Construct optimization for protein NMR structure analysis using amide hydrogen/deuterium exchange mass spectrometry

Disordered or unstructured regions of proteins, while often very important biologically, can pose significant challenges for resonance assignment and three‐dimensional structure determination of the ordered regions of proteins by NMR methods. In this article, we demonstrate the application of ¹H/²H exchange mass spectrometry (DXMS) for the rapid identification of disordered segments of proteins and design of protein constructs that are more suitable for structural analysis by NMR. In this benchmark study, DXMS is applied to five NMR protein targets chosen from the Northeast Structural Genomics project. These data were then used to design optimized constructs for three partially disordered proteins. Truncated proteins obtained by deletion of disordered N‐ and C‐terminal tails were evaluated using ¹H‐¹⁵N HSQC and ¹H‐¹⁵N heteronuclear NOE NMR experiments to assess their structural integrity. These constructs provide significantly improved NMR spectra, with minimal structural perturbations to the ordered regions of the protein structure. As a representative example, we compare the solution structures of the full length and DXMS‐based truncated construct for a 77‐residue partially disordered DUF896 family protein YnzC from Bacillus subtilis, where deletion of the disordered residues (ca. 40% of the protein) does not affect the native structure. In addition, we demonstrate that throughput of the DXMS process can be increased by analyzing mixtures of up to four proteins without reducing the sequence coverage for each protein. Our results demonstrate that DXMS can serve as a central component of a process for optimizing protein constructs for NMR structure determination. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

Structure and Function of the Vacuolar H+-ATPase: Moving from Low-Resolution Models to High-Resolution Structures

In the absence of a high-resolution structure for the vacuolar H⁺-ATPase, a number of approaches can yield valuable information about structure/function relationships in the enzyme. Electron microscopy can provide not only a representation of the overall architecture of the complex, but also a low-resolution map onto which structures solved for individually expressed subunits can be fitted. Here we review the possibilities for electron microscopy of the Saccharomyces V-ATPase and examine the suitability of V-ATPase subunits for expression in high yield prokaryotic systems, a key step towards high-resolution structural studies. We also review the role of experimentally-derived structural models in understanding structure/function relationships in the V-ATPase, with particular reference to the complex of proton-translocating 16 kDa proteolipids in the membrane domain of the V-ATPase. This model in turn makes testable predictions about the sites of binding of bafilomycins and the functional interactions between the proteolipid and the single-copy membrane subunit Vph1p, with implications for the constitution of the proton translocation pathway.     

Structure and Disorder in an Unfolded State under Nondenaturing Conditions from Ensemble Models Consistent with a Large Number of Experimental Restraints

Obtaining detailed structural models of disordered states of proteins under nondenaturing conditions is important for a better understanding of both functional intrinsically disordered proteins and unfolded states of folded proteins. Extensive experimental characterization of the drk N-terminal SH3 domain unfolded state has shown that, although it appears to be highly disordered, it possesses significant nonrandom secondary and tertiary structure. In our previous attempts to generate structural models of the unfolded state using the program ENSEMBLE, we were limited by insufficient experimental restraints and conformational sampling. In this study, we have vastly expanded our experimental restraint set to include ¹H-¹⁵N residual dipolar couplings, small-angle X-ray scattering measurements, nitroxide paramagnetic relaxation enhancements, O₂-induced ¹³C paramagnetic shifts, hydrogen-exchange protection factors, and ¹⁵N R₂ data, in addition to the previously used nuclear Overhauser effects, amino terminal Cu²⁺-Ni²⁺ binding paramagnetic relaxation enhancements, J-couplings, chemical shifts, hydrodynamic radius, and solvent accessibility restraints. We have also implemented a new ensemble calculation methodology that uses iterative conformational sampling and seeks to calculate the simplest possible ensemble models. As a result, we can now generate ensembles that are consistent with much larger experimental data sets than was previously possible. Although highly heterogeneous and having broad molecular size distributions, the calculated drk N-terminal SH3 domain unfolded-state ensembles have very different properties than expected for random or statistical coils and possess significant nonnative α-helical structure and both native-like and nonnative tertiary structure.     

High-Resolution NMR Reveals Secondary Structure and Folding of Amino Acid Transporter from Outer Chloroplast Membrane

Solving high-resolution structures for membrane proteins continues to be a daunting challenge in the structural biology community. In this study we report our high-resolution NMR results for a transmembrane protein, outer envelope protein of molar mass 16 kDa (OEP16), an amino acid transporter from the outer membrane of chloroplasts. Three-dimensional, high-resolution NMR experiments on the ¹³C, ¹⁵N, ²H-triply-labeled protein were used to assign protein backbone resonances and to obtain secondary structure information. The results yield over 95% assignment of N, H_N, CO, C_α, and C_β chemical shifts, which is essential for obtaining a high resolution structure from NMR data. Chemical shift analysis from the assignment data reveals experimental evidence for the first time on the location of the secondary structure elements on a per residue basis. In addition T _1Z and T₂ relaxation experiments were performed in order to better understand the protein dynamics. Arginine titration experiments yield an insight into the amino acid residues responsible for protein transporter function. The results provide the necessary basis for high-resolution structural determination of this important plant membrane protein.     

VITAL NMR: using chemical shift derived secondary structure information for a limited set of amino acids to assess homology model accuracy

Homology modeling is a powerful tool for predicting protein structures, whose success depends on obtaining a reasonable alignment between a given structural template and the protein sequence being analyzed. In order to leverage greater predictive power for proteins with few structural templates, we have developed a method to rank homology models based upon their compliance to secondary structure derived from experimental solid-state NMR (SSNMR) data. Such data is obtainable in a rapid manner by simple SSNMR experiments (e.g., ¹³C–¹³C 2D correlation spectra). To test our homology model scoring procedure for various amino acid labeling schemes, we generated a library of 7,474 homology models for 22 protein targets culled from the TALOS+/SPARTA+ training set of protein structures. Using subsets of amino acids that are plausibly assigned by SSNMR, we discovered that pairs of the residues Val, Ile, Thr, Ala and Leu (VITAL) emulate an ideal dataset where all residues are site specifically assigned. Scoring the models with a predicted VITAL site-specific dataset and calculating secondary structure with the Chemical Shift Index resulted in a Pearson correlation coefficient (−0.75) commensurate to the control (−0.77), where secondary structure was scored site specifically for all amino acids (ALL 20) using STRIDE. This method promises to accelerate structure procurement by SSNMR for proteins with unknown folds through guiding the selection of remotely homologous protein templates and assessing model quality.     

Predictive Computational Models of Substrate Binding by a Nucleoside Transporter

Transporters play a vital role in both the resistance mechanisms of existing drugs and effective targeting of their replacements. Melarsoprol and diamidine compounds similar to pentamidine and furamidine are primarily taken up by trypanosomes of the genus Trypanosoma brucei through the P2 aminopurine transporter. In standardized competition experiments with [³H]adenosine, P2 transporter inhibition constants (K_i) have been determined for a diverse dataset of adenosine analogs, diamidines, Food and Drug Administration-approved compounds and analogs thereof, and custom-designed trypanocidal compounds. Computational biology has been employed to investigate compound structure diversity in relation to P2 transporter interaction. These explorations have led to models for inhibition predictions of known and novel compounds to obtain information about the molecular basis for P2 transporter inhibition. A common pharmacophore for P2 transporter inhibition has been identified along with other key structural characteristics. Our model provides insight into P2 transporter interactions with known compounds and contributes to strategies for the design of novel antiparasitic compounds. This approach offers a quantitative and predictive tool for molecular recognition by specific transporters without the need for structural or even primary sequence information of the transport protein.     

Molecular modeling studies,synthesis and biological evaluation of dabigatran analogues as thrombin inhibitors

In this work, 48 thrombin inhibitors based on the structural scaffold of dabigatran were analyzed using a combination of molecular modeling techniques. We generated three-dimensional quantitative structure–activity relationship (3D-QSAR) models based on three alignments for both comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) to highlight the structural requirements for thrombin protein inhibition. In addition to the 3D-QSAR study, Topomer CoMFA model also was established with a higher leave-one-out cross-validation q² and a non-cross-validation r², which suggest that the three models have good predictive ability. The results indicated that the steric, hydrophobic and electrostatic fields play key roles in QSAR model. Furthermore, we employed molecular docking and re-docking simulation explored the binding relationship of the ligand and the receptor protein in detail. Molecular docking simulations identified several key interactions that were also indicated through 3D-QSAR analysis. On the basis of the obtained results, two compounds were designed and predicted by three models, the biological evaluation in vitro (IC₅₀) demonstrated that these molecular models were effective for the development of novel potent thrombin inhibitors.     

Predictive Models of Primary Tropical Forest Structure from Geomorphometric Variables Based on SRTM in the Tapajós Region,Brazilian Amazon

Surveying primary tropical forest over large regions is challenging. Indirect methods of relating terrain information or other external spatial datasets to forest biophysical parameters can provide forest structural maps at large scales but the inherent uncertainties need to be evaluated fully. The goal of the present study was to evaluate relief characteristics, measured through geomorphometric variables, as predictors of forest structural characteristics such as average tree basal area (BA) and height (H) and average percentage canopy openness (CO). Our hypothesis is that geomorphometric variables are good predictors of the structure of primary tropical forest, even in areas, with low altitude variation. The study was performed at the Tapajós National Forest, located in the Western State of Pará, Brazil. Forty-three plots were sampled. Predictive models for BA, H and CO were parameterized based on geomorphometric variables using multiple linear regression. Validation of the models with nine independent sample plots revealed a Root Mean Square Error (RMSE) of 3.73 m²/ha (20%) for BA, 1.70 m (12%) for H, and 1.78% (21%) for CO. The coefficient of determination between observed and predicted values were r² = 0.32 for CO, r² = 0.26 for H and r² = 0.52 for BA. The models obtained were able to adequately estimate BA and CO. In summary, it can be concluded that relief variables are good predictors of vegetation structure and enable the creation of forest structure maps in primary tropical rainforest with an acceptable uncertainty.     

Stable Isotope Metabolic Labeling with a Novel 15N-Enriched Bacteria Diet for Improved Proteomic Analyses of Mouse Models for Psychopathologies

The identification of differentially regulated proteins in animal models of psychiatric diseases is essential for a comprehensive analysis of associated psychopathological processes. Mass spectrometry is the most relevant method for analyzing differences in protein expression of tissue and body fluid proteomes. However, standardization of sample handling and sample-to-sample variability are problematic. Stable isotope metabolic labeling of a proteome represents the gold standard for quantitative mass spectrometry analysis. The simultaneous processing of a mixture of labeled and unlabeled samples allows a sensitive and accurate comparative analysis between the respective proteomes. Here, we describe a cost-effective feeding protocol based on a newly developed ¹⁵N bacteria diet based on Ralstonia eutropha protein, which was applied to a mouse model for trait anxiety. Tissue from ¹⁵N-labeled vs. ¹⁴N-unlabeled mice was examined by mass spectrometry and differences in the expression of glyoxalase-1 (GLO1) and histidine triad nucleotide binding protein 2 (Hint2) proteins were correlated with the animals'' psychopathological behaviors for methodological validation and proof of concept, respectively. Additionally, phenotyping unraveled an antidepressant-like effect of the incorporation of the stable isotope ¹⁵N into the proteome of highly anxious mice. This novel phenomenon is of considerable relevance to the metabolic labeling method and could provide an opportunity for the discovery of candidate proteins involved in depression-like behavior. The newly developed ¹⁵N bacteria diet provides researchers a novel tool to discover disease-relevant protein expression differences in mouse models using quantitative mass spectrometry.     

VORFFIP-Driven Dock: V-D2OCK,a Fast and Accurate Protein Docking Strategy

The experimental determination of the structure of protein complexes cannot keep pace with the generation of interactomic data, hence resulting in an ever-expanding gap. As the structural details of protein complexes are central to a full understanding of the function and dynamics of the cell machinery, alternative strategies are needed to circumvent the bottleneck in structure determination. Computational protein docking is a valid and valuable approach to model the structure of protein complexes. In this work, we describe a novel computational strategy to predict the structure of protein complexes based on data-driven docking: VORFFIP-driven dock (V-D²OCK). This new approach makes use of our newly described method to predict functional sites in protein structures, VORFFIP, to define the region to be sampled during docking and structural clustering to reduce the number of models to be examined by users. V-D²OCK has been benchmarked using a validated and diverse set of protein complexes and compared to a state-of-art docking method. The speed and accuracy compared to contemporary tools justifies the potential use of VD²OCK for high-throughput, genome-wide, protein docking. Finally, we have developed a web interface that allows users to browser and visualize V-D²OCK predictions from the convenience of their web-browsers.     

Protein structure prediction using sparse dipolar coupling data

Residual dipolar coupling (RDC) represents one of the most exciting emerging NMR techniques for protein structure studies. However, solving a protein structure using RDC data alone is still a highly challenging problem. We report here a computer program, RDC-PROSPECT, for protein structure prediction based on a structural homolog or analog of the target protein in the Protein Data Bank (PDB), which best aligns with the ¹⁵N–¹H RDC data of the protein recorded in a single ordering medium. Since RDC-PROSPECT uses only RDC data and predicted secondary structure information, its performance is virtually independent of sequence similarity between a target protein and its structural homolog/analog, making it applicable to protein targets beyond the scope of current protein threading techniques. We have tested RDC-PROSPECT on all ¹⁵N–¹H RDC data (representing 43 proteins) deposited in the BioMagResBank (BMRB) database. The program correctly identified structural folds for 83.7% of the target proteins, and achieved an average alignment accuracy of 98.1% residues within a four-residue shift.     

SpaK/SpaR Two-component System Characterized by a Structure-driven Domain-fusion Method and in Vitro Phosphorylation Studies

Here we introduce a quantitative structure-driven computational domain-fusion method, which we used to predict the structures of proteins believed to be involved in regulation of the subtilin pathway in Bacillus subtilis, and used to predict a protein-protein complex formed by interaction between the proteins. Homology modeling of SpaK and SpaR yielded preliminary structural models based on a best template for SpaK comprising a dimer of a histidine kinase, and for SpaR a response regulator protein. Our LGA code was used to identify multi-domain proteins with structure homology to both modeled structures, yielding a set of domain-fusion templates then used to model a hypothetical SpaK/SpaR complex. The models were used to identify putative functional residues and residues at the protein-protein interface, and bioinformatics was used to compare functionally and structurally relevant residues in corresponding positions among proteins with structural homology to the templates. Models of the complex were evaluated in light of known properties of the functional residues within two-component systems involving His-Asp phosphorelays. Based on this analysis, a phosphotransferase complexed with a beryllofluoride was selected as the optimal template for modeling a SpaK/SpaR complex conformation. In vitro phosphorylation studies performed using wild type and site-directed SpaK mutant proteins validated the predictions derived from application of the structure-driven domain-fusion method: SpaK was phosphorylated in the presence of ³²P-ATP and the phosphate moiety was subsequently transferred to SpaR, supporting the hypothesis that SpaK and SpaR function as sensor and response regulator, respectively, in a two-component signal transduction system, and furthermore suggesting that the structure-driven domain-fusion approach correctly predicted a physical interaction between SpaK and SpaR. Our domain-fusion algorithm leverages quantitative structure information and provides a tool for generation of hypotheses regarding protein function, which can then be tested using empirical methods.     

Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis – a case study on HIV-1 protease inhibitors

Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r_loo² values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r² values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r_pred²) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r_pred² of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.     

Molecular Modeling of the Full-length Human TRPV1 Channel in Closed and Desensitized States

The transient receptor potential vanilloid subtype 1 (TRPV1) is a member of the TRP family gated by vanilloids, heat, and protons. Structurally, TRPV1 subunits have a modular architecture underlying different functionalities, namely stimuli recognition, channel gating, ion selectivity, subunit oligomerization, and regulation by intracellular signaling molecules. Considering modular organization and recent structural information in the ion channel field, we have modeled a full-length TRPV1 by assembly of its major modules: the cytosolic N-terminal, C-terminal, and membrane-spanning region. For N-terminal, we used the ankyrin repeat structure fused with the N-end segment. The membrane domain was modeled with the structure of the eukaryotic, voltage-gated Kv1.2 K⁺ channel. The C-terminus was cast using the coordinates of HCN channels. The extensive structure–function data available for TRPV1 was used to validate the models in terms of the location of molecular determinants of function in the structure. Additionally, the current information allowed the modeling of the vanilloid receptor in the closed and desensitized states. The closed state shows the N-terminal module highly exposed and accessible to adenosine triphosphate and the C-terminal accessible to phosphoinositides. In contrast, the desensitized state depicts the N-terminal and C-terminal modules close together, compatible with an interaction mediated by Ca²⁺–calmodulin complex. These models identify potential previously unrecognized intra- and interdomain interactions that may play an important functional role. Although the molecular models should be taken with caution, they provide a helpful tool that yields testable hypothesis that further our understanding on ion channels work in terms of underlying protein structure.     

Extensive de novo solid-state NMR assignments of the 33 kDa C-terminal domain of the Ure2 prion

We present the de novo resonance assignments for the crystalline 33 kDa C-terminal domain of the Ure2 prion using an optimized set of five 3D solid-state NMR spectra. We obtained, using a single uniformly ¹³C, ¹⁵N labeled protein sample, sequential chemical-shift information for 74% of the N, Cα, Cβ triples, and for 80% of further side-chain resonances for these spin systems. We describe the procedures and protocols devised, and discuss possibilities and limitations of the assignment of this largest protein assigned today by solid-state NMR, and for which no solution-state NMR shifts were available. A comparison of the NMR chemical shifts with crystallographic data reveals that regions with high crystallographic B-factors are particularly difficult to assign. While the secondary structure elements derived from the chemical shift data correspond mainly to those present in the X-ray crystal structure, we detect an additional helical element and structural variability in the protein crystal, most probably originating from the different molecules in the asymmetric unit, with the observation of doubled resonances in several parts, including entire stretches, of the protein. Our results provide the point of departure towards an atomic-resolution structural analysis of the C-terminal Ure2p domain in the context of the full-length prion fibrils.     

RDC derived protein backbone resonance assignment using fragment assembly

Experimental residual dipolar couplings (RDCs) in combination with structural models have the potential for accelerating the protein backbone resonance assignment process because RDCs can be measured accurately and interpreted quantitatively. However, this application has been limited due to the need for very high-resolution structural templates. Here, we introduce a new approach to resonance assignment based on optimal agreement between the experimental and calculated RDCs from a structural template that contains all assignable residues. To overcome the inherent computational complexity of such a global search, we have adopted an efficient two-stage search algorithm and included connectivity data from conventional assignment experiments. In the first stage, a list of strings of resonances (CA-links) is generated via exhaustive searches for short segments of sequentially connected residues in a protein (local templates), and then ranked by the agreement of the experimental ¹³C_α chemical shifts and ¹⁵N-¹H RDCs to the predicted values for each local template. In the second stage, the top CA-links for different local templates in stage I are combinatorially connected to produce CA-links for all assignable residues. The resulting CA-links are ranked for resonance assignment according to their measured RDCs and predicted values from a tertiary structure. Since the final RDC ranking of CA-links includes all assignable residues and the assignment is derived from a “global minimum”, our approach is far less reliant on the quality of experimental data and structural templates. The present approach is validated with the assignments of several proteins, including a 42 kDa maltose binding protein (MBP) using RDCs and structural templates of varying quality. Since backbone resonance assignment is an essential first step for most of biomolecular NMR applications and is often a bottleneck for large systems, we expect that this new approach will improve the efficiency of the assignment process for small and medium size proteins and will extend the size limits assignable by current methods for proteins with structural models.     

Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction

While chemical shifts are invaluable for obtaining structural information from proteins, they also offer one of the rare ways to obtain information about protein dynamics. A necessary tool in transforming chemical shifts into structural and dynamic information is chemical shift prediction. In our previous work we developed a method for 4D prediction of protein ¹H chemical shifts in which molecular motions, the 4th dimension, were modeled using molecular dynamics (MD) simulations. Although the approach clearly improved the prediction, the X-ray structures and single NMR conformers used in the model cannot be considered fully realistic models of protein in solution. In this work, NMR ensembles (NMRE) were used to expand the conformational space of proteins (e.g. side chains, flexible loops, termini), followed by MD simulations for each conformer to map the local fluctuations. Compared with the non-dynamic model, the NMRE+MD model gave 6–17% lower root-mean-square (RMS) errors for different backbone nuclei. The improved prediction indicates that NMR ensembles with MD simulations can be used to obtain a more realistic picture of protein structures in solutions and moreover underlines the importance of short and long time-scale dynamics for the prediction. The RMS errors of the NMRE+MD model were 0.24, 0.43, 0.98, 1.03, 1.16 and 2.39 ppm for ¹Hα, ¹HN, ¹³Cα, ¹³Cβ, ¹³CO and backbone ¹⁵N chemical shifts, respectively. The model is implemented in the prediction program 4DSPOT, available at .     

Accurate geometries for “Mountain pass” regions of the Ramachandran plot using quantum chemical calculations

Unusual local arrangements of protein in Ramachandran space are not well represented by standard geometry tools used in either protein structure refinement using simple harmonic geometry restraints or in protein simulations using molecular mechanics force fields. In contrast, quantum chemical computations using small poly‐peptide molecular models can predict accurate geometries for any well‐defined backbone Ramachandran orientation. For conformations along transition regions—ϕ from −60 to 60°—a very good agreement with representative high‐resolution experimental X‐ray (≤1.5 Å) protein structures is obtained for both backbone C⁻¹‐N‐C_α angle and the nonbonded O⁻¹…C distance, while “standard geometry” leads to the “clashing” of O…C atoms and Amber FF99SB predicts distances too large by about 0.15 Å. These results confirm that quantum chemistry computations add valuable support for detailed analysis of local structural arrangements in proteins, providing improved or missing data for less understood high‐energy or unusual regions.