Prediction of native-state hydrogen exchange from perfectly funneled energy landscapes

Analysis of proteins in the light of mutations

Proteins have evolved through mutations-amino acid substitutions-since life appeared on Earth, some 109 years ago. The study of these phenomena has been of particular significance because of their impact on protein stability, function, and structure. This study offers a new viewpoint on how the most recent findings in these areas can be used to explore the impact of mutations on protein sequence, stability, and evolvability. Preliminary results indicate that: (1) mutations can be viewed as sensitive probes to identify 'typos' in the amino-acid sequence, and also to assess the resistance of naturally occurring proteins to unwanted sequence alterations; (2) the presence of 'typos' in the amino acid sequence, rather than being an evolutionary obstacle, could promote faster evolvability and, in turn, increase the likelihood of higher protein stability; (3) the mutation site is far more important than the substituted amino acid in terms of the marginal stability changes of the protein, and (4) the unpredictability of protein evolution at the molecular level-by mutations-exists even in the absence of epistasis effects. Finally, the Darwinian concept of evolution "descent with modification" and experimental evidence endorse one of the results of this study, which suggests that some regions of any protein sequence are susceptible to mutations while others are not. This work contributes to our general understanding of protein responses to mutations and may spur significant progress in our efforts to develop methods to accurately forecast changes in protein stability, their propensity for metamorphism, and their ability to evolve.

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

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

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.

Proteins' Evolution upon Point Mutations

As the reader must be already aware, state-of-the-art protein folding prediction methods have reached a smashing success in their goal of accurately determining the three-dimensional structures of proteins. Yet, a solution to simple problems such as the effects of protein point mutations on their (i) native conformation; (ii) marginal stability; (iii) ensemble of high-energy nativelike conformations; and (iv) metamorphism propensity and, hence, their evolvability, remains as an unsolved problem. As a plausible solution to the latter, some properties of the amide hydrogen-deuterium exchange, a highly sensitive probe of the structure, stability, and folding of proteins, are assessed from a new perspective. The preliminary results indicate that the protein marginal stability change upon point mutations provides the necessary and sufficient information to estimate, through a Boltzmann factor, the evolution of the amide hydrogen exchange protection factors and, consequently, that of the ensemble of folded conformations coexisting with the native state. This work contributes to our general understanding of the effects of point mutations on proteins and may spur significant progress in our efforts to develop methods to determine the appearance of new folds and functions accurately.

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.

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.

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.

Interdomain flexibility and interfacial integrity of β-lactamase inhibitory protein (BLIP) modulate its binding to class A β-lactamases

β-Lactamase inhibitory protein (BLIP) consists of a tandem repeat of αβ domains conjugated by an interdomain loop and can effectively bind and inactivate class A β-lactamases, which are responsible for resistance of bacteria to β-lactam antibiotics. The varied ability of BLIP to bind different β-lactamases and the structural determinants for significant enhancement of BLIP variants with a point mutation are poorly understood. Here, we investigated the conformational dynamics of BLIP upon binding to three clinically prevalent class A β-lactamases (TEM1, SHV1, and PC1) with dissociation constants between subnanomolar and micromolar. Hydrogen deuterium exchange mass spectrometry revealed that the flexibility of the interdomain region was significantly suppressed upon strong binding to TEM1, but was not significantly changed upon weak binding to SHV1 or PC1. E73M and K74G mutations in the interdomain region improved binding affinity toward SHV1 and PC1, respectively, showing significantly increased flexibility of the interdomain region compared to the wild-type and favorable conformational changes upon binding. In contrast, more rigidity of the interfacial loop 135–145 was observed in these BLIP mutants in both free and bound states. Consistently, molecular dynamics simulations of BLIP exhibited drastic changes in the flexibility of the loop 135–145 in all complexes. Our results indicated for the first time that higher flexibility of the interdomain linker, as well as more rigidity of the interfacial loop 135–145, could be desirable determinants for enhancing inhibition of BLIP to class A β-lactamases. Together, these findings provide unique insights into the design of enhanced inhibitors.

Thoughts on the Protein's Native State

The presence of metamorphism in the protein's native state is not yet fully understood. To shed light on this issue, we present an assessment, in terms of the amide hydrogen exchange protection factor, that aims to determine the possible existence of structural fluctuations in the native-state consistent with both the upper-bound marginal stability of proteins and the presence of metamorphism. The preliminary results enable us to conclude that the native-state metamorphism is, indeed, more probable than previously thought.

Structural and Energetic Characterization of the Denatured State from the Perspectives of Peptides, the Coil Library, and Intrinsically Disordered Proteins

The α and polyproline II (PPII) basins are the two most populated regions of the Ramachandran map when constructed from the protein coil library, a widely used denatured state model built from the segments of irregular structure found in the Protein Data Bank. This indicates the α and PPII conformations are dominant components of the ensembles of denatured structures that exist in solution for biological proteins, an observation supported in part by structural studies of short, and thus unfolded, peptides. Although intrinsic conformational propensities have been determined experimentally for the common amino acids in short peptides, and estimated from surveys of the protein coil library, the ability of these intrinsic conformational propensities to quantitatively reproduce structural behavior in intrinsically disordered proteins (IDPs), an increasingly important class of proteins in cell function, has thus far proven elusive to establish. Recently, we demonstrated that the sequence dependence of the mean hydrodynamic size of IDPs in water and the impact of heat on the coil dimensions, provide access to both the sequence dependence and thermodynamic energies that are associated with biases for the α and PPII backbone conformations. Here, we compare results from peptide-based studies of intrinsic conformational propensities and surveys of the protein coil library to those of the sequence-based analysis of heat effects on IDP hydrodynamic size, showing that a common structural and thermodynamic description of the protein denatured state is obtained.

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.

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.

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.

Impact of Heat on Coil Hydrodynamic Size Yields the Energetics of Denatured State Conformational Bias

Conformational equilibria in the protein denatured state have key roles regulating folding, stability, and function. The extent of conformational bias in the protein denatured state under folding conditions, however, has thus far proven elusive to quantify, particularly with regard to its sequence dependence and energetic character. To better understand the structural preferences of the denatured state, we analyzed both the sequence dependence to the mean hydrodynamic size of disordered proteins in water and the impact of heat on the coil dimensions, showing that the sequence dependence and thermodynamic energies associated with intrinsic biases for the α and polyproline II (PPII) backbone conformations can be obtained. Experiments that evaluate how the hydrodynamic size changes with compositional changes in the protein reveal amino acid specific preferences for PPII that are in good quantitative agreement with calorimetry-measured values from unfolded peptides and those inferred by survey of the protein coil library. At temperatures above 25 °C, the denatured state follows the predictions of a PPII-dominant ensemble. Heat effects on coil hydrodynamic size indicate the α bias is comparable to the PPII bias at cold temperatures. Though historically thought to give poor resolution to structural details, the hydrodynamic size of the unfolded state is found to be an effective reporter on the extent of the biases for the α and PPII backbone conformations.

Folding and Dynamics Are Strongly pH-Dependent in a Psychrophile Frataxin

Protein dynamics, folding, and thermodynamics represent a central aspect of biophysical chemistry. pH, temperature, and denaturant perturbations inform our understanding of diverse contributors to stability and rates. In this work, we performed a thermodynamic analysis using a combined experimental and computational approach to gain insights into the role of electrostatics in the folding reaction of a psychrophile frataxin variant from Psychromonas ingrahamii. This folding reaction is strongly modulated by pH with a single, narrow, and well-defined transition state with ∼80% compactness, ∼70% electrostatic interactions, and ∼60% hydration shell compared to the native state (αD = 0.82, αH = 0.67, and αΔCp = 0.59). Our results are best explained by a two-proton/two-state model with very different pKa values of the native and denatured states (∼5.5 and ∼8.0, respectively). As a consequence, the stability strongly increases from pH 8.0 to 6.0 (|ΔΔG°| = 5.2 kcal mol-1), mainly because of a decrease in the TΔS°. Variation of ΔH° and ΔS° at pH below 7.0 is dominated by a change in ΔHf⧧ and ΔSf⧧, while at pH above 7.0, it is governed by ΔHu⧧ and ΔSu⧧. Molecular dynamics simulations showed that these pH modulations could be explained by the fluctuations of two regions, rich in electrostatic contacts, whose dynamics are pH-dependent and motions are strongly correlated. Results presented herein contribute to the understanding of the stability and dynamics of this frataxin variant, pointing to an intrinsic feature of the family topology to support different folding mechanisms.

Protein dynamics revealed by hydrogen/deuterium exchange mass spectrometry: Correlation between experiments and simulation

Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a powerful technique for studying protein dynamics, which is an important factor governing protein functions. However, the process of hydrogen/deuterium exchange (HDX) of proteins is highly complex and the underlying mechanism has not yet been fully elucidated. Meanwhile, molecular dynamics (MD) simulation is a computational technique that can be used to elucidate HDX behaviour on proteins and facilitate interpretation of HDX-MS data. This article aims to summarize the current understandings on the mechanism of HDX and its correlation with MD simulation, to discuss the recent developments in the techniques of HDX-MS and MD simulation and to extend the perspectives of these two techniques in protein dynamics study.

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.

A Metastable Contact and Structural Disorder in the Estrogen Receptor Transactivation Domain

The N-terminal transactivation domain (NTD) of estrogen receptor alpha, a well-known member of the family of intrinsically disordered proteins, mediates the receptor's transactivation function. However, an accurate molecular dissection of NTD's structure-function relationships remains elusive. Here, we show that the NTD adopts a mostly disordered, unexpectedly compact conformation that undergoes structural expansion on chemical denaturation. By combining small-angle X-ray scattering, hydroxyl radical protein footprinting, and computational modeling, we derive the ensemble-structures of the NTD and determine its ensemble-contact map revealing metastable long-range contacts, e.g., between residues I33 and S118. We show that mutation at S118, a known phosphorylation site, promotes conformational changes and increases coactivator binding. We further demonstrate via fluorine-19 (¹⁹F) nuclear magnetic resonance that mutations near I33 alter ¹⁹F chemical shifts at S118, confirming the proposed I33-S118 contact in the ensemble of structural disorder. These findings extend our understanding of how specific contact metastability mediates critical functions of disordered proteins.

Accurate Prediction of Amide Exchange in the Fast Limit Reveals Thrombin Allostery

Amide hydrogen/deuterium exchange mass spectrometry (HDXMS) of proteins has become extremely popular for identifying ligand-binding sites, protein-protein interactions, intrinsic disorder, and allosteric changes upon protein modification. Such phenomena are revealed when amide exchange is measured in the fast limit, that is, within a few minutes of exchange in deuterated buffer. The HDXMS data have a resolution of the length of peptides and are difficult to interpret because many different phenomena lead to changes in hydrogen/deuterium exchange. We present a quantitative analysis of accelerated molecular dynamics simulations that provides impressive agreement with peptide-length HDXMS data. Comparative analysis of thrombin and a single-point mutant reveals that the simulation analysis can distinguish the subtle differences in exchange due to mutation. In addition, the results provide a deeper understanding of the underlying changes in dynamics revealed by the HDXMS that extend far from the site of mutation.

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.

Evolved Minimal Frustration in Multifunctional Biomolecules

Protein folding is often viewed in terms of a funneled potential or free energy landscape. A variety of experiments now indicate the existence of multifunnel landscapes, associated with multifunctional biomolecules. Here, we present evidence that these systems have evolved to exhibit the minimal number of funnels required to fulfill their cellular functions, suggesting an extension to the principle of minimum frustration. We find that minimal disruptive mutations result in additional funnels, and the associated structural ensembles become more diverse. The same trends are observed in an atomic cluster. These observations suggest guidelines for rational design of engineered multifunctional biomolecules.

High-Resolution Hydroxyl Radical Protein Footprinting: Biophysics Tool for Drug Discovery

Hydroxyl radical footprinting (HRF) of proteins with mass spectrometry (MS) is a widespread approach for assessing protein structure. Hydroxyl radicals react with a wide variety of protein side chains, and the ease with which radicals can be generated (by radiolysis or photolysis) has made the approach popular with many laboratories. As some side chains are less reactive and thus cannot be probed, additional specific and nonspecific labeling reagents have been introduced to extend the approach. At the same time, advances in liquid chromatography and MS approaches permit an examination of the labeling of individual residues, transforming the approach to high resolution. Lastly, advances in understanding of the chemistry of the approach have led to the determination of absolute protein topologies from HRF data. Overall, the technology can provide precise and accurate measures of side-chain solvent accessibility in a wide range of interesting and useful contexts for the study of protein structure and dynamics in both academia and industry.

An overview of hydrogen deuterium exchange mass spectrometry (HDX-MS) in drug discovery

INTRODUCTION

Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a powerful methodology to study protein dynamics, protein folding, protein-protein interactions, and protein small molecule interactions. The development of novel methodologies and technical advancements in mass spectrometers has greatly expanded the accessibility and acceptance of this technique within both academia and industry. Areas covered: This review examines the theoretical basis of how amide exchange occurs, how different mass spectrometer approaches can be used for HDX-MS experiments, as well as the use of HDX-MS in drug development, specifically focusing on how HDX-MS is used to characterize bio-therapeutics, and its use in examining protein-protein and protein small molecule interactions. Expert opinion: HDX-MS has been widely accepted within the pharmaceutical industry for the characterization of bio-therapeutics as well as in the mapping of antibody drug epitopes. However, there is room for this technique to be more widely used in the drug discovery process. This is particularly true in the use of HDX-MS as a complement to other high-resolution structural approaches, as well as in the development of small molecule therapeutics that can target both active-site and allosteric binding sites.

Fast Protein Translation Can Promote Co- and Posttranslational Folding of Misfolding-Prone Proteins

Chemical kinetic modeling has previously been used to predict that fast-translating codons can enhance cotranslational protein folding by helping to avoid misfolded intermediates. Consistent with this prediction, protein aggregation in yeast and worms was observed to increase when translation was globally slowed down, possibly due to increased cotranslational misfolding. Observation of similar behavior in molecular simulations would confirm predictions from the simpler chemical kinetic model and provide a molecular perspective on cotranslational folding, misfolding, and the impact of translation speed on these processes. All-atom simulations cannot reach the timescales relevant to protein synthesis, and most conventional structure-based coarse-grained models do not allow for nonnative structure formation. Here, we introduce a protocol to incorporate misfolding using the functional forms of publicly available force fields. With this model we create two artificial proteins that are capable of undergoing structural transitions between a native and a misfolded conformation and simulate their synthesis by the ribosome. Consistent with the chemical kinetic predictions, we find that rapid synthesis of misfolding-prone nascent-chain segments increases the fraction of folded proteins by kinetically partitioning more molecules through on-pathway intermediates, decreasing the likelihood of sampling misfolded conformations. Novel to this study, to our knowledge, we observe that differences in protein dynamics, arising from different translation-elongation schedules, can persist long after the nascent protein has been released from the ribosome, and that a sufficient level of energetic frustration is needed for fast-translating codons to be beneficial for folding. These results provide further evidence that fast-translating codons can be as biologically important as pause sites in coordinating cotranslational folding.

Unusual dimerization of a BcCsp mutant leads to reduced conformational dynamics

Cold shock proteins (Csp) constitute a family of ubiquitous small proteins that act as RNA-chaperones to avoid cold-induced termination of translation. All members contain two subdomains composed of 2 and 3 β-strands, respectively, which are connected by a hinge loop and fold into a β-barrel. Bacillus caldolyticus Csp (BcCsp) is one of the most studied members of the family in terms of its folding, function, and structure. This protein has been described as a monomer in solution, although a recent crystal structure showed dimerization via domain swapping (DS). In contrast, other cold shock proteins of the same fold are known to dimerize in a nonswapped arrangement. Hypothesizing that reducing the size of the hinge loop may promote swapping as in several other DS proteins with different folds we deleted two residues from these region (BcCsp∆36-37), leading to a protein in monomer-dimer equilibrium with similar folding stability to that of the wild-type. Strikingly, the crystal structure of BcCsp∆36-37 revealed a nonswapped dimer with its interface located at the nucleic acid-binding surface, showing that the deletion led to structural consequences far from the perturbation site. Concomitantly, circular dichroism experiments on BcCsp∆36-37 demonstrated that binding of the oligonucleotide hexathymidine disrupts the dimer. Additionally, HDXMS shows a protective effect on the protein structure upon dimerization, where the resulting interactions between ligand-binding surfaces in the dimer reduced the extent of exchange throughout the whole protein. Our work provides evidence of the complex interplay between conformational dynamics, deletions, and oligomerization within the Csp protein family.

DATABASES

Structural data are available in the Protein Data Bank under accession number 5JX4.

Piecing Together the Allosteric Patterns of Chaperonin GroEL

Despite considerable efforts, elucidating the allostery of large macromolecular assemblies at a molecular level in solution remains technically challenging due to its structural complexity. Here we have employed an approach combining amide backbone hydrogen/deuterium exchange coupled with mass spectrometry, fluorescence spectroscopy, and molecular simulations to characterize allosteric patterns of chaperonin GroEL, an ∼800 kDa tetradecamer from E. coli. Using available crystal structures of GroEL, we quantitatively map out GroEL allosteric changes in solution by resolving exchange behaviors of 133 overlapping proteolytic peptides with more than 95% sequence coverage. This comprehensive analysis gives a refined resolution down to five residues to pilot the GroEL allosteric determinants, of which the localized dynamics is monitored by tryptophan-mutated GroEL. Furthermore, the GroEL conformational transition is evaluated by molecular dynamics simulations with an atomic-interaction-based coarse-grained model. Collectively, we provide a practical methodology to analyze GroEL allostery in solution.

Coarse-Grained Conformational Sampling of Protein Structure Improves the Fit to Experimental Hydrogen-Exchange Data

Monitoring hydrogen/deuterium exchange (HDX) undergone by a protein in solution produces experimental data that translates into valuable information about the protein's structure. Data produced by HDX experiments is often interpreted using a crystal structure of the protein, when available. However, it has been shown that the correspondence between experimental HDX data and crystal structures is often not satisfactory. This creates difficulties when trying to perform a structural analysis of the HDX data. In this paper, we evaluate several strategies to obtain a conformation providing a good fit to the experimental HDX data, which is a premise of an accurate structural analysis. We show that performing molecular dynamics simulations can be inadequate to obtain such conformations, and we propose a novel methodology involving a coarse-grained conformational sampling approach instead. By extensively exploring the intrinsic flexibility of a protein with this approach, we produce a conformational ensemble from which we extract a single conformation providing a good fit to the experimental HDX data. We successfully demonstrate the applicability of our method to four small and medium-sized proteins.

A Residue-Resolved Bayesian Approach to Quantitative Interpretation of Hydrogen-Deuterium Exchange from Mass Spectrometry: Application to Characterizing Protein-Ligand Interactions

Characterization of interactions between proteins and other molecules is crucial for understanding the mechanisms of action of biological systems and, thus, drug discovery. An increasingly useful approach to mapping these interactions is measurement of hydrogen/deuterium exchange (HDX) using mass spectrometry (HDX-MS), which measures the time-resolved deuterium incorporation of peptides obtained by enzymatic digestion of the protein. Comparison of exchange rates between apo- and ligand-bound conditions results in a mapping of the differential HDX (ΔHDX) of the ligand. Residue-level analysis of these data, however, must account for experimental error, sparseness, and ambiguity due to overlapping peptides. Here, we propose a Bayesian method consisting of a forward model, noise model, prior probabilities, and a Monte Carlo sampling scheme. This method exploits a residue-resolved exponential rate model of HDX-MS data obtained from all peptides simultaneously, and explicitly models experimental error. The result is the best possible estimate of ΔHDX magnitude and significance for each residue given the data. We demonstrate the method by revealing richer structural interpretation of ΔHDX data on two nuclear receptors: vitamin D-receptor (VDR) and retinoic acid receptor gamma (RORγ). The method is implemented in HDX Workbench and as a standalone module of the open source Integrative Modeling Platform.

Protein Folding and Structure Prediction from the Ground Up II: AAWSEM for α/β Proteins

The atomistic associative memory, water mediated, structure and energy model (AAWSEM) is an efficient coarse-grained force field with transferable tertiary interactions that incorporates local in sequence energetic biases using structural information derived from all-atom simulations of long segments of the protein. For α helical proteins, the accuracy of structure prediction using AAWSEM has been established previously. In this article, we examine the capability of AAWSEM to predict the structure of α/β proteins. We also elaborate on an iterative approach that uses the structures from a first round of AAWSEM simulation as fragment memories. This iterative scheme improves the quality of the structure prediction and makes the free energy profile more funneled toward native configurations. We explore the use of clustering analyses as a way of evaluating the confidence in various structure prediction models. Clustering using a local relative order parameter (mutual Q) of the predicted structural ensemble turns out to be optimal. The tightest cluster according to mutual Q generally has the most correctly folded structure. Since there is no bioinformatic input, AAWSEM amounts to an ab initio protein structure prediction method that combines the efficiency of coarse-grained simulations with the local structural accuracy that can be achieved from all-atom simulations.

Quantitative mapping of protein structure by hydroxyl radical footprinting-mediated structural mass spectrometry: a protection factor analysis

Measurements from hydroxyl radical footprinting (HRF) provide rich information about the solvent accessibility of amino acid side chains of a protein. Traditional HRF data analyses focus on comparing the difference in the modification/footprinting rate of a specific site to infer structural changes across two protein states, e.g., between a free and ligand-bound state. However, the rate information itself is not fully used for the purpose of comparing different protein sites within a protein on an absolute scale. To provide such a cross-site comparison, we present a new, to our knowledge, data analysis algorithm to convert the measured footprinting rate constant to a protection factor (PF) by taking into account the known intrinsic reactivity of amino acid side chain. To examine the extent to which PFs can be used for structural interpretation, this PF analysis is applied to three model systems where radiolytic footprinting data are reported in the literature. By visualizing structures colored with the PF values for individual peptides, a rational view of the structural features of various protein sites regarding their solvent accessibility is revealed, where high-PF regions are buried and low-PF regions are more exposed to the solvent. Furthermore, a detailed analysis correlating solvent accessibility and local structural contacts for gelsolin shows a statistically significant agreement between PF values and various structure measures, demonstrating that the PFs derived from this PF analysis readily explain fundamental HRF rate measurements. We also tested this PF analysis on alternative, chemical-based HRF data, showing improved correlations of structural properties of a model protein barstar compared to examining HRF rate data alone. Together, this PF analysis not only permits a novel, to our knowledge, approach of mapping protein structures by using footprinting data, but also elevates the use of HRF measurements from a qualitative, cross-state comparison to a quantitative, cross-site assessment of protein structures in the context of individual conformational states of interest.

Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis

Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure, and dynamics. More recently, hydrogen exchange mass spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from molecular dynamics (MD) simulation snapshots is used to determine partitioning over bonded and nonbonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models.

How amide hydrogens exchange in native proteins

Amide hydrogen exchange (HX) is widely used in protein biophysics even though our ignorance about the HX mechanism makes data interpretation imprecise. Notably, the open exchange-competent conformational state has not been identified. Based on analysis of an ultralong molecular dynamics trajectory of the protein BPTI, we propose that the open (O) states for amides that exchange by subglobal fluctuations are locally distorted conformations with two water molecules directly coordinated to the N-H group. The HX protection factors computed from the relative O-state populations agree well with experiment. The O states of different amides show little or no temporal correlation, even if adjacent residues unfold cooperatively. The mean residence time of the O state is ∼100 ps for all examined amides, so the large variation in measured HX rate must be attributed to the opening frequency. A few amides gain solvent access via tunnels or pores penetrated by water chains including native internal water molecules, but most amides access solvent by more local structural distortions. In either case, we argue that an overcoordinated N-H group is necessary for efficient proton transfer by Grotthuss-type structural diffusion.

Functional dynamics of hexameric helicase probed by hydrogen exchange and simulation

The biological function of large macromolecular assemblies depends on their structure and their dynamics over a broad range of timescales; for this reason, it is a significant challenge to investigate these assemblies using conventional experimental techniques. One of the most promising experimental techniques is hydrogen-deuterium exchange detected by mass spectrometry. Here, we describe to our knowledge a new computational method for quantitative interpretation of deuterium exchange kinetics and apply it to a hexameric viral helicase P4 that unwinds and translocates RNA into a virus capsid at the expense of ATP hydrolysis. Room-temperature dynamics probed by a hundred nanoseconds of all-atom molecular dynamics simulations is sufficient to predict the exchange kinetics of most sequence fragments and provide a residue-level interpretation of the low-resolution experimental results. The strategy presented here is also a valuable tool to validate experimental data, e.g., assignments, and to probe mechanisms that cannot be observed by x-ray crystallography, or that occur over timescales longer than those that can be realistically simulated, such as the opening of the hexameric ring.

Learning To Fold Proteins Using Energy Landscape Theory

This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models that are optimized using lessons learned from the energy landscape theory of protein folding. We also present a review of the results of the AMH/AMC/AMW/AWSEM family of coarse-grained molecular dynamics protein folding models to illustrate the points covered in the first part of the article. Accurate coarse-grained structure prediction models can be used to investigate a wide range of conceptual and mechanistic issues outside of protein structure prediction; specifically, the paper concludes by reviewing how AWSEM has in recent years been able to elucidate questions related to the unusual kinetic behavior of artificially designed proteins, multidomain protein misfolding, and the initial stages of protein aggregation.