature of this transporter is not explained by its primary structural states, known as inward- and outward-open conformations; instead, the defining factor is the feasibility of conformational intermediates between those states, wherein access pathways leading to the substrate binding sites become simultaneously occluded from both sides of the membrane. This analysis offers a physically coherent, broadly transferable route to understand the emergence of function from structure among secondary-active membrane transporters.

Visualization of conformational transition of GRP94 in solution

GRP94, an ER paralog of the heat-shock protein 90 family, binds and hydrolyses ATP to chaperone the folding and maturation of its selected clients. Compared with other hsp90 proteins, the in-solution conformational dynamics of GRP94 along the ATP hydrolysis cycle are less understood, hindering our understanding of its chaperoning mechanism. Leveraging small-angle X-ray scattering, negative-staining EM, and hydrogen-deuterium exchange coupled mass-spec, here we show that in its apo form, ∼60% of mouse GRP94 (mGRP94) populates an "extended" conformation, whereas the rest exist in either "close V" or "twist V" like "compact" conformations. Different from other hsp90 proteins, the presence of AMPPNP only impacts the relative abundance of the two compact conformations, rather than shifting the equilibrium between the "extended" and "compact" conformations of mGRP94. HDX-MS study of apo, AMPPNP-bound, and ADP-bound mGRP94 suggests a conformational transition from "twist V" to "close V" upon ATP binding and a back transition from "close V" to "twist V" upon ATP hydrolysis. These results illustrate the dissimilarities of GRP94 in conformation transition during ATP hydrolysis from other hsp90 paralogs.

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.

Activation loop plasticity and active site coupling in the MAP kinase, ERK2

Changes in the dynamics of the protein kinase, ERK2, have been shown to accompany its activation by dual phosphorylation. However, our knowledge about the conformational changes represented by these motions is incomplete. Previous NMR relaxation dispersion studies showed that active, dual-phosphorylated ERK2 undergoes global exchange between at least two energetically similar conformations. These findings, combined with measurements by hydrogen exchange mass spectrometry (HX-MS), suggested that the global conformational exchange involves motions of the activation loop (A-loop) that are coupled to regions surrounding the kinase active site. In order to better understand the contribution of dynamics to the activation of ERK2, we applied long conventional molecular dynamics (MD) simulations starting from crystal structures of active, phosphorylated (2P), and inactive, unphosphorylated (0P) ERK2. Individual trajectories were run for (5 to 25) µ s and totaled 727 µ s. The results showed that the A-loop is unexpectedly flexible in both 2P- and 0P-ERK2, and able to adopt multiple long-lived (>5 µ s) conformational states. Simulations starting from the X-ray structure of 2P-ERK2 (2ERK) revealed A-loop states corresponding to restrained dynamics within the N-lobe, including regions surrounding catalytic residues. One A-loop conformer forms lasting interactions with the C-terminal L16 segment and shows reduced RMSF and greater compaction in the active site. By contrast, simulations starting from the most common X-ray conformation of 0P-ERK2 (5UMO) reveal frequent excursions of A-loop residues away from a C-lobe docking site pocket and towards a new state that shows greater dynamics in the N-lobe and disorganization around the active site. Thus, the A-loop in ERK2 appears to switch between distinct conformational states that reflect allosteric coupling with the active site, likely occurring via the L16 segment. Analyses of crystal packing interactions across many structural datasets suggest that the A-loop observed in X-ray structures of ERK2 may be driven by lattice contacts and less representative of the solution structure. The novel conformational states identified by MD expand our understanding of ERK2 regulation, by linking the activated state of the kinase to reduced dynamics and greater compaction surrounding the catalytic site.

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.

Biochemical and structural insights into SARS-CoV-2 polyprotein processing by Mpro

SARS-CoV-2, a human coronavirus, is the causative agent of the COVID-19 pandemic. Its genome is translated into two large polyproteins subsequently cleaved by viral papain-like protease and main protease (Mpro). Polyprotein processing is essential yet incompletely understood. We studied Mpro-mediated processing of the nsp7-11 polyprotein, whose mature products include cofactors of the viral replicase, and identified the order of cleavages. Integrative modeling based on mass spectrometry (including hydrogen-deuterium exchange and cross-linking) and x-ray scattering yielded a nsp7-11 structural ensemble, demonstrating shared secondary structural elements with individual nsps. The pattern of cross-links and HDX footprint of the C145A Mpro and nsp7-11 complex demonstrate preferential binding of the enzyme active site to the polyprotein junction sites and additional transient contacts to help orient the enzyme on its substrate for cleavage. Last, proteolysis assays were used to characterize the effect of inhibitors/binders on Mpro processing/inhibition using the nsp7-11 polyprotein as substrate.

Insights into Phosphorylation-Induced Protein Allostery and Conformational Dynamics of Glycogen Phosphorylase via Integrative Structural Mass Spectrometry and In Silico Modeling

Allosteric regulation plays a fundamental role in innumerable biological processes. Understanding its dynamic mechanism and impact at the molecular level is of great importance in disease diagnosis and drug discovery. Glycogen phosphorylase (GP) is a phosphoprotein responding to allosteric regulation and has significant biological importance to glycogen metabolism. Although the atomic structures of GP have been previously solved, the conformational dynamics of GP related to allostery regulation remain largely elusive due to its macromolecular size (∼196 kDa). Here, we integrated native top-down mass spectrometry (nTD-MS), hydrogen-deuterium exchange MS (HDX-MS), protection factor (PF) analysis, molecular dynamics (MD) simulations, and allostery signaling analysis to examine the structural basis and dynamics for the allosteric regulation of GP by phosphorylation. nTD-MS reveals differences in structural stability as well as oligomeric state between the unphosphorylated (GPb) and phosphorylated (GPa) forms. HDX-MS, PF analysis, and MD simulations further pinpoint the structural differences between GPb and GPa involving the binding interfaces (the N-terminal and tower-tower helices), catalytic site, and PLP-binding region. More importantly, it also allowed us to complete the missing link of the long-range communication process from the N-terminal tail to the catalytic site caused by phosphorylation. This integrative MS and in silico-based platform is highly complementary to biophysical approaches and yields valuable insights into protein structures and dynamic regulation.

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.

Simulation-based inference with approximately correct parameters via maximum entropy

Inferring the input parameters of simulators from observations is a crucial challenge with applications from epidemiology to molecular dynamics. Here we show a simple approach in the regime of sparse data and approximately correct models, which is common when trying to use an existing model to infer latent variables with observed data. This approach is based on the principle of maximum entropy (MaxEnt) and provably makes the smallest change in the latent joint distribution to fit new data. This method requires no likelihood or model derivatives and its fit is insensitive to prior strength, removing the need to balance observed data fit with prior belief. The method requires the ansatz that data is fit in expectation, which is true in some settings and may be reasonable in all settings with few data points. The method is based on sample reweighting, so its asymptotic run time is independent of prior distribution dimension. We demonstrate this MaxEnt approach and compare with other likelihood-free inference methods across three systems: a point particle moving in a gravitational field, a compartmental model of epidemic spread and molecular dynamics simulation of a protein.

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.

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.

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.

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.

Advances in Hydrogen/Deuterium Exchange Mass Spectrometry and the Pursuit of Challenging Biological Systems

Solution-phase hydrogen/deuterium exchange (HDX) coupled to mass spectrometry (MS) is a widespread tool for structural analysis across academia and the biopharmaceutical industry. By monitoring the exchangeability of backbone amide protons, HDX-MS can reveal information about higher-order structure and dynamics throughout a protein, can track protein folding pathways, map interaction sites, and assess conformational states of protein samples. The combination of the versatility of the hydrogen/deuterium exchange reaction with the sensitivity of mass spectrometry has enabled the study of extremely challenging protein systems, some of which cannot be suitably studied using other techniques. Improvements over the past three decades have continually increased throughput, robustness, and expanded the limits of what is feasible for HDX-MS investigations. To provide an overview for researchers seeking to utilize and derive the most from HDX-MS for protein structural analysis, we summarize the fundamental principles, basic methodology, strengths and weaknesses, and the established applications of HDX-MS while highlighting new developments and applications.

Interpreting Hydrogen-Deuterium Exchange Experiments with Molecular Simulations: Tutorials and Applications of the HDXer Ensemble Reweighting Software [Article v1.0]

Hydrogen-deuterium exchange (HDX) is a comprehensive yet detailed probe of protein structure and dynamics and, coupled to mass spectrometry, has become a powerful tool for investigating an increasingly large array of systems. Computer simulations are often used to help rationalize experimental observations of exchange, but interpretations have frequently been limited to simple, subjective correlations between microscopic dynamical fluctuations and the observed macroscopic exchange behavior. With this in mind, we previously developed the HDX ensemble reweighting approach and associated software, HDXer, to aid the objective interpretation of HDX data using molecular simulations. HDXer has two main functions; first, to compute H-D exchange rates that describe each structure in a candidate ensemble of protein structures, for example from molecular simulations, and second, to objectively reweight the conformational populations present in a candidate ensemble to conform to experimental exchange data. In this article, we first describe the HDXer approach, theory, and implementation. We then guide users through a suite of tutorials that demonstrate the practical aspects of preparing experimental data, computing HDX levels from molecular simulations, and performing ensemble reweighting analyses. Finally we provide a practical discussion of the capabilities and limitations of the HDXer methods including recommendations for a user's own analyses. Overall, this article is intended to provide an up-to-date, pedagogical counterpart to the software, which is freely available at https://github.com/Lucy-Forrest-Lab/HDXer.

Combining Experimental Data and Computational Methods for the Non-Computer Specialist

Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.

Data-Driven Molecular Dynamics: A Multifaceted Challenge

The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

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.