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).

Discovery of Nanomolar Inhibitors for Human Dihydroorotate Dehydrogenase Using Structure-Based Drug Discovery Methods.

We used a structure-based drug discovery approach to identify novel inhibitors of human dihydroorotate dehydrogenase (DHODH), which is a therapeutic target for treating cancer and autoimmune and inflammatory diseases. In the case of acute myeloid leukemia, no previously discovered DHODH inhibitors have yet succeeded in this clinical application. Thus, there remains a strong need for new inhibitors that could be used as alternatives to the current standard-of-care. Our goal was to identify novel inhibitors of DHODH. We implemented prefiltering steps to omit PAINS and Lipinski violators at the earliest stages of this project. This enriched compounds in the data set that had a higher potential of favorable oral druggability. Guided by Glide SP docking scores, we found 20 structurally unique compounds from the ChemBridge EXPRESS-pick library that inhibited DHODH with IC50, DHODH values between 91 nM and 2.7 µM. Ten of these compounds reduced MOLM-13 cell viability with IC50, MOLM-13 values between 2.3 and 50.6 µM. Compound 16 (IC50, DHODH = 91 nM) inhibited DHODH more potently than the known DHODH inhibitor, teriflunomide (IC50, DHODH = 130 nM), during biochemical characterizations and presented a promising scaffold for future hit-to-lead optimization efforts. Compound 17 (IC50, MOLM-13 = 2.3 µM) was most successful at reducing survival in MOLM-13 cell lines compared with our other hits. The discovered compounds represent excellent starting points for the development and optimization of novel DHODH inhibitors.

Protein Structure Prediction with Mass Spectrometry Data.

Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen-deuterium exchange, hydroxyl radical protein footprinting, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data.

Umbrella Sampling Simulations of Cardiac Thin Filament Reveal Thermodynamic Consequences of Troponin I Inhibitory Peptide Mutations.

The cardiac thin filament comprises F-actin, tropomyosin, and troponin (cTn). cTn is composed of three subunits: troponin C (cTnC), troponin I (cTnI), and troponin T (cTnT). To computationally study the effect of the thin filament on cTn activation events, we employed targeted molecular dynamics followed by umbrella sampling using a model of the thin filament to measure the thermodynamics of cTn transition events. Our simulations revealed that the thin filament causes an increase in the free energy required to open the cTnC hydrophobic patch and causes a more favorable interaction between this region and the cTnI switch peptide. Mutations to the cTn complex can lead to cardiomyopathy, a collection of diseases that present clinically with symptoms of hypertrophy or dilation of the cardiac muscle, leading to impairment of the heart's ability to function normally and ultimately myocardial infarction or heart failure. Upon introduction of cardiomyopathic mutations to R145 of cTnI, we observed a general decrease in the free energy of opening the cTnC hydrophobic patch, which is on par with previous experimental results. These mutations also exhibited a decrease in electrostatic interactions between cTnI-R145 and actin-E334. After introduction of a small molecule to the wild-type cTnI-actin interface to intentionally disrupt intersubunit contacts, we successfully observed similar thermodynamic consequences and disruptions to the same protein-protein contacts as observed with the cardiomyopathic mutations. Computational studies utilizing the cTn complex in isolation would have been unable to observe these effects, highlighting the importance of using a more physiologically relevant thin-filament model to investigate the global consequences of cardiomyopathic mutations to the cTn complex.

Targeting Troponin C with Small Molecules Containing Diphenyl Moieties: Calcium Sensitivity Effects on Striated Muscles and Structure-Activity Relationship.

Despite large investments from academia and industry, heart failure, which results from a disruption of the contractile apparatus, remains a leading cause of death. Cardiac muscle contraction is a calcium-dependent mechanism, which is regulated by the troponin protein complex (cTn) and specifically by the N-terminal domain of its calcium-binding subunit (cNTnC). There is an increasing need for the development of small molecules that increase calcium sensitivity without altering the systolic calcium concentration, thereby strengthening the cardiac function. Here, we examined the effect of our previously identified calcium-sensitizing small molecule, ChemBridge compound 7930079, in the context of several homologous muscle systems. The effect of this molecule on force generation in isolated cardiac trabeculae and slow skeletal muscle fibers was measured. Furthermore, we explored the use of Gaussian accelerated molecular dynamics in sampling highly predictive receptor conformations based on NMR-derived starting structures. Additionally, we took a rational computational approach for lead optimization based on lipophilic diphenyl moieties. This integrated structural-biochemical-physiological approach led to the identification of three novel low-affinity binders, which had similar binding affinities to the known positive inotrope trifluoperazine. The most potent identified calcium sensitizer was compound 16 with an apparent affinity of 117 ± 17 µM.

Binding of calcium and magnesium to human cardiac troponin C.

Cardiac muscle thin filaments are composed of actin, tropomyosin, and troponin that change conformation in response to Ca2+ binding, triggering muscle contraction. Human cardiac troponin C (cTnC) is the Ca2+-sensing component of the thin filament. It contains structural sites (III/IV) that bind both Ca2+ and Mg2+ and a regulatory site (II) that has been thought to bind only Ca2+. Binding of Ca2+ at this site initiates a series of conformational changes that culminate in force production. However, the mechanisms that underpin the regulation of binding at site II remain unclear. Here, we have quantified the interaction between site II and Ca2+/Mg2+ through isothermal titration calorimetry and thermodynamic integration simulations. Direct and competitive binding titrations with WT N-terminal cTnC and full-length cTnC indicate that physiologically relevant concentrations of both Ca2+/Mg2+ interacted with the same locus. Moreover, the D67A/D73A N-terminal cTnC construct in which two coordinating residues within site II were removed was found to have significantly reduced affinity for both cations. In addition, 1 mM Mg2+ caused a 1.4-fold lower affinity for Ca2+. These experiments strongly suggest that cytosolic-free Mg2+ occupies a significant population of the available site II. Interaction of Mg2+ with site II of cTnC likely has important functional consequences for the heart both at baseline as well as in diseased states that decrease or increase the availability of Mg2+, such as secondary hyperparathyroidism or ischemia, respectively.

Simulation of Energy-Resolved Mass Spectrometry Distributions from Surface-Induced Dissociation.

Understanding the relationship between protein structure and experimental data is crucial for utilizing experiments to solve biochemical problems and optimizing the use of sparse experimental data for structural interpretation. Tandem mass spectrometry (MS/MS) can be used with a variety of methods to collect structural data for proteins. One example is surface-induced dissociation (SID), which is used to break apart protein complexes (via a surface collision) into intact subcomplexes and can be performed at multiple laboratory frame SID collision energies. These energy-resolved MS/MS experiments have shown that the profile of the breakages depends on the acceleration energy of the collision. It is possible to extract an appearance energy (AE) from energy-resolved mass spectrometry (ERMS) data, which shows the relative intensity of each type of subcomplex as a function of SID acceleration energy. We previously determined that these AE values for specific interfaces correlated with structural features related to interface strength. In this study, we further examined the structural relationships by developing a method to predict the full ERMS plot from the structure, rather than extracting a single value. First, we noted that for proteins with multiple interface types, we could reproduce the correct shapes of breakdown curves, further confirming previous structural hypotheses. Next, we demonstrated that interface size and energy density (measured using Rosetta) correlated with data derived from the ERMS plot (R2 = 0.71). Furthermore, based on this trend, we used native crystal structures to predict ERMS. The majority of predictions resulted in good agreement, and the average root-mean-square error was 0.20 for the 20 complexes in our data set. We also show that if additional information on cleavage as a function of collision energy could be obtained, the accuracy of predictions improved further. Finally, we demonstrated that ERMS prediction results were better for the native than for inaccurate models in 17/20 cases. An application to run this simulation has been developed in Rosetta, which is freely available for use.

Actives-Based Receptor Selection Strongly Increases the Success Rate in Structure-Based Drug Design and Leads to Identification of 22 Potent Cancer Inhibitors.

Computer-aided drug design, an important component of the early stages of the drug discovery pipeline, routinely identifies large numbers of false positive hits that are subsequently confirmed to be experimentally inactive compounds. We have developed a methodology to improve true positive prediction rates in structure-based drug design and have successfully applied the protocol to twenty target systems and identified the top three performing conformers for each of the targets. Receptor performance was evaluated based on the area under the curve of the receiver operating characteristic curve for two independent sets of known actives. For a subset of five diverse cancer-related disease targets, we validated our approach through experimental testing of the top 50 compounds from a blind screening of a small molecule library containing hundreds of thousands of compounds. Our methods of receptor and compound selection resulted in the identification of 22 novel inhibitors in the low µM-nM range, with the most potent being an EGFR inhibitor with an IC50 value of 7.96 nM. Additionally, for a subset of five independent target systems, we demonstrated the utility of Gaussian accelerated molecular dynamics to thoroughly explore a target system's potential energy surface and generate highly predictive receptor conformations.

Umbrella Sampling Simulations Measure Switch Peptide Binding and Hydrophobic Patch Opening Free Energies in Cardiac Troponin.

The cardiac troponin (cTn) complex is an important regulatory protein in heart contraction. Upon binding of Ca2+, cTn undergoes a conformational shift that allows the troponin I switch peptide (cTnISP) to be released from the actin filament and bind to the troponin C hydrophobic patch (cTnCHP). Mutations and modifications to this complex can change its sensitivity to Ca2+ and alter the energetics of the transition from the Ca2+-unbound, cTnISP-unbound form to the Ca2+-bound, cTnISP-bound form. We utilized targeted molecular dynamics (TMD) to obtain a trajectory of this transition pathway, followed by umbrella sampling to estimate the free energy associated with the cTnISP-cTnCHP binding and the cTnCHP opening events for wild-type (WT) cTn. We were able to reproduce experimental values for the cTnISP-cTnCHP binding event and obtain cTnCHP opening free energies in agreement with previous computational measurements of smaller cTnC systems. This excellent agreement for WT cTn demonstrated the strength of computational methods in studying the dynamics and energetics of the cTn complex. We then introduced mutations to the cTn complex that cause cardiomyopathy or alter its Ca2+ sensitivity and observed a general decrease in the free energy of opening the cTnCHP. For these same mutations, we observed no general trend in the effect on the cTnISP-cTnCHP binding event. Our method sets the stage for future computational studies on this system that predict the consequences of yet uncharacterized mutations on cTn dynamics and energetics.

Computational Exploration and Characterization of Potential Calcium Sensitizing Mutations in Cardiac Troponin C.

Calcium-dependent heart muscle contraction is regulated by the cardiac troponin protein complex (cTn) and specifically by the N-terminal domain of its calcium binding subunit (cNTnC). cNTnC contains one calcium binding site (site II), and altered calcium binding in this site has been studied for decades. It has been previously shown that cNTnC mutants, which increase calcium sensitization may have therapeutic benefits, such as restoring cardiac muscle contractility and functionality post-myocardial infarction events. Here, we computationally characterized eight mutations for their potential effects on calcium binding affinity in site II of cNTnC. We utilized two distinct methods to estimate calcium binding: adaptive steered molecular dynamics (ASMD) and thermodynamic integration (TI). We observed a sensitizing trend for all mutations based on the employed ASMD methodology. The TI results showed excellent agreement with experimentally known calcium binding affinities in wild-type cNTnC. Based on the TI results, five mutants were predicted to increase calcium sensitivity in site II. This study presents an interesting comparison of the two computational methods, which have both been shown to be valuable tools in characterizing the impacts of calcium sensitivity in mutant cNTnC systems.

Relative interfacial cleavage energetics of protein complexes revealed by surface collisions.

To fulfill their biological functions, proteins must interact with their specific binding partners and often function as large assemblies composed of multiple proteins or proteins plus other biomolecules. Structural characterization of these complexes, including identification of all binding partners, their relative binding affinities, and complex topology, is integral for understanding function. Understanding how proteins assemble and how subunits in a complex interact is a cornerstone of structural biology. Here we report a native mass spectrometry (MS)-based method to characterize subunit interactions in globular protein complexes. We demonstrate that dissociation of protein complexes by surface collisions, at the lower end of the typical surface-induced dissociation (SID) collision energy range, consistently cleaves the weakest protein:protein interfaces, producing products that are reflective of the known structure. We present here combined results for multiple complexes as a training set, two validation cases, and four computational models. We show that SID appearance energies can be predicted from structures via a computationally derived expression containing three terms (number of residues in a given interface, unsatisfied hydrogen bonds, and a rigidity factor).

Utilization of Hydrophobic Microenvironment Sensitivity in Diethylpyrocarbonate Labeling for Protein Structure Prediction.

Diethylpyrocarbonate (DEPC) labeling analyzed with mass spectrometry can provide important insights into higher order protein structures. It has been previously shown that neighboring hydrophobic residues promote a local increase in DEPC concentration such that serine, threonine, and tyrosine residues are more likely to be labeled despite low solvent exposure. In this work, we developed a Rosetta algorithm that used the knowledge of labeled and unlabeled serine, threonine, and tyrosine residues and assessed their local hydrophobic environment to improve protein structure prediction. Additionally, DEPC-labeled histidine and lysine residues with higher relative solvent accessible surface area values (i.e., more exposed) were scored favorably. Application of our score term led to reductions of the root-mean-square deviations (RMSDs) of the lowest scoring models. Additionally, models that scored well tended to have lower RMSDs. A detailed tutorial describing our protocol and required command lines is included. Our work demonstrated the considerable potential of DEPC covalent labeling data to be used for accurate higher order structure determination.

Prediction of Protein Complex Structure Using Surface-Induced Dissociation and Cryo-Electron Microscopy.

A variety of techniques involving the use of mass spectrometry (MS) have been developed to obtain structural information on proteins and protein complexes. One example of these techniques, surface-induced dissociation (SID), has been used to study the oligomeric state and connectivity of protein complexes. Recently, we demonstrated that appearance energies (AE) could be extracted from SID experiments and that they correlate with structural features of specific protein-protein interfaces. While SID AE provides some structural information, the AE data alone are not sufficient to determine the structures of the complexes. For this reason, we sought to supplement the data with computational modeling, through protein-protein docking. In a previous study, we demonstrated that the scoring of structures generated from protein-protein docking could be improved with the inclusion of SID data; however, this work relied on knowledge of the correct tertiary structure and only built full complexes for a few cases. Here, we performed docking using input structures that require less prior knowledge, using homology models, unbound crystal structures, and bound+perturbed crystal structures. Using flexible ensemble docking (to build primarily subcomplexes from an ensemble of backbone structures), the RMSD100 of all (15/15) predicted structures using the combined Rosetta, cryo-electron microscopy (cryo-EM), and SID score was less than 4 Å, compared to only 7/15 without SID and cryo-EM. Symmetric docking (which used symmetry to build full complexes) resulted in predicted structures with RMSD100 less than 4 Å for 14/15 cases with experimental data, compared to only 5/15 without SID and cryo-EM. Finally, we also developed a confidence metric for which all (26/26) proteins flagged as high confidence were accurately predicted.

Adaptative Steered Molecular Dynamics Study of Mutagenesis Effects on Calcium Affinity in the Regulatory Domain of Cardiac Troponin C.

Calcium-dependent cardiac muscle contraction is regulated by the protein complex troponin (cTn) and specifically by the regulatory N-terminal domain (N-cTnC) which contains one active Ca2+ binding site (site II). It has been previously shown that cardiac muscle contractility and functionality is affected by mutations in N-cTnC which alter calcium binding affinity. Here, we describe the application of adaptive steered molecular dynamics to characterize the influence of N-cTnC mutations on site II calcium binding affinity. We observed the correct trends for all of the studied calcium sensitizing and desensitizing mutants, in conjunction with loop II perturbations. Additionally, the potential of mean force accuracy was shown to increase substantially with increasingly slower speeds and using fewer trajectories. This study presents a novel approach to computationally estimate the Ca2+ binding affinity of N-cTnC structures and is a valuable potential tool to support the design and characterization of novel mutations with potential therapeutic benefits.

Using NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD Protein Structure Refinement.

Cryo-EM has become one of the prime methods for protein structure elucidation, frequently yielding density maps with near-atomic or medium resolution. If protein structures cannot be deduced unambiguously from the density maps, computational structure refinement tools are needed to generate protein structural models. We have previously developed an iterative Rosetta-MDFF protocol that used cryo-EM densities to refine protein structures. Here we show that, in addition to cryo-EM densities, incorporation of other experimental restraints into the Rosetta-MDFF protocol further improved refined structures. We used NMR chemical shift (CS) data integrated with cryo-EM densities in our hybrid protocol in both the Rosetta step and the molecular dynamics (MD) simulations step. In 15 out of 18 cases for all MD rounds, the refinement results obtained when density maps and NMR chemical shift data were used in combination outperformed those of density map-only refinement. Notably, the improvement in refinement was highest when medium and low-resolution density maps were used. With our hybrid method, the RMSDs of final models obtained were always better than the RMSDs obtained by our previous protocol with just density refinement for both medium (6.9 Å) and low (9 Å) resolution maps. For all the six test proteins with medium resolution density maps (6.9 Å), the final refined structure RMSDs were lower for the hybrid method than for the cryo-EM only refinement. The final refined RMSDs were less than 1.5 Å when our hybrid protocol was used with 4 Å density maps. For four out of the six proteins the final RMSDs were even less than 1 Å. This study demonstrates that by using a combination of cryo-EM and NMR restraints, it is possible to refine structures to atomic resolution, outperforming single restraint refinement. This hybrid protocol will be a valuable tool when only low-resolution cryo-EM density data and NMR chemical shift data are available to refine structures.

Discovery of Novel Small-Molecule Calcium Sensitizers for Cardiac Troponin C: A Combined Virtual and Experimental Screening Approach.

Heart failure is a leading cause of death throughout the world and is triggered by a disruption of the cardiac contractile machinery. This machinery is regulated in a calcium-dependent manner by the protein complex troponin. Calcium binds to the N-terminal domain of cardiac troponin C (cNTnC) setting into motion the cascade of events leading to muscle contraction. Because of the severity and prevalence of heart failure, there is a strong need to develop small-molecule therapeutics designed to increase the calcium sensitivity of cardiac troponin in order to treat this devastating condition. Molecules that are able to stabilize an open configuration of cNTnC and additionally facilitate the binding of the cardiac troponin I (cTnI) switch peptide have the potential to enable increased calcium sensitization and strengthened cardiac function. Here, we employed a high throughput virtual screening methodology built upon the ability of computational docking to reproduce known experimental results and to accurately recognize cNTnC conformations conducive to small molecule binding using a receiver operator characteristic curve analysis. This approach combined with concurrent stopped-flow kinetic experimental verification led to the identification of a number of sensitizers, which slowed the calcium off-rate. An initial hit, compound 4, was identified with medium affinity (84 ± 30 µM). Through refinement, a calcium sensitizing agent, compound 5, with an apparent affinity of 1.45 ± 0.09 µM was discovered. This molecule is one of the highest affinity calcium sensitizers known to date.

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.

Mechanism of Cardiac Troponin C Calcium Sensitivity Modulation by Small Molecules Illuminated by Umbrella Sampling Simulations.

Cardiac troponin C (cTnC) binds intracellular calcium and subsequently cardiac troponin I (cTnI), initiating cardiac muscle contraction. Due to its role in contraction, cTnC has been a therapeutic target in the search for small molecules to treat conditions that interfere with normal muscle contraction like the heritable cardiomyopathies. Structural studies have shown the binding location of small molecules such as bepridil, dfbp-o, 3-methyldiphenylamine (DPA), and W7 to be a hydrophobic pocket in the regulatory domain of cTnC (cNTnC) but have not shown the influence of these small molecules on the energetics of opening this domain. Here we describe an application of an umbrella sampling method used to elucidate the impact these calcium sensitivity modulators have on the free energy of cNTnC hydrophobic patch opening. We found that all these molecules lowered the free energy of opening in the absence of the cTnI, with bepridil facilitating the least endergonic transformation. In the presence of cTnI, however, we saw a stabilization of the open configuration due to DPA and dfbp-o binding, and a destabilization of the open configuration imparted by bepridil and W7. Predicted poor binding molecule NSC34337 left the hydrophobic patch in under 3 ns in conventional MD simulations suggesting that only hydrophobic patch binders stabilized the open conformation. In conclusion, this study presents a novel approach to study the impact of small molecules on hydrophobic patch opening through umbrella sampling, and it proposes mechanisms for calcium sensitivity modulation.

Cell-Permeable Bicyclic Peptidyl Inhibitors against NEMO-IκB Kinase Interaction Directly from a Combinatorial Library.

Macrocyclic peptides are capable of binding to flat protein surfaces such as the interfaces of protein-protein interactions with antibody-like affinity and specificity, but generally lack cell permeability in order to access intracellular targets. In this work, we designed and synthesized a large combinatorial library of cell-permeable bicyclic peptides, in which the first ring consisted of randomized peptide sequences for potential binding to a target of interest, while the second ring featured a family of different cell-penetrating motifs, for both cell penetration and target binding. The library was screened against the IκB kinase α/ß (IKKα/ß)-binding domain of NF-κB essential modulator (NEMO), resulting in the discovery of several cell-permeable bicyclic peptides, which inhibited the NEMO-IKKß interaction with low µM IC50 values. Further optimization of one of the hits led to a relatively potent and cell-permeable NEMO inhibitor (IC50 = 1.0 µM), which selectively inhibited canonical NF-κB signaling in mammalian cells and the proliferation of cisplatin-resistant ovarian cancer cells. The inhibitor provides a useful tool for investigating the biological functions of NEMO/NF-κB and a potential lead for further development of a novel class of anti-inflammatory and anticancer drugs.

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.