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

Structural proteomics defines a sequential priming mechanism for the progesterone receptor

The progesterone receptor (PR) is a steroid-responsive nuclear receptor with two isoforms: PR-A and PR-B. Disruption of PR-A:PR-B signaling is associated with breast cancer through interactions with oncogenic co-regulatory proteins (CoRs). However, molecular details of isoform-specific PR-CoR interactions remain poorly understood. Using structural mass spectrometry, we investigate the sequential binding mechanism of purified full-length PR and intact CoRs, steroid receptor coactivator 3 (SRC3) and p300, as complexes on target DNA. Our findings reveal selective CoR NR-box binding by PR and unique interaction surfaces between PR and CoRs during complex assembly, providing a structural basis for CoR sequential binding on PR. Antagonist-bound PR showed persistent CoR interactions, challenging the classical model of nuclear receptor activation and repression. In this work, we offer a peptide-level perspective on the organization of the PR transcriptional complex and infer the mechanisms behind the interactions of these proteins, both in active and inactive conformations.

Tutorial on integrative spatiotemporal modeling by integrative modeling platform

Cells function through dynamic interactions between macromolecules. Detailed characterization of the dynamics of large biomolecular systems is often not feasible by individual biophysical methods. In such cases, it may be possible to compute useful models by integrating multiple sources of information. We have previously developed an integrative method to model dynamic processes by computing biomolecular heterogeneity at fixed time points, then generating static integrative structural modes for each of these heterogeneity models, and finally connecting these static models to produce a scored trajectory model that depicts the process. Here, we demonstrate how to compute, score, and assess these integrative spatiotemporal models using our open-source Integrative Modeling Platform (IMP) program (https://integrativemodeling.org/).

Kingfisher: An open-sourced web-based platform for the analysis of hydrogen exchange mass spectrometry data

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is now a critical tool in molecular biology and structural proteomics. It is routinely used to probe protein and conformational dynamics through a well-established experiment where amide hydrogens exchange with deuterium atoms in a buffer containing D2O. Although there have been numerous advances in the field, data analysis still poses challenges mainly due to the need for manual curation of the data and the lack of standardized statistics and accessible software. In response, we developed Kingfisher, an open-source, user-friendly, web-based solution that facilitates downstream analysis using well-established statistics and provides advanced high-resolution representations of the HDX results. Kingfisher is able to read data directly as exported from common software packages and usually takes less than a minute to run the analysis, without the need to download the raw code or install any software. We foresee Kingfisher as a valuable tool for both newcomers and experts in the field of Hydrogen Exchange Mass Spectrometry. Kingfisher is available to all users as an interactive web application at https://kingfisher.wustl.edu/.

Computational Tools for Hydrogen-Deuterium Exchange Mass Spectrometry Data Analysis

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

Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver

Ion mobility coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM). Although there are several computational methods for predicting CCSIM based on protein structures, including our previously developed projection approximation using rough circular shapes (PARCS), the process usually requires prior experience with the command-line interface. To overcome this challenge, here we present a web application on the Rosetta Online Server that Includes Everyone (ROSIE) webserver to predict CCSIM from protein structure using projection approximation with PARCS. In this web interface, the user is only required to provide one or more PDB files as input. Results from our case studies suggest that CCSIM predictions (with ROSIE-PARCS) are highly accurate with an average error of 6.12%. Furthermore, the absolute difference between CCSIM and CCSPARCS can help in distinguishing accurate from inaccurate AlphaFold2 protein structure predictions. ROSIE-PARCS is designed with a user-friendly interface, is available publicly and is free to use. The ROSIE-PARCS web interface is supported by all major web browsers and can be accessed via this link (https://rosie.graylab.jhu.edu).

A Functional Bayesian Model for Hydrogen-Deuterium Exchange Mass Spectrometry

Proteins often undergo structural perturbations upon binding to other proteins or ligands or when they are subjected to environmental changes. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) can be used to explore conformational changes in proteins by examining differences in the rate of deuterium incorporation in different contexts. To determine deuterium incorporation rates, HDX-MS measurements are typically made over a time course. Recently introduced methods show that incorporating the temporal dimension into the statistical analysis improves power and interpretation. However, these approaches have technical assumptions that hinder their flexibility. Here, we propose a more flexible methodology by reframing these methods in a Bayesian framework. Our proposed framework has improved algorithmic stability, allows us to perform uncertainty quantification, and can calculate statistical quantities that are inaccessible to other approaches. We demonstrate the general applicability of the method by showing it can perform rigorous model selection on a spike-in HDX-MS experiment, improved interpretation in an epitope mapping experiment, and increased sensitivity in a small molecule case-study. Bayesian analysis of an HDX experiment with an antibody dimer bound to an E3 ubiquitin ligase identifies at least two interaction interfaces where previous methods obtained confounding results due to the complexities of conformational changes on binding. Our findings are consistent with the cocrystal structure of these proteins, demonstrating a bayesian approach can identify important binding epitopes from HDX data. We also generate HDX-MS data of the bromodomain-containing protein BRD4 in complex with GSK1210151A to demonstrate the increased sensitivity of adopting a Bayesian approach.

Fundamentals of HDX-MS

Hydrogen deuterium exchange mass spectrometry (HDX-MS) is becoming part of the standard repertoire of techniques used by molecular biologists to investigate protein structure and dynamics. This is partly due to the increased use of automation in all stages of the technique and its versatility of application-many proteins that present challenges with techniques such as X-ray crystallography and cryoelectron microscopy are amenable to investigation with HDX-MS. The present review is aimed at scientists who are curious about the technique, and how it may aid their research. It describes the fundamental basis of solvent exchange, the basics of a standard HDX-MS experiment, as well as highlighting emerging novel experimental advances, which point to where the field is heading.

Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction

Ion mobility (IM) mass spectrometry provides structural information about protein shape and size in the form of an orientationally-averaged collision cross-section (CCSIM). While IM data have been used with various computational methods, they have not yet been utilized to predict monomeric protein structure from sequence. Here, we show that IM data can significantly improve protein structure determination using the modelling suite Rosetta. We develop the Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm that allows for fast and accurate prediction of CCSIM from structure. Following successful testing of the PARCS algorithm, we use an integrative modelling approach to utilize IM data for protein structure prediction. Additionally, we propose a confidence metric that identifies near native models in the absence of a known structure. The results of this study demonstrate the ability of IM data to consistently improve protein structure prediction.

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.

Interlaboratory Studies Using the NISTmAb to Advance Biopharmaceutical Structural Analytics

Biopharmaceuticals such as monoclonal antibodies are required to be rigorously characterized using a wide range of analytical methods. Various material properties must be characterized and well controlled to assure that clinically relevant features and critical quality attributes are maintained. A thorough understanding of analytical method performance metrics, particularly emerging methods designed to address measurement gaps, is required to assure methods are appropriate for their intended use in assuring drug safety, stability, and functional activity. To this end, a series of interlaboratory studies have been conducted using NISTmAb, a biopharmaceutical-representative and publicly available monoclonal antibody test material, to report on state-of-the-art method performance, harmonize best practices, and inform on potential gaps in the analytical measurement infrastructure. Reported here is a summary of the study designs, results, and future perspectives revealed from these interlaboratory studies which focused on primary structure, post-translational modifications, and higher order structure measurements currently employed during biopharmaceutical development.

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

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

Challenges and Opportunities for Bayesian Statistics in Proteomics

Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of interest, many of these approaches only produce a point estimate, such as a mean, leaving little room for more nuanced interpretations. By contrast, Bayesian statistics allows quantification of uncertainty through the use of probability distributions. These probability distributions enable scientists to ask complex questions of their proteomics data. Bayesian statistics also offers a modular framework for data analysis by making dependencies between data and parameters explicit. Hence, specifying complex hierarchies of parameter dependencies is straightforward in the Bayesian framework. This allows us to use a statistical methodology which equals, rather than neglects, the sophistication of experimental design and instrumentation present in proteomics. Here, we review Bayesian methods applied to proteomics, demonstrating their potential power, alongside the challenges posed by adopting this new statistical framework. To illustrate our review, we give a walk-through of the development of a Bayesian model for dynamic organic orthogonal phase-separation (OOPS) data.

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.

Exploiting the Propagation of Constrained Variables for Enhanced HDX-MS Data Optimization

Nonlinear programming has found useful applications in protein biophysics to help understand the microscopic exchange kinetics of data obtained using hydrogen-deuterium exchange mass spectrometry (HDX-MS). Finding a microscopic kinetic solution for HDX-MS data provides a window into local protein stability and energetics allowing them to be quantified and understood. Optimization of HDX-MS data is a significant challenge, however, due to the requirement to solve a large number of variables simultaneously with exceptionally large variable bounds. Modeled rates are frequently uncertain with an explicate dependency on the initial guess values. In order to enhance the search for a minimum solution in HDX-MS optimization, the ability of selected constrained variables to propagate throughout the data is considered. We reveal that locally bound constrained optimization induces a global effect on all variables. The global response to local constraints is large and surprisingly long-range, but the outcome is unpredictable, unexpectedly decreasing the overall accuracy of certain data sets depending on the stringency of the constraints. Utilizing previously described in-house validation criteria based on covariance matrices, a method is described that is able to accurately determine whether constraints benefit or impair the optimization of HDX-MS data. From this, we establish a new two-stage method for our online optimizer HDXmodeller that can effectively leverage locally bound variables to enhance HDX-MS data modeling.

Leveraging crosslinking mass spectrometry in structural and cell biology

Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.

Quantitative Hydrogen/Deuterium Exchange Mass Spectrometry

This Account describes considerations for the data generation, data analysis, and data interpretation of a hydrogen/deuterium exchange-mass spectrometry (HDX-MS) experiment to have a quantitative argument. Although HDX-MS has gained its popularity as a biophysical tool, the argument from its data often remains qualitative. To generate HDX-MS data that are more suitable for a quantitative argument, the sequence coverage and sequence resolution should be optimized during the feasibility stage, and the time window coverage and time window resolution should be improved during the HDX stage. To extract biophysically meaningful values for a certain perturbation from medium-resolution HDX-MS data, there are two major ways: (i) estimating the area between the two deuterium buildup curves using centroid values with and without the perturbation when plotted against log time scale and (ii) dissecting into multiple single-exponential curves using the isotope envelopes. To have more accurate arguments for an HDX-MS perturbation study, (i) false negatives due to sequence coverage, (ii) false negatives due to time window coverage, (iii) false positives due to sequence resolution, and (iv) false positives due to allosteric effects should be carefully examined.

Probing Universal Protein Dynamics Using Hydrogen-Deuterium Exchange Mass Spectrometry-Derived Residue-Level Gibbs Free Energy

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is a powerful technique to monitor protein intrinsic dynamics. The technique provides high-resolution information on how protein intrinsic dynamics are altered in response to biological signals, such as ligand binding, oligomerization, or allosteric networks. However, identification, interpretation, and visualization of such events from HDX-MS data sets is challenging as these data sets consist of many individual data points collected across peptides, time points, and experimental conditions. Here, we present PyHDX, an open-source Python package and webserver, that allows the user to batch extract the universal quantity Gibbs free energy at residue levels over multiple protein conditions and homologues. The output is directly visualized on a linear map or 3D structures or is exported as .csv files or PyMOL scripts.

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.

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

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

Characterization and Management of Noise in HDX-MS Data Modeling

Quantification of hydrogen deuterium exchange (HDX) kinetics can provide information on the stability of individual amino acids in proteins by finding the degree to which the local backbone environment corresponds to that of a random coil. When characterized by mass spectrometry, extraction of HDX kinetics is not possible because different residue exchange rates become merged depending on the peptides that are formed during proteolytic digestion. We have recently developed an advanced programming tool called HDXmodeller, which enables the exchange rates of individual amino acids to be understood by optimization of low-resolution HDX-mass spectrometry (MS) data. HDXmodeller is also uniquely able to appraise each optimization and quantify the accuracy of modeled exchange rates ab initio using a novel autovalidation method based on a covariance matrix. Here, we address the noise-handling capabilities of HDXmodeller and demonstrate the effectiveness of the algorithm on self-inconsistent datasets. Reference intervals for experimental HDX-MS data are also derived, and this information is presented in an updated online workflow for HDXmodeller, allowing users to evaluate the consistency of their data. The development of a modified version of HDXmodeller is also discussed with enhanced noise-handling capability brought about through loss function optimization. Changes in optimizer accuracy with different loss functions are also demonstrated along with the effectiveness of HDXmodeller to select the most effective optimizer for different data using currently embedded autovalidation criteria.

The Angiopoietin-Like Protein 3 and 8 Complex Interacts with Lipoprotein Lipase and Induces LPL Cleavage

Lipoprotein lipase (LPL) is the key enzyme that hydrolyzes triglycerides from triglyceride-rich lipoproteins. Angiopoietin-like proteins (ANGPTL) 3, 4, and 8 are well-characterized protein inhibitors of LPL. ANGPTL8 forms a complex with ANGPTL3, and the complex is a potent endogenous inhibitor of LPL. However, the nature of the structural interaction between ANGPTL3/8 and LPL is unknown. To probe the conformational changes in LPL induced by ANGPTL3/8, we found that HDX-MS detected significantly altered deuteration in the lid region, ApoC2 binding site, and furin cleavage region of LPL in the presence of ANGPTL3/8. Supporting this HDX structural evidence, we found that ANGPTL3/8 inhibits LPL enzymatic activities and increases LPL cleavage. ANGPTL3/8-induced effects on LPL activity and LPL cleavage are much stronger than those of ANGPTL3 or ANGPTL8 alone. ANGPTL3/8-mediated LPL cleavage is blocked by both an ANGPTL3 antibody and a furin inhibitor. Knock-down of furin expression by siRNA significantly reduced ANGPT3/8-induced cleavage of LPL. Our data suggest ANGPTL3/8 promotes furin-mediated LPL cleavage.

Improving Spectral Validation Rates in Hydrogen-Deuterium Exchange Data Analysis

The data analysis practices associated with hydrogen-deuterium exchange mass spectrometry (HX-MS) lag far behind that of most other MS-based protein analysis tools. A reliance on external tools from other fields and a persistent need for manual data validation restrict this powerful technology to the expert user. Here, we provide an extensive upgrade to the HX data analysis suite available in the Mass Spec Studio in the form of two new apps (HX-PIPE and HX-DEAL), completing a workflow that provides an HX-tailored peptide identification capability, accelerated validation routines, automated spectral deconvolution strategies, and a rich set of exportable graphics and statistical reports. With these new tools, we demonstrate that the peptide identifications obtained from undeuterated samples generated at the start of a project contain information that helps predict and control the extent of manual validation required. We also uncover a large fraction of HX-usable peptides that remains unidentified in most experiments. We show that automated spectral deconvolution routines can identify exchange regimes in a project-wide manner, although they remain difficult to accurately assign in all scenarios. Taken together, these new tools provide a robust and complete solution suitable for the analysis of high-complexity HX-MS data.

HDXmodeller: an online webserver for high-resolution HDX-MS with auto-validation

The extent to which proteins are protected from hydrogen deuterium exchange (HDX) provides valuable insight into their folding, dynamics and interactions. Characterised by mass spectrometry (MS), HDX benefits from negligible mass restrictions and exceptional throughput and sensitivity but at the expense of resolution. Exchange mechanisms which naturally transpire for individual residues cannot be accurately located or understood because amino acids are characterised in differently sized groups depending on the extent of proteolytic digestion. Here we report HDXmodeller, the world's first online webserver for high-resolution HDX-MS. HDXmodeller accepts low-resolution HDX-MS input data and returns high-resolution exchange rates quantified for each residue. Crucially, HDXmodeller also returns a set of unique statistics that can correctly validate exchange rate models to an accuracy of 99%. Remarkably, these statistics are derived without any prior knowledge of the individual exchange rates and facilitate unparallel user confidence and the capacity to evaluate different data optimisation strategies.

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.

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

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

A novel phage display vector for selection of target-specific peptides

Intrinsic low display level of polypeptides on phage is a fundamental and limiting hurdle in successful isolation of target-specific binders by phage display technology. To circumvent this challenge, we optimized the copy number of peptides displayed on the phage surface using type 33 phage vector. We randomized the first 67 amino acids of the wild type PIII to identify mutants that would result in its reduced expression. Consequently, the display level was improved by 30-fold due to higher incorporation of the synthetic PIII-peptide fusion protein on the phage surface. Utilization of this novel phage vector should provide a solid basis for the discovery of therapeutic peptides.

Integration of phage and yeast display platforms: A reliable and cost effective approach for binning of peptides as displayed on-phage

Hundreds of target specific peptides are routinely discovered by peptide display platforms. However, due to the high cost of peptide synthesis only a limited number of peptides are chemically made for further analysis. Here we describe an accurate and cost effective method to bin peptides on-phage based on binding region(s), without any requirement for peptide or protein synthesis. This approach, which integrates phage and yeast display platforms, requires display of target and its alanine variants on yeast. Flow cytometry was used to detect binding of peptides on-phage to the target on yeast. Once hits were identified, they were synthesized to confirm their binding region(s) by HDX (Hydrogen deuterium exchange) and crystallography. Moreover, we have successfully shown that this approach can be implemented as part of a panning process to deplete non-functional peptides. This technique can be applied to any target that can be successfully displayed on yeast; it narrows down the number of peptides requiring synthesis; and its utilization during selection results in enrichment of peptide population against defined binding regions on the target.

Mass Spectrometry-Based Protein Footprinting for Higher-Order Structure Analysis: Fundamentals and Applications

Proteins adopt different higher-order structures (HOS) to enable their unique biological functions. Understanding the complexities of protein higher-order structures and dynamics requires integrated approaches, where mass spectrometry (MS) is now positioned to play a key role. One of those approaches is protein footprinting. Although the initial demonstration of footprinting was for the HOS determination of protein/nucleic acid binding, the concept was later adapted to MS-based protein HOS analysis, through which different covalent labeling approaches "mark" the solvent accessible surface area (SASA) of proteins to reflect protein HOS. Hydrogen-deuterium exchange (HDX), where deuterium in D2O replaces hydrogen of the backbone amides, is the most common example of footprinting. Its advantage is that the footprint reflects SASA and hydrogen bonding, whereas one drawback is the labeling is reversible. Another example of footprinting is slow irreversible labeling of functional groups on amino acid side chains by targeted reagents with high specificity, probing structural changes at selected sites. A third footprinting approach is by reactions with fast, irreversible labeling species that are highly reactive and footprint broadly several amino acid residue side chains on the time scale of submilliseconds. All of these covalent labeling approaches combine to constitute a problem-solving toolbox that enables mass spectrometry as a valuable tool for HOS elucidation. As there has been a growing need for MS-based protein footprinting in both academia and industry owing to its high throughput capability, prompt availability, and high spatial resolution, we present a summary of the history, descriptions, principles, mechanisms, and applications of these covalent labeling approaches. Moreover, their applications are highlighted according to the biological questions they can answer. This review is intended as a tutorial for MS-based protein HOS elucidation and as a reference for investigators seeking a MS-based tool to address structural questions in protein science.

Avoiding H/D Scrambling with Minimal Ion Transmission Loss for HDX-MS/MS-ETD Analysis on a High-Resolution Q-TOF Mass Spectrometer

Hydrogen/deuterium exchange monitored by mass spectrometry (HDX-MS) enables the study of protein dynamics by measuring the time-resolved deuterium incorporation into a protein incubated in D₂O. Using electron-based fragmentation in the gas phase it is possible to measure deuterium uptake at single-residue resolution. However, a prerequisite for this approach is that the solution-phase labeling is conserved in the gas phase prior to precursor fragmentation. It is therefore essential to reduce or even avoid intramolecular hydrogen/deuterium migration, which causes randomization of the deuterium labels along the peptide (hydrogen scrambling). Here, we describe an optimization strategy for reducing scrambling to a negligible level while minimizing the impact on sensitivity on a high-resolution Q-TOF equipped with ETD and an electrospray ionization interface consisting of a glass transfer capillary followed by a dual ion funnel. In our strategy we narrowed down the optimization to two accelerating potentials, and we defined the optimization of these in a simple rule by accounting for their interdependency in relation to scrambling and transmission efficiency. Using this rule, we were able to reduce scrambling from 75% to below 5% on average using the highly scrambling-sensitive quadruply charged P1 peptide scrambling probe resulting in a minor 33% transmission loss. To demonstrate the applicability of this approach, we probe the dynamics of certain regions in cytochrome c.

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.

SSEThread: Integrative threading of the DNA-PKcs sequence based on data from chemical cross-linking and hydrogen deuterium exchange

X-ray crystallography and electron microscopy maps resolved to 3-8 Å are generally sufficient for tracing the path of the polypeptide chain in space, while often insufficient for unambiguously registering the sequence on the path (i.e., threading). Frequently, however, additional information is available from other biophysical experiments, physical principles, statistical analyses, and other prior models. Here, we formulate an integrative approach for sequence assignment to a partial backbone model as an optimization problem, which requires three main components: the representation of the system, the scoring function, and the optimization method. The method is implemented in the open source Integrative Modeling Platform (IMP) (https://integrativemodeling.org), allowing a number of different terms in the scoring function. We apply this method to localizing the sequence assignment within a 199-residue disordered region of three structured and sequence unassigned helices in the DNA-PKcs crystallographic structure, using chemical crosslinks, hydrogen deuterium exchange, and sequence connectivity. The resulting ensemble of threading models provides two major solutions, one of which suggests that the crucial ABCDE cluster of phosphorylation sites cannot undergo intra-molecular autophosphorylation without a conformational rearrangement. The ensemble of solutions embodies the most accurate and precise sequence threading given the available information.

Resolution of protein hydrogen/deuterium exchange by fitting amide exchange probabilities to the peptide isotopic envelopes

RATIONALE

Mass spectra processing in protein hydrogen/deuterium (H/D) exchange has been remarkably improved by the introduction of fitting of the amide exchange probabilities to peptide isotopic envelope intensities (Kan et al., 2013), in contrast to methods in which only the peptide deuterium uptakes (centroid shifts of isotopic envelopes) are used. However, the known implementations are based on the general fitting routines that use only the objective function values. Besides, applicability of more than one fitting method makes necessary their comparative evaluation.

METHODS

Two fitting methods were considered: the common least squares and the fitting of the multinomial distribution representing the number of deuterium atoms exchanged in the individual peptides. Both methods were applied either directly to the isotopic envelope data or to the deuterium distributions obtained by envelope deconvolution (i.e. de-isotoping).

RESULTS

An autonomous Matlab script was prepared, based on the exact expressions for the gradient and Hessian of the objective function, with the trust-region algorithm implemented in the compact analytical form recently made available. The least-squares fitting to the envelope data produced the best results, with the greatest precision and good coverage of exact values by the confidence intervals. The deuterium distributions were sensitive to the (simulated) experimental error whose progression by envelope deconvolution caused degradation in accuracy. The multinomial distribution fitting exhibited poor performance due to inadequate representation of the experimental error and missing of the appropriate weight parameters. Some specific peptide arrangement details were discussed as potential sources of ambiguity in the fitting results.

CONCLUSIONS

The method of fitting to peptide isotopic envelopes has been improved by using the exact gradient and Hessian of the objective function. The fitting should be repeated with different initial guesses in order to find not only the global minimum, but also the local minima with similar depths which may exist due to eventual ambiguity of the fitting results.

Archiving and disseminating integrative structure models

Limitations in the applicability, accuracy, and precision of individual structure characterization methods can sometimes be overcome via an integrative modeling approach that relies on information from all available sources, including all available experimental data and prior models. The open-source Integrative Modeling Platform (IMP) is one piece of software that implements all computational aspects of integrative modeling. To maximize the impact of integrative structures, the coordinates should be made publicly available, as is already the case for structures based on X-ray crystallography, NMR spectroscopy, and electron microscopy. Moreover, the associated experimental data and modeling protocols should also be archived, such that the original results can easily be reproduced. Finally, it is essential that the integrative structures are validated as part of their publication and deposition. A number of research groups have already developed software to implement integrative modeling and have generated a number of structures, prompting the formation of an Integrative/Hybrid Methods Task Force. Following the recommendations of this task force, the existing PDBx/mmCIF data representation used for atomic PDB structures has been extended to address the requirements for archiving integrative structural models. This IHM-dictionary adds a flexible model representation, including coarse graining, models in multiple states and/or related by time or other order, and multiple input experimental information sources. A prototype archiving system called PDB-Dev ( https://pdb-dev.wwpdb.org ) has also been created to archive integrative structural models, together with a Python library to facilitate handling of integrative models in PDBx/mmCIF format.

Estimating Constraints for Protection Factors from HDX-MS Data

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

Tracking Higher Order Protein Structure by Hydrogen-Deuterium Exchange Mass Spectrometry

BACKGROUND

Structural biology has provided a fundamental understanding of protein structure and mechanistic insight into their function. However, high-resolution structures alone are insufficient for a complete understanding of protein behavior. Higher energy conformations, conformational changes, and subtle structural fluctuations that underlie the proper function of proteins are often difficult to probe using traditional structural approaches. Hydrogen/Deuterium Exchange with Mass Spectrometry (HDX-MS) provides a way to probe the accessibility of backbone amide protons under native conditions, which reports on local structural dynamics of solution protein structure that can be used to track complex structural rearrangements that occur in the course of a protein's function.

CONCLUSION

In the last 20 years the advances in labeling techniques, sample preparation, instrumentation, and data analysis have enabled HDX to gain insights into very complex biological systems. Analysis of challenging targets such as membrane protein complexes is now feasible and the field is paving the way to the analysis of more and more complex systems.

Deciphering Binding Interactions of IL-23R with HDX-MS: Mapping Protein and Macrocyclic Dodecapeptide Ligands

Molecular characterization of the binding epitope of IL-23R and its cognate cytokine IL-23 is paramount to understand the role in autoimmune diseases and to support the discovery of new inhibitors of this protein-protein interaction. Our results revealed that HDX-MS was able to identify the binding epitope of IL-23R:IL-23, which opened the way to evaluate a peptide macrocycle described in the literature as disrupter of this autoimmune target. Thus, the characterization of the interactions of this chemotype by HDX-MS in combination with computational approaches was achieved. To our knowledge, this is the first reported structural evidence regarding the site where a small compound binds to IL-23R.

MS methods to study macromolecule-ligand interaction: Applications in drug discovery

The interaction of small compounds (i.e. ligands) with macromolecules or macromolecule assemblies (i.e. targets) is the mechanism of action of most of the drugs available today. Mass spectrometry is a popular technique for the interrogation of macromolecule-ligand interactions and therefore is also widely used in drug discovery and development. Thanks to its versatility, mass spectrometry is used for multiple purposes such as biomarker screening, identification of the mechanism of action, ligand structure optimization or toxicity assessment. The evolution and automation of the instruments now allows the development of high throughput methods with high sensitivity and a minimized false discovery rate. Herein, all these approaches are described with a focus on the methods for studying macromolecule-ligand interaction aimed at defining the structure-activity relationships of drug candidates, along with their mechanism of action, metabolism and toxicity.

Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data

In recent years mass spectrometry-based covalent labeling techniques such as hydroxyl radical footprinting (HRF) have emerged as valuable structural biology techniques, yielding information on protein tertiary structure. These data, however, are not sufficient to predict protein structure unambiguously, as they provide information only on the relative solvent exposure of certain residues. Despite some recent advances, no software currently exists that can utilize covalent labeling mass spectrometry data to predict protein tertiary structure. We have developed the first such tool, which incorporates mass spectrometry derived protection factors from HRF labeling as a new centroid score term for the Rosetta scoring function to improve the prediction of protein tertiary structures. We tested our method on a set of four soluble benchmark proteins with known crystal structures and either published HRF experimental results or internally acquired data. Using the HRF labeling data, we rescored large decoy sets of structures predicted with Rosetta for each of the four benchmark proteins. As a result, the model quality improved for all benchmark proteins as compared to when scored with Rosetta alone. For two of the four proteins we were even able to identify atomic resolution models with the addition of HRF data.

Utilization of peptide phage display to investigate hotspots on IL-17A and what it means for drug discovery

To date, IL-17A antibodies remain the only therapeutic approach to correct the abnormal activation of the IL-17A/IL-17R signaling complex. Why is it that despite the remarkable success of IL-17 antibodies, there is no small molecule antagonist of IL-17A in the clinic? Here we offer a unique approach to address this question. In order to understand the interaction of IL-17A with its receptor, we combined peptide discovery using phage display with HDX, crystallography, and functional assays to map and characterize hot regions that contribute to most of the energetics of the IL-17A/IL-17R interaction. These functional maps are proposed to serve as a guide to aid in the development of small molecules that bind to IL-17A and block its interaction with IL-17RA.

Assessing Exhaustiveness of Stochastic Sampling for Integrative Modeling of Macromolecular Structures

Modeling of macromolecular structures involves structural sampling guided by a scoring function, resulting in an ensemble of good-scoring models. By necessity, the sampling is often stochastic, and must be exhaustive at a precision sufficient for accurate modeling and assessment of model uncertainty. Therefore, the very first step in analyzing the ensemble is an estimation of the highest precision at which the sampling is exhaustive. Here, we present an objective and automated method for this task. As a proxy for sampling exhaustiveness, we evaluate whether two independently and stochastically generated sets of models are sufficiently similar. The protocol includes testing 1) convergence of the model score, 2) whether model scores for the two samples were drawn from the same parent distribution, 3) whether each structural cluster includes models from each sample proportionally to its size, and 4) whether there is sufficient structural similarity between the two model samples in each cluster. The evaluation also provides the sampling precision, defined as the smallest clustering threshold that satisfies the third, most stringent test. We validate the protocol with the aid of enumerated good-scoring models for five illustrative cases of binary protein complexes. Passing the proposed four tests is necessary, but not sufficient for thorough sampling. The protocol is general in nature and can be applied to the stochastic sampling of any set of models, not just structural models. In addition, the tests can be used to stop stochastic sampling as soon as exhaustiveness at desired precision is reached, thereby improving sampling efficiency; they may also help in selecting a model representation that is sufficiently detailed to be informative, yet also sufficiently coarse for sampling to be exhaustive.

Integrative structure modeling with the Integrative Modeling Platform

Building models of a biological system that are consistent with the myriad data available is one of the key challenges in biology. Modeling the structure and dynamics of macromolecular assemblies, for example, can give insights into how biological systems work, evolved, might be controlled, and even designed. Integrative structure modeling casts the building of structural models as a computational optimization problem, for which information about the assembly is encoded into a scoring function that evaluates candidate models. Here, we describe our open source software suite for integrative structure modeling, Integrative Modeling Platform (https://integrativemodeling.org), and demonstrate its use.

Computational method allowing Hydrogen-Deuterium Exchange Mass Spectrometry at single amide Resolution

Hydrogen-deuterium exchange (HDX) coupled with mass spectrometry (HDXMS) is a rapid and effective method for localizing and determining protein stability and dynamics. Localization is routinely limited to a peptide resolution of 5 to 20 amino acid residues. HDXMS data can contain information beyond that needed for defining protein stability at single amide resolution. Here we present a method for extracting this information from an HDX dataset to generate a HDXMS protein stability fingerprint. High resolution (HR)-HDXMS was applied to the analysis of a model protein of a spectrin tandem repeat that exemplified an intuitive stability profile based on the linkage of two triple helical repeats connected by a helical linker. The fingerprint recapitulated expected stability maximums and minimums with interesting structural features that corroborate proposed mechanisms of spectrin flexibility and elasticity. HR-HDXMS provides the unprecedented ability to accurately assess protein stability at the resolution of a single amino acid. The determination of HDX stability fingerprints may be broadly applicable in many applications for understanding protein structure and function as well as protein ligand interactions.