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

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.

EGCG has Dual and Opposing Effects on the N-terminal Region of Self-associating α-synuclein Oligomers

Oligomers of the protein α-synuclein (α-syn) are thought to be a major toxic species in Parkinson's disease, particularly through their ability to permeabilize cell membranes. The green tea polyphenol epigallocatechin gallate (EGCG) has been found to reduce this ability. We have analyzed α-syn oligomer dynamics and interconversion by H/D exchange monitored by mass spectrometry (HDX-MS). Our results show that the two oligomers OI and OII co-exist in equilibrium; OI is a multimer of OII and its dissociation can be followed by HDX-MS by virtue of the correlated exchange of the N-terminal region. Urea destabilizes the α-syn oligomers, dissociating OI to OII and monomers. Oligomers exposed to EGCG undergo Met oxidation. Intriguingly, EGCG induces an oxidation-dependent effect on the structure of the N-terminal region. For the non-oxidized N-terminal region, EGCG increases the stability of the folded structure as measured by a higher level of protection against H/D exchange. In contrast, protection is clearly abrogated in the Met oxidized N-terminal region. Having a non-oxidized and disordered N-terminal region is known to be essential for efficient membrane binding. Therefore, our results suggest that the combined effect of a structural stabilization of the non-oxidized N-terminal region and the presence of a disordered oxidized N-terminal region renders the oligomers less cytotoxic by decreasing the ability of the N-terminal region to bind to cell membranes and facilitate their permeabilization.

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.

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.

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.

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.

Protein Structure Prediction with Mass Spectrometry Data

Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen-deuterium exchange, hydroxyl radical protein footprinting, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Hydrogen/Deuterium Exchange Measurements May Provide an Incomplete View of Protein Dynamics: a Case Study on Cytochrome c

Many aspects of protein function rely on conformational fluctuations. Hydrogen/deuterium exchange (HDX) mass spectrometry (MS) provides a window into these dynamics. Despite the widespread use of HDX-MS, it remains unclear whether this technique provides a truly comprehensive view of protein dynamics. HDX is mediated by H-bond-opening/closing events, implying that HDX methods provide an H-bond-centric view. This raises the question if there could be fluctuations that leave the H-bond network unaffected, thereby rendering them undetectable by HDX-MS. We explore this issue in experiments on cytochrome c (cyt c). Compared to the Fe(II) protein, Fe(III) cyt c shows enhanced deuteration on both the distal and proximal sides of the heme. Previous studies have attributed the enhanced dynamics of Fe(III) cyt c to the facile and reversible rupture of the distal M80-Fe(III) bond. Using molecular dynamics (MD) simulations, we conducted a detailed analysis of various cyt c conformers. Our MD data confirm that rupture of the M80-Fe(III) contact triggers major reorientation of the distal Ω loop. Surprisingly, this event takes place with only miniscule H-bonding alterations. In other words, the distal loop dynamics are almost "HDX-silent". Moreover, distal loop movements cannot account for enhanced dynamics on the opposite (proximal) side of the heme. Instead, enhanced deuteration of Fe(III) cyt c is attributed to sparsely populated conformers where both the distal (M80) and proximal (H18) coordination bonds have been ruptured, along with opening of numerous H-bonds on both sides of the heme. We conclude that there can be major structural fluctuations that are only weakly coupled to changes in H-bonding, making them virtually impossible to track by HDX-MS. In such cases, HDX-MS may provide an incomplete view of protein dynamics.

Structural Ensemble of the Insulin Monomer

Experimental evidence suggests that monomeric insulin exhibits significant conformational heterogeneity, and modifications of apparently disordered regions affect both biological activity and the longevity of pharmaceutical formulations, presumably through receptor binding and fibrillation/degradation, respectively. However, a microscopic understanding of conformational heterogeneity has been lacking. Here, we integrate all-atom molecular dynamics simulations with an analysis pipeline to investigate the structural ensemble of human insulin monomers. We find that 60% of the structures present at least one of the following elements of disorder: melting of the A-chain N-terminal helix, detachment of the B-chain N-terminus, and detachment of the B-chain C-terminus. We also observe partial melting and extension of the B-chain helix and significant conformational heterogeneity in the region containing the B-chain β-turn. We then estimate hydrogen-exchange protection factors for the sampled ensemble and find them in line with experimental results for KP-insulin, although the simulations underestimate the importance of unfolded states. Our results help explain the ready exchange of specific amide sites that appear to be protected in crystal structures. Finally, we discuss the implications for insulin function and stability.

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.

Accurate Estimation of Solvent Accessible Surface Area for Coarse-Grained Biomolecular Structures with Deep Learning

Coarse-grained (CG) models of biomolecules have been widely used in protein/ribonucleic acid (RNA) three-dimensional structure prediction, docking, drug design, and molecular simulations due to their superiority in computational efficiency. Most of these applications strongly depend on the reasonable estimation of solvation free energy, which requires the accurate calculation of solvent accessible surface area (SASA). Although algorithms for SASA calculations with all-atom protein and RNA structures have been well-established, accurately estimating the SASA based on CG structures is extremely challenging. In this work, we developed a deep learning-based SASA estimator (DeepCGSA), which can provide almost perfect SASA estimation based on CG structures of protein and RNA molecules. Extensive testing analysis showed that for three types of widely used CG protein models, including the Cα-based, Cα-Cβ, and Martini models, the correlation coefficients between the predicted values and the reference values can be as high as 0.95-0.99, which perform dramatically better than available methods. In addition, the new method can be used for CG RNA structures and unfolded protein structures with much improved accuracy. We anticipate that DeepCGSA will be highly useful in the protein/RNA structure prediction, drug design, and other applications, in which accurate estimations of SASA for CG biomolecular structures are critically important.

Protein Structure Prediction from NMR Hydrogen-Deuterium Exchange Data

Amide hydrogen-deuterium exchange (HDX) has long been used to determine regional flexibility and binding sites in proteins; however, the data are too sparse for full structural characterization. Experiments that measure HDX rates, such as HDX-NMR, have far higher throughput compared to structure determination via X-ray crystallography, cryo-EM, or a full suite of NMR experiments. Data from HDX-NMR experiments encode information on the protein structure, making HDX a prime candidate to be supplemented by computational algorithms for protein structure prediction. We have developed a methodology to incorporate HDX-NMR data into ab initio protein structure prediction using the Rosetta software framework to predict structures based on experimental agreement. To demonstrate the efficacy of our algorithm, we examined 38 proteins with HDX-NMR data available, comparing the predicted model with and without the incorporation of HDX data into scoring. The root-mean-square deviation (rmsd, a measure of the average atomic distance between superimposed models) of the predicted model improved by 1.42 Å on average after incorporating the HDX-NMR data into scoring. The average rmsd improvement for the proteins where the selected model rmsd changed after incorporating HDX data was 3.63 Å, including one improvement of more than 11 Å and seven proteins improving by greater than 4 Å, with 12/15 proteins improving overall. Additionally, for independent verification, two proteins that were not part of the original benchmark were scored including HDX data, with a dramatic improvement of the selected model rmsd of nearly 9 Å for one of the proteins. Moreover, we have developed a confidence metric allowing us to successfully identify near-native models in the absence of a native structure. Improvement in model selection with a strong confidence measure demonstrates that protein structure prediction with HDX-NMR is a powerful tool which can be performed with minimal additional computational strain and expense.

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.

Hybrid methods for combined experimental and computational determination of protein structure

Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.

Allosteric Pathways Originating at Cysteine Residues in Regulators of G-Protein Signaling Proteins

Regulators of G-protein signaling (RGS) proteins play a central role in modulating signaling via G-protein coupled receptors (GPCRs). Specifically, RGS proteins bind to activated Gα subunits in G-proteins, accelerate the GTP hydrolysis, and thereby rapidly dampen GPCR signaling. Therefore, covalent molecules targeting conserved cysteine residues among RGS proteins have emerged as potential candidates to inhibit the RGS/Gα protein-protein interaction and enhance GPCR signaling. Although these inhibitors bind to conserved cysteine residues among RGS proteins, we have previously suggested [J. Am. Chem. Soc. 2018;140:3454–3460] that their potencies and specificities are related to differential protein dynamics among RGS proteins. Using data from all-atom molecular dynamics simulations, we reveal these differences in dynamics of RGS proteins by partitioning the protein structural space into a network of communities that allow allosteric signals to propagate along unique pathways originating at inhibitor binding sites and terminating at the RGS/Gα protein-protein interface.

Dramatic Shape Changes Occur as Cytochrome c Folds

Extensive experimental studies on the folding of cytochrome c (Cyt c) make this small protein an ideal target for atomic detailed simulations for the purposes of quantitatively characterizing the structural transitions and the associated time scales for folding to the native state from an ensemble of unfolded states. We use previously generated atomically detailed folding trajectories by the stochastic difference equation in length to calculate the time-dependent changes in the small-angle X-ray scattering (SAXS) profiles. Excellent agreement is obtained between experiments and simulations for the time-dependent SAXS spectra, allowing us to identify the structures of the folding intermediates, which shows that Cyt c reaches the native state by a sequential folding mechanism. Using the ensembles of structures along the folding pathways, we show that compaction and the sphericity of Cyt c change dramatically from the prolate ellipsoid shape in the unfolded state to the spherical native state. Our data, which are in unprecedented quantitative agreement with all aspects of time-resolved SAXS experiments, show that hydrophobic collapse and amide group protection coincide on the 100 microseconds time scale, which is in accordance with ultrafast hydrogen/deuterium exchange studies. Based on these results, we propose that compaction of polypeptide chains, accompanied by dramatic shape changes, is a universal characteristic of globular proteins, regardless of the underlying folding mechanism.

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.

Combination of HDX-MS and in silico modeling to study enzymatic reactivity and stereo-selectivity at different solvent conditions

The higher-order structure of a protein defines its function, and protein structural dynamics are often essential for protein binding and enzyme catalysis. Methods for protein characterization in solution are continuously being developed to understand and explore protein conformational changes with regards to function and activity. The goal of this study was to survey the use of combining HDX-MS global conformational screening with in silico modeling and continuous labeling peptide-level HDX-MS as an approach to highlight regions of interest within an enzyme required for biocatalytic processes. We surveyed in silico modeling correlated with peptide level HDX-MS experiments to characterize and localize transaminase enzyme structural dynamics at different conditions. This approach was orthogonally correlated with a global Size-Exclusion-HDX (SEC-HDX) screen for global conformational comparison and global alpha-helical content measurements by circular dichroism. Enzymatic activity and stereo-selectivity of transaminases were compared at different reaction-solution conditions that forced protein conformational changes by increasing acetonitrile concentration. The experimental peptide-level HDX-MS results demonstrated similar trends to the modeling data showing that certain regions remained folded in transaminases ATA-036 and ATA-303 with increasing acetonitrile concentration, which is also associated with shifting stereoselectivity. HDX modeling, SEC-HDX and CD experimental data showed that transaminase ATA-234 had the highest level of global unfolding with increasing acetonitrile concentration compared to the other two enzymes, which correlated with drastically reduced product conversion in transamination reaction. The combined HDX modeling/experimental workflow, based on enzymatic reactions studied at different conditions to induce changes in enzyme conformation, could be used as a tool to guide directed evolution efforts by identifying and focusing on the regions of an enzyme required for reaction product conversion and stereoselectivity.

Successes and challenges in simulating the folding of large proteins

Computational simulations of protein folding can be used to interpret experimental folding results, to design new folding experiments, and to test the effects of mutations and small molecules on folding. However, whereas major experimental and computational progress has been made in understanding how small proteins fold, research on larger, multidomain proteins, which comprise the majority of proteins, is less advanced. Specifically, large proteins often fold via long-lived partially folded intermediates, whose structures, potentially toxic oligomerization, and interactions with cellular chaperones remain poorly understood. Molecular dynamics based folding simulations that rely on knowledge of the native structure can provide critical, detailed information on folding free energy landscapes, intermediates, and pathways. Further, increases in computational power and methodological advances have made folding simulations of large proteins practical and valuable. Here, using serpins that inhibit proteases as an example, we review native-centric methods for simulating the folding of large proteins. These synergistic approaches range from Gō and related structure-based models that can predict the effects of the native structure on folding to all-atom-based methods that include side-chain chemistry and can predict how disease-associated mutations may impact folding. The application of these computational approaches to serpins and other large proteins highlights the successes and limitations of current computational methods and underscores how computational results can be used to inform experiments. These powerful simulation approaches in combination with experiments can provide unique insights into how large proteins fold and misfold, expanding our ability to predict and manipulate protein folding.

Regulator of G-protein signaling (RGS) proteins as drug targets: Progress and future potentials

G protein-coupled receptors (GPCRs) play critical roles in regulating processes such as cellular homeostasis, responses to stimuli, and cell signaling. Accordingly, GPCRs have long served as extraordinarily successful drug targets. It is therefore not surprising that the discovery in the mid-1990s of a family of proteins that regulate processes downstream of GPCRs generated great excitement in the field. This finding enhanced the understanding of these critical signaling pathways and provided potentially new targets for pharmacological intervention. These regulators of G-protein signaling (RGS) proteins were viewed by many as nodes downstream of GPCRs that could be targeted with small molecules to tune signaling processes. In this review, we provide a brief overview of the discovery of RGS proteins and of the gradual and continuing discovery of their roles in disease states, focusing particularly on cancer and neurological disorders. We also discuss high-throughput screening efforts that have led to the discovery first of peptide-based and then of small-molecule inhibitors targeting a subset of the RGS proteins. We explore the unique mechanisms of RGS inhibition these chemical tools have revealed and highlight the most up-to-date studies using these tools in animal experiments. Finally, we discuss the future opportunities in the field, as there are clearly more avenues left to be explored and potentials to be realized.

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.

An Interhelical Salt Bridge Controls Flexibility and Inhibitor Potency for Regulators of G-protein Signaling Proteins 4, 8, and 19

Regulators of G-protein signaling (RGS) proteins modulate receptor signaling by binding to activated G-protein α-subunits, accelerating GTP hydrolysis. Selective inhibition of RGS proteins increases G-protein activity and may provide unique tissue specificity. Thiadiazolidinones (TDZDs) are covalent inhibitors that act on cysteine residues to inhibit RGS4, RGS8, and RGS19. There is a correlation between protein flexibility and potency of inhibition by the TDZD 4-[(4- fluorophenyl)methyl]-2-(4-methylphenyl)-1,2,4-thiadiazolidine-3,5-dione (CCG-50014). In the context of a single conserved cysteine residue on the α ₄ helix, RGS19 is the most flexible and most potently inhibited by CCG-50014, followed by RGS4 and RGS8. In this work, we identify residues responsible for differences in both flexibility and potency of inhibition among RGS isoforms. RGS19 lacks a charged residue on the α ₄ helix that is present in RGS4 and RGS8. Introducing a negative charge at this position (L118D) increased the thermal stability of RGS19 and decreased the potency of inhibition of CCG-50014 by 8-fold. Mutations eliminating salt bridge formation in RGS8 and RGS4 decreased thermal stability in RGS8 and increased potency of inhibition of both RGS4 and RGS8 by 4- and 2-fold, respectively. Molecular dynamics simulations with an added salt bridge in RGS19 (L118D) showed reduced RGS19 flexibility. Hydrogen-deuterium exchange studies showed striking differences in flexibility in the α ₄ helix of RGS4, 8, and 19 with salt bridge-modifying mutations. These results show that the α ₄ salt bridge-forming residue controls flexibility in several RGS isoforms and supports a causal relationship between RGS flexibility and the potency of TDZD inhibitors. SIGNIFICANCE STATEMENT: Inhibitor potency is often viewed in relation to the static structure of a target protein binding pocket. Using both experimental and computation studies we assess determinants of dynamics and inhibitor potency for three different RGS proteins. A single salt bridge-forming residue determines differences in flexibility between RGS isoforms; mutations either increase or decrease protein motion with correlated alterations in inhibitor potency. This strongly suggests a causal relationship between RGS protein flexibility and covalent inhibitor potency.

Interlaboratory Comparison of Hydrogen-Deuterium Exchange Mass Spectrometry Measurements of the Fab Fragment of NISTmAb

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is an established, powerful tool for investigating protein-ligand interactions, protein folding, and protein dynamics. However, HDX-MS is still an emergent tool for quality control of biopharmaceuticals and for establishing dynamic similarity between a biosimilar and an innovator therapeutic. Because industry will conduct quality control and similarity measurements over a product lifetime and in multiple locations, an understanding of HDX-MS reproducibility is critical. To determine the reproducibility of continuous-labeling, bottom-up HDX-MS measurements, the present interlaboratory comparison project evaluated deuterium uptake data from the Fab fragment of NISTmAb reference material (PDB: 5K8A ) from 15 laboratories. Laboratories reported ∼89 800 centroid measurements for 430 proteolytic peptide sequences of the Fab fragment (∼78 900 centroids), giving ∼100% coverage, and ∼10 900 centroid measurements for 77 peptide sequences of the Fc fragment. Nearly half of peptide sequences are unique to the reporting laboratory, and only two sequences are reported by all laboratories. The majority of the laboratories (87%) exhibited centroid mass laboratory repeatability precisions of ⟨ s^Lab⟩ ≤ (0.15 ± 0.01) Da (1σ_x̅). All laboratories achieved ⟨s^Lab⟩ ≤ 0.4 Da. For immersions of protein at T_HDX = (3.6 to 25) °C and for D₂O exchange times of t_HDX = (30 s to 4 h) the reproducibility of back-exchange corrected, deuterium uptake measurements for the 15 laboratories is σ_{reproducibility}^{15 Laboratories}( t_HDX) = (9.0 ± 0.9) % (1σ). A nine laboratory cohort that immersed samples at T_HDX = 25 °C exhibited reproducibility of σ_{reproducibility}^25C cohort( t_HDX) = (6.5 ± 0.6) % for back-exchange corrected, deuterium uptake measurements.

The Symmetric Difference Distance: A New Way to Evaluate the Evolution of Interfaces along Molecular Dynamics Trajectories; Application to Influenza Hemagglutinin

We propose a new and easy approach to evaluate structural dissimilarities between frames issued from molecular dynamics, and we test this methodology on human hemagglutinin. This protein is responsible for the entry of the influenza virus into the host cell by endocytosis, and this virus causes seasonal epidemics of infectious disease, which can be estimated to result in hundreds of thousands of deaths each year around the world. We computed the three interfaces between the three protomers of the hemagglutinin H1 homotrimer (PDB code: 1RU7) for each of its conformations generated from molecular dynamics simulation. For each conformation, we considered the set of residues involved in the union of these three interfaces. The dissimilarity between each pair of conformations was measured with our new methodology, the symmetric difference distance between the associated set of residues. The main advantages of the full procedure are: (i) it is parameter free; (ii) no spatial alignment is needed and (iii) it is simple enough so that it can be implemented by a beginner in programming. It is shown to be a relevant tool to follow the evolution of the conformation along the molecular dynamics trajectories.

Interplay of cysteine exposure and global protein dynamics in small-molecule recognition by a regulator of G-protein signaling protein

Regulator of G protein signaling (RGS) proteins play a pivotal role in regulation of G protein-coupled receptor (GPCR) signaling and are therefore becoming an increasingly important therapeutic target. Recently discovered thiadiazolidinone (TDZD) compounds that target cysteine residues have shown different levels of specificities and potencies for the RGS4 protein, thereby suggesting intrinsic differences in dynamics of this protein upon binding of these compounds. In this work, we investigated using atomistic molecular dynamics (MD) simulations the effect of binding of several small-molecule inhibitors on perturbations and dynamical motions in RGS4. Specifically, we studied two conformational models of RGS4 in which a buried cysteine residue is solvent-exposed due to side-chain motions or due to flexibility in neighboring helices. We found that TDZD compounds with aromatic functional groups perturb the RGS4 structure more than compounds with aliphatic functional groups. Moreover, small-molecules with aromatic functional groups but lacking sulfur atoms only transiently reside within the protein and spontaneously dissociate to the solvent. We further measured inhibitory effects of TDZD compounds using a protein-protein interaction assay on a single-cysteine RGS4 protein showing trends in potencies of compounds consistent with our simulation studies. Thermodynamic analyses of RGS4 conformations in the apo-state and on binding to TDZD compounds revealed links between both conformational models of RGS4. The exposure of cysteine side-chains appears to facilitate initial binding of TDZD compounds followed by migration of the compound into a bundle of four helices, thereby causing allosteric perturbations in the RGS/Gα protein-protein interface.