Prediction of amino acid residues protected from hydrogen-deuterium exchange in a protein chain

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.

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.

Probing Cascade complex composition and stability using native mass spectrometry techniques

Adaptive prokaryotic immune systems rely on clustered regularly interspaced short palindromic repeats and their associated genes to provide the components necessary to clear infection by foreign genetic elements. These immune systems are based on highly specific nucleases that bind DNA or RNA and, upon sequence recognition, degrade the bound nucleic acid. Because of their specificity, CRISPR-Cas systems are being co-opted to edit genes in eukaryotic cells. While the general function of these systems is well understood, an understanding of mechanistic details to facilitate engineering and application to this new arena remains a topic of intense study. Here, we present two methods that have been successfully used to study the structure and mechanism of the Type IE CRISPR system, Cascade, from Escherichia coli. We provide the protocol for a typical native mass spectrometry experiment which, because it allows for analysis of a protein complex without disruption of the noncovalent interactions within the complex, can be used to determine complex composition, architecture, and relative affinity between subunits. We, also, provide the protocol for intact protein hydrogen-deuterium exchange mass spectrometry, which provides insight into the overall conformational stability of the complex and changes in complex stability based on conditions such as substrate binding. Investigating the solution-phase structure, stability, and dynamics of these complexes improves the overall understanding of the mechanism facilitating engineered adjustments to function or utility.

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.

Biological insights from hydrogen exchange mass spectrometry

Over the past two decades, hydrogen exchange mass spectrometry (HXMS) has achieved the status of a widespread and routine approach in the structural biology toolbox. The ability of hydrogen exchange to detect a range of protein dynamics coupled with the accessibility of mass spectrometry to mixtures and large complexes at low concentrations result in an unmatched tool for investigating proteins challenging to many other structural techniques. Recent advances in methodology and data analysis are helping HXMS deliver on its potential to uncover the connection between conformation, dynamics and the biological function of proteins and complexes. This review provides a brief overview of the HXMS method and focuses on four recent reports to highlight applications that monitor structure and dynamics of proteins and complexes, track protein folding, and map the thermodynamics and kinetics of protein unfolding at equilibrium. These case studies illustrate typical data, analysis and results for each application and demonstrate a range of biological systems for which the interpretation of HXMS in terms of structure and conformational parameters provides unique insights into function. This article is part of a Special Issue entitled: Mass spectrometry in structural biology.

Protein hydrogen exchange: testing current models

To investigate the determinants of protein hydrogen exchange (HX), HX rates of most of the backbone amide hydrogens of Staphylococcal nuclease were measured by NMR methods. A modified analysis was used to improve accuracy for the faster hydrogens. HX rates of both near surface and well buried hydrogens are spread over more than 7 orders of magnitude. These results were compared with previous hypotheses for HX rate determination. Contrary to a common assumption, proximity to the surface of the native protein does not usually produce fast exchange. The slow HX rates for unprotected surface hydrogens are not well explained by local electrostatic field. The ability of buried hydrogens to exchange is not explained by a solvent penetration mechanism. The exchange rates of structurally protected hydrogens are not well predicted by algorithms that depend only on local interactions or only on transient unfolding reactions. These observations identify some of the present difficulties of HX rate prediction and suggest the need for returning to a detailed hydrogen by hydrogen analysis to examine the bases of structure-rate relationships, as described in the companion paper (Skinner et al., Protein Sci 2012;21:996-1005).

OneG: a computational tool for predicting cryptic intermediates in the unfolding kinetics of proteins under native conditions

Understanding the relationships between conformations of proteins and their stabilities is one key to address the protein folding paradigm. The free energy change (ΔG) of unfolding reactions of proteins is measured by traditional denaturation methods and native hydrogen-deuterium (H/D) exchange methods. However, the free energy of unfolding (ΔG(U)) and the free energy of exchange (ΔG(HX)) of proteins are not in good agreement, though the experimental conditions of both methods are well matching to each other. The anomaly is due to any one or combinations of the following reasons: (i) effects of cis-trans proline isomerisation under equilibrium unfolding reactions of proteins (ii) inappropriateness in accounting the baselines of melting curves (iii) presence of cryptic intermediates, which may elude the melting curve analysis and (iv) existence of higher energy metastable states in the H/D exchange reactions of proteins. Herein, we have developed a novel computational tool, OneG, which accounts the discrepancy between ΔG(U) and ΔG(HX) of proteins by systematically accounting all the four factors mentioned above. The program is fully automated and requires four inputs: three-dimensional structures of proteins, ΔG(U), ΔG(U)(*) and residue-specific ΔG(HX) determined under EX2-exchange conditions in the absence of denaturants. The robustness of the program has been validated using experimental data available for proteins such as cytochrome c and apocytochrome b(562) and the data analyses revealed that cryptic intermediates of the proteins detected by the experimental methods and the cryptic intermediates predicted by the OneG for those proteins were in good agreement. Furthermore, using OneG, we have shown possible existence of cryptic intermediates and metastable states in the unfolding pathways of cardiotoxin III and cobrotoxin, respectively, which are homologous proteins. The unique application of the program to map the unfolding pathways of proteins under native conditions have been brought into fore and the program is publicly available at http://sblab.sastra.edu/oneg.html.