BioMagResBank

Side chain resonances in static oriented proton-decoupled 15N solid-state NMR spectra of membrane proteins

Proton-decoupled (15)N solid-state NMR spectra are used to analyze the structure, dynamics, and membrane topology of proteins uniformly labeled with (15)N. Preparation of the proteins by bacterial overexpression results in the labeling not only of the backbone amides but also of nitrogens localized within the side chains of arginine, glutamine, tryptophan, asparagines, lysines, and histidines. Most of these side chain resonances appear in the spectral region of the anisotropic backbone amides, and residual intensities have been observed also in cross-polarization spectra. In the past this issue has received little attention although it can cause ambiguities during assignment. Here we show that by combining cross-polarization and Hahn echo solid-state NMR experiments, it is possible to differentiate between side chain and backbone resonances. This is demonstrated using experimental and simulated (15)N spectra of oriented purple membranes, diphtheria toxin T domain and Bcl-x(L).

PINE-SPARKY: graphical interface for evaluating automated probabilistic peak assignments in protein NMR spectroscopy

Summary: PINE-SPARKY supports the rapid, user-friendly and efficient visualization of probabilistic assignments of NMR chemical shifts to specific atoms in the covalent structure of a protein in the context of experimental NMR spectra. PINE-SPARKY is based on the very popular SPARKY package for visualizing multidimensional NMR spectra (T. D. Goddard and D. G. Kneller, SPARKY 3, University of California, San Francisco). PINE-SPARKY consists of a converter (PINE2SPARKY), which takes the output from an automated PINE-NMR analysis and transforms it into SPARKY input, plus a number of SPARKY extensions. Assignments and their probabilities obtained in the PINE-NMR step are visualized as labels in SPARKY's spectrum view. Three SPARKY extensions (PINE Assigner, PINE Graph Assigner, and Assign the Best by PINE) serve to manipulate the labels that signify the assignments and their probabilities. PINE Assigner lists all possible assignments for a peak selected in the dialog box and enables the user to choose among these. A window in PINE Graph Assigner shows all atoms in a selected residue along with all atoms in its adjacent residues; in addition, it displays a ranked list of PINE-derived connectivity assignments to any selected atom. Assign the Best-by-PINE allows the user to choose a probability threshold and to automatically accept as “fixed” all assignments above that threshold; following this operation, only the less certain assignments need to be examined visually. Once assignments are fixed, the output files generated by PINE-SPARKY can be used as input to PINE-NMR for further refinements.

Availability: The program, in the form of source code and binary code along with tutorials and reference manuals, is available at http://pine.nmrfam.wisc.edu/PINE-SPARKY.

Contact:whlee@nmrfam.wisc.edu; markley@nmrfam.wisc.edu

Efficient and accurate estimation of relative order tensors from lambda-maps

The rapid increase in the availability of RDC data from multiple alignment media in recent years has necessitated the development of more sophisticated analyses that extract the RDC data's full information content. This article presents an analysis of the distribution of RDCs from two media (2D-RDC data), using the information obtained from a lambda-map. This article also introduces an efficient algorithm, which leverages these findings to extract the order tensors for each alignment medium using unassigned RDC data in the absence of any structural information. The results of applying this 2D-RDC analysis method to synthetic and experimental data are reported in this article. The relative order tensor estimates obtained from the 2D-RDC analysis are compared to order tensors obtained from the program REDCAT after using assignment and structural information. The final comparisons indicate that the relative order tensors estimated from the unassigned 2D-RDC method very closely match the results from methods that require assignment and structural information. The presented method is successful even in cases with small datasets. The results of analyzing experimental RDC data for the protein 1P7E are presented to demonstrate the potential of the presented work in accurately estimating the principal order parameters from RDC data that incompletely sample the RDC space. In addition to the new algorithm, a discussion of the uniqueness of the solutions is presented; no more than two clusters of distinct solutions have been shown to satisfy each lambda-map.

Empirical correlation between protein backbone 15N and 13C secondary chemical shifts and its application to nitrogen chemical shift re-referencing

The linear analysis of chemical shifts (LACS) has provided a robust method for identifying and correcting 13C chemical shift referencing problems in data from protein NMR spectroscopy. Unlike other approaches, LACS does not require prior knowledge of the three-dimensional structure or inference of the secondary structure of the protein. It also does not require extensive assignment of the NMR data. We report here a way of extending the LACS approach to 15N NMR data from proteins, so as to enable the detection and correction of inconsistencies in chemical shift referencing for this nucleus. The approach is based on our finding that the secondary 15N chemical shift of the backbone nitrogen atom of residue i is strongly correlated with the secondary chemical shift difference (experimental minus random coil) between the alpha and beta carbons of residue i-1. Thus once alpha and beta 13C chemical shifts are available (their difference is referencing error-free), the 15N referencing can be validated, and an appropriate offset correction can be derived. This approach can be implemented prior to a structure determination and can be used to analyze potential referencing problems in database data not associated with three-dimensional structure. Application of the LACS algorithm to the current BMRB protein chemical shift database, revealed that nearly 35% of the BMRB entries have delta 15N values mis-referenced by over 0.7 ppm and over 25% of them have delta 1HN values mis-referenced by over 0.12 ppm. One implication of the findings reported here is that a backbone 15N chemical shift provides a better indicator of the conformation of the preceding residue than of the residue itself.

Solution structure of the Pseudomonas putida protein PpPutA45 and its DNA complex

Proline utilization A (PutA) is a membrane-associated multifunctional enzyme that catalyzes the oxidation of proline to glutamate in a two-step process. In certain, gram-negative bacteria such as Pseudomonas putida, PutA also acts as an auto repressor in the cytoplasm, when an insufficient concentration of proline is available. Here, the N-terminal residues 1-45 of PutA from P. putida (PpPutA45) are shown to be responsible for DNA binding and dimerization. The solution structure of PpPutA45 was determined using NMR methods, where the protein is shown to be a symmetrical homodimer (12 kDa) consisting of two ribbon-helix-helix (RHH) structures. DNA sequence recognition by PpPutA45 was determined using DNA gel mobility shift assays and NMR chemical shift perturbations (CSPs). PpPutA45 was shown to bind a 14 base-pair DNA oligomer (5'-GCGGTTGCACCTTT-3'). A model of the PpPutA45-DNA oligomer complex was generated using Haddock 2.1. The antiparallel beta-sheet that results from PpPutA45 dimerization serves as the DNA recognition binding site by inserting into the DNA major groove. The dimeric core of four alpha-helices provides a structural scaffold for the beta-sheet from which residues Thr5, Gly7, and Lys9 make sequence-specific contacts with the DNA. The structural model implies flexibility of Lys9 which can make hydrogen bond contacts with either guanine or thymine. The high sequence and structure conservation of the PutA RHH domain suggest interdomain interactions play an important role in the evolution of the protein.

GeNMR: a web server for rapid NMR-based protein structure determination

GeNMR (GEnerate NMR structures) is a web server for rapidly generating accurate 3D protein structures using sequence data, NOE-based distance restraints and/or NMR chemical shifts as input. GeNMR accepts distance restraints in XPLOR or CYANA format as well as chemical shift files in either SHIFTY or BMRB formats. The web server produces an ensemble of PDB coordinates for the protein within 15-25 min, depending on model complexity and completeness of experimental restraints. GeNMR uses a pipeline of several pre-existing programs and servers to calculate the actual protein structure. In particular, GeNMR combines genetic algorithms for structure optimization along with homology modeling, chemical shift threading, torsion angle and distance predictions from chemical shifts/NOEs as well as ROSETTA-based structure generation and simulated annealing with XPLOR-NIH to generate and/or refine protein coordinates. GeNMR greatly simplifies the task of protein structure determination as users do not have to install or become familiar with complex stand-alone programs or obscure format conversion utilities. Tests conducted on a sample of 90 proteins from the BioMagResBank indicate that GeNMR produces high-quality models for all protein queries, regardless of the type of NMR input data. GeNMR was developed to facilitate rapid, user-friendly structure determination of protein structures via NMR spectroscopy. GeNMR is accessible at http://www.genmr.ca.

Relationship between chemical shift value and accessible surface area for all amino acid atoms

BACKGROUND

Chemical shifts obtained from NMR experiments are an important tool in determining secondary, even tertiary, protein structure. The main repository for chemical shift data is the BioMagResBank, which provides NMR-STAR files with this type of information. However, it is not trivial to link this information to available coordinate data from the PDB for non-backbone atoms due to atom and chain naming differences, as well as sequence numbering changes.

RESULTS

We here describe the analysis of a consistent set of chemical shift and coordinate data, in which we focus on the relationship between the per-atom solvent accessible surface area (ASA) in the reported coordinates and their reported chemical shift value. The data is available online on http://www.ebi.ac.uk/pdbe/docs/NMR/shiftAnalysis/index.html.

CONCLUSION

Atoms with zero per-atom ASA have a significantly larger chemical shift dispersion and often have a different chemical shift distribution compared to those that are solvent accessible. With higher per-atom ASA, the chemical shift values also tend towards random coil values. The per-atom ASA, although not the determinant of the chemical shift, thus provides a way to directly correlate chemical shift information to the atomic coordinates.

Probabilistic interaction network of evidence algorithm and its application to complete labeling of peak lists from protein NMR spectroscopy

The process of assigning a finite set of tags or labels to a collection of observations, subject to side conditions, is notable for its computational complexity. This labeling paradigm is of theoretical and practical relevance to a wide range of biological applications, including the analysis of data from DNA microarrays, metabolomics experiments, and biomolecular nuclear magnetic resonance (NMR) spectroscopy. We present a novel algorithm, called Probabilistic Interaction Network of Evidence (PINE), that achieves robust, unsupervised probabilistic labeling of data. The computational core of PINE uses estimates of evidence derived from empirical distributions of previously observed data, along with consistency measures, to drive a fictitious system M with Hamiltonian H to a quasi-stationary state that produces probabilistic label assignments for relevant subsets of the data. We demonstrate the successful application of PINE to a key task in protein NMR spectroscopy: that of converting peak lists extracted from various NMR experiments into assignments associated with probabilities for their correctness. This application, called PINE-NMR, is available from a freely accessible computer server (http://pine.nmrfam.wisc.edu). The PINE-NMR server accepts as input the sequence of the protein plus user-specified combinations of data corresponding to an extensive list of NMR experiments; it provides as output a probabilistic assignment of NMR signals (chemical shifts) to sequence-specific backbone and aliphatic side chain atoms plus a probabilistic determination of the protein secondary structure. PINE-NMR can accommodate prior information about assignments or stable isotope labeling schemes. As part of the analysis, PINE-NMR identifies, verifies, and rectifies problems related to chemical shift referencing or erroneous input data. PINE-NMR achieves robust and consistent results that have been shown to be effective in subsequent steps of NMR structure determination.

What mathematicians call the “labeling problem” underlies difficulties in interpreting many classes of complex biological data. To derive valid inferences from multiple, noisy datasets, one must consider all possible combinations of the data to find the solution that best matches the experimental evidence. Exhaustive searches totally outstrip current computer resources, and, as a result, it has been necessary to resort to approximations such as branch and bound or Monte Carlo simulations, which have the disadvantages of being limited to use in separate steps of the analysis and not providing the final results in a probabilistic fashion that allows the quality of the answers to be evaluated. The Probabilistic Interaction Network of Evidence (PINE) algorithm that we present here offers a general solution to this problem. We have demonstrated the usefulness of the PINE approach by applying it to one of the major bottlenecks in NMR spectroscopy. The PINE-NMR server takes as input the sequence of a protein and the peak lists from one or more multidimensional NMR experiments and provides as output a probabilistic assignment of the NMR signals to specific atoms in the protein's covalent structure and a self-consistent probabilistic analysis of the protein's secondary structure.

On projection-reconstruction NMR

Three most simple Projection-Reconstruction algorithms, namely, the Lowest-Value, Additive Back-Projection and Hybrid Back-Projection/Lowest-Value algorithms, are analyzed. A new, also simple, algorithm that reconstructs the spectrum by utilizing the amplitude histogram at each reconstruction point, is explored. The algorithms are tested using simulated spectra. While all the algorithms considered can potentially result in substantial reduction of the amount of data needed for reconstruction, they can suffer from a number of drawbacks. In particular, they often fail when the spectra are noisy and/or contain overlapping peaks. When compared to the existing algorithms, the new, histogram-based algorithm has the potential advantage of being able to deal with spectra containing peaks of opposite phase.

Inter- and intramolecular contacts in a membrane protein/surfactant complex observed by heteronuclear dipole-to-dipole cross-relaxation

Heteronuclear dipole-to-dipole cross-relaxation has been applied to exploring intermolecular interactions and intramolecular spatial proximities in a large supramolecular structure comprised of a beta-barrel membrane protein, OmpX, in complex with a polymeric surfactant, amphipol A8-35. The experiments, performed in either the laboratory or the rotating frame, reveal the existence of intermolecular contacts between aromatic amino acids and specific groups of the polymer, in addition to intra-protein dipolar interactions, some of them involving carbonyl carbons. This study opens the perspective of collecting by NMR spectroscopy a new kind of through-space structural information involving aromatic and carbonyl (13)C atoms of large proteins.

Distance matrix-based approach to protein structure prediction

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2)--the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the dynamics. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM) that is based on the contact matrix C (related to D), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to atomic molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide a similar sampling of conformations. Finally, we use distance constraints from databases of known protein structures for structure refinement. We use the distributions of distances of various types in known protein structures to obtain the most probable ranges or the mean-force potentials for the distances. We then impose these constraints on structures to be refined or include the mean-force potentials directly in the energy minimization so that more plausible structural models can be built. This approach has been successfully used by us in 2006 in the CASPR structure refinement (http://predictioncenter.org/caspR).

A conserved glutamine plays a central role in LOV domain signal transmission and its duration

Light is a key stimulus for plant biological functions, several of which are controlled by light-activated kinases known as phototropins, a group of kinases that contain two light-sensing domains (LOV, light-oxygen-voltage domains) and a C-terminal serine/threonine kinase domain. The second sensory domain, LOV2, plays a key role in regulating kinase enzymatic activity via the photochemical formation of a covalent adduct between a LOV2 cysteine residue and an internally bound flavin mononucleotide (FMN) chromophore. Subsequent conformational changes in LOV2 lead to the unfolding of a peripheral Jalpha helix and, ultimately, phototropin kinase activation. To date, the mechanism coupling bond formation and helix dissociation has remained unclear. Previous studies found that a conserved glutamine residue [Q513 in the Avena sativa phototropin 1 LOV2 (AsLOV2) domain] switches its hydrogen bonding pattern with FMN upon light stimulation. Located in the immediate vicinity of the FMN binding site, this Gln residue is provided by the Ibeta strand that interacts with the Jalpha helix, suggesting a route for signal propagation from the core of the LOV domain to its peripheral Jalpha helix. To test whether Q513 plays a key role in tuning the photochemical and transduction properties of AsLOV2, we designed two point mutations, Q513L and Q513N, and monitored the effects on the chromophore and protein using a combination of UV-visible absorbance and circular dichroism spectroscopy, limited proteolysis, and solution NMR. The results show that these mutations significantly dampen the changes between the dark and lit state AsLOV2 structures, leaving the protein in a pseudodark state (Q513L) or a pseudolit state (Q513N). Further, both mutations changed the photochemical properties of this receptor, in particular the lifetime of the photoexcited signaling states. Together, these data establish that this residue plays a central role in both spectral tuning and signal propagation from the core of the LOV domain through the Ibeta strand to the peripheral Jalpha helix.

Engineering a stable and selective peptide blocker of the Kv1.3 channel in T lymphocytes

Kv1.3 potassium channels maintain the membrane potential of effector memory (T(EM)) T cells that are important mediators of multiple sclerosis, type 1 diabetes mellitus, and rheumatoid arthritis. The polypeptide ShK-170 (ShK-L5), containing an N-terminal phosphotyrosine extension of the Stichodactyla helianthus ShK toxin, is a potent and selective blocker of these channels. However, a stability study of ShK-170 showed minor pH-related hydrolysis and oxidation byproducts that were exacerbated by increasing temperatures. We therefore engineered a series of analogs to minimize the formation of these byproducts. The analog with the greatest stability, ShK-192, contains a nonhydrolyzable phosphotyrosine surrogate, a methionine isostere, and a C-terminal amide. ShK-192 shows the same overall fold as ShK, and there is no evidence of any interaction between the N-terminal adduct and the rest of the peptide. The docking configuration of ShK-192 in Kv1.3 shows the N-terminal para-phosphonophenylalanine group lying at the junction of two channel monomers to form a salt bridge with Lys(411) of the channel. ShK-192 blocks Kv1.3 with an IC(50) of 140 pM and exhibits greater than 100-fold selectivity over closely related channels. After a single subcutaneous injection of 100 microg/kg, approximately 100 to 200 pM concentrations of active peptide is detectable in the blood of Lewis rats 24, 48, and 72 h after the injection. ShK-192 effectively inhibits the proliferation of T(EM) cells and suppresses delayed type hypersensitivity when administered at 10 or 100 microg/kg by subcutaneous injection once daily. ShK-192 has potential as a therapeutic for autoimmune diseases mediated by T(EM) cells.

NMRDyn: a program for NMR relaxation studies of protein association

Self-association is an important biological phenomenon that is associated with many cellular processes. NMR relaxation measurements provide data about protein molecular dynamics at the atomic level and are sensitive to changes induced by self-association. Thus, measurements and analysis of NMR relaxation data can provide structurally resolved information on self-association that would not be accessible otherwise. Here, we present a computer program, NMRdyn, which analyses relaxation data to provide parameters defining protein self-association. Unlike existing relaxation analysis software, NMRdyn can explicitly model the monomer-oligomer equilibrium while fitting measured relaxation data. Additionally, the program is packaged with a user-friendly interface, which is important because relaxation data can often be large and complex. NMRdyn is available from http://research1t.imb.uq.edu.au/nmr/NMRdyn.

Protein chaperones Q8ZP25_SALTY from Salmonella typhimurium and HYAE_ECOLI from Escherichia coli exhibit thioredoxin-like structures despite lack of canonical thioredoxin active site sequence motif

The structure of the 142-residue protein Q8ZP25_SALTY encoded in the genome of Salmonella typhimurium LT2 was determined independently by NMR and X-ray crystallography, and the structure of the 140-residue protein HYAE_ECOLI encoded in the genome of Escherichia coli was determined by NMR. The two proteins belong to Pfam (Finn et al. 34:D247-D251, 2006) PF07449, which currently comprises 50 members, and belongs itself to the 'thioredoxin-like clan'. However, protein HYAE_ECOLI and the other proteins of Pfam PF07449 do not contain the canonical Cys-X-X-Cys active site sequence motif of thioredoxin. Protein HYAE_ECOLI was previously classified as a [NiFe] hydrogenase-1 specific chaperone interacting with the twin-arginine translocation (Tat) signal peptide. The structures presented here exhibit the expected thioredoxin-like fold and support the view that members of Pfam family PF07449 specifically interact with Tat signal peptides.

Charting online OMICS resources: A navigational chart for clinical researchers

The life sciences have sprouted several popular and successful OMICS technologies that span all levels of biological information transfer. Ever since the start of the Human Genome Project, the then revolutionary idea to make all resulting data publicly available has been central to all of the efforts across OMICS technologies. As a result, a great variety of publicly available data repositories and resources is currently available to the research community. This widespread availability of data does come at the price of increased confusion on the part of the users, especially for those that see the OMICS technologies as tools to help unravel a larger biological or clinical question. We therefore provide a comprehensive overview of the available resources across OMICS fields, with a special emphasis on those databases that are relevant to the study of proteins. Additionally, we also describe various integrative systems that have been established, and highlight new developments in the field that can revolutionize the way in which live data integration is achieved over the internet.

HMDB: a knowledgebase for the human metabolome

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.

Structural insight into the recognition of the H3K4me3 mark by the TFIID subunit TAF3

Trimethylation of lysine residue K4 of histone H3 (H3K4me3) strongly correlates with active promoters for RNA polymerase II-transcribed genes. Several reader proteins, including the basal transcription factor TFIID, for this nucleosomal mark have been identified. Its TAF3 subunit specifically binds the H3K4me3 mark via its conserved plant homeodomain (PHD) finger. Here, we report the solution structure of the TAF3-PHD finger and its complex with an H3K4me3 peptide. Using a combination of NMR, mutagenesis, and affinity measurements, we reveal the structural basis of binding affinity, methylation-state specificity, and crosstalk with asymmetric dimethylation of R2. A unique local structure rearrangement in the K4me3-binding pocket of TAF3 due to a conserved sequence insertion underscores the requirement for cation-pi interactions by two aromatic residues. Interference by asymmetric dimethylation of arginine 2 suggests that a H3R2/K4 "methyl-methyl" switch in the histone code dynamically regulates TFIID-promoter association.

Estimation of relative order tensors, and reconstruction of vectors in space using unassigned RDC data and its application

Advances in NMR instrumentation and pulse sequence design have resulted in easier acquisition of Residual Dipolar Coupling (RDC) data. However, computational and theoretical analysis of this type of data has continued to challenge the international community of investigators because of their complexity and rich information content. Contemporary use of RDC data has required a-priori assignment, which significantly increases the overall cost of structural analysis. This article introduces a novel algorithm that utilizes unassigned RDC data acquired from multiple alignment media (nD-RDC, n3) for simultaneous extraction of the relative order tensor matrices and reconstruction of the interacting vectors in space. Estimation of the relative order tensors and reconstruction of the interacting vectors can be invaluable in a number of endeavors. An example application has been presented where the reconstructed vectors have been used to quantify the fitness of a template protein structure to the unknown protein structure. This work has other important direct applications such as verification of the novelty of an unknown protein and validation of the accuracy of an available protein structure model in drug design. More importantly, the presented work has the potential to bridge the gap between experimental and computational methods of structure determination.

Structure, dynamics, and selectivity of the sodium channel blocker mu-conotoxin SIIIA

mu-SIIIA, a novel mu-conotoxin from Conus striatus, appeared to be a selective blocker of tetrodotoxin-resistant sodium channels in frog preparations. It also exhibited potent analgesic activity in mice, although its selectivity profile against mammalian sodium channels remains unknown. We have determined the structure of mu-SIIIA in aqueous solution and characterized its backbone dynamics by NMR and its functional properties electrophysiologically. Consistent with the absence of hydroxyprolines, mu-SIIIA adopts a single conformation with all peptide bonds in the trans conformation. The C-terminal region contains a well-defined helix encompassing residues 11-16, while residues 3-5 in the N-terminal region form a helix-like turn resembling 3 10-helix. The Trp12 and His16 side chains are close together, as in the related conotoxin mu-SmIIIA, but Asn2 is more distant. Dynamics measurements show that the N-terminus and Ser9 have larger-magnitude motions on the subnanosecond time scale, while the C-terminus is more rigid. Cys4, Trp12, and Cys13 undergo significant conformational exchange on microsecond to millisecond time scales. mu-SIIIA is a potent, nearly irreversible blocker of Na V1.2 but also blocks Na V1.4 and Na V1.6 with submicromolar potency. The selectivity profile of mu-SIIIA, including poor activity against the cardiac sodium channel, Na V1.5, is similar to that of the closely related mu-KIIIA, suggesting that the C-terminal regions of both are critical for blocking neuronal Na V1.2. The structural and functional characterization described in this paper of an analgesic mu-conotoxin that targets neuronal subtypes of mammalian sodium channels provides a basis for the design of novel analogues with an improved selectivity profile.

Optimized expression and purification of myristoylated human neuronal calcium sensor 1 in E. coli

We have developed a protocol to produce large quantities of high purity myristoylated and non-myristoylated neuronal calcium sensor 1 (NCS-1) protein. NCS-1 is a member of the neuronal calcium sensor (NCS) family and plays an important role in modulating G-protein signaling and exocytosis pathways in cells. Many of these functions are calcium-dependent and require NCS-1 to be modified with an N-terminal myristoyl moiety. In our system, a C-terminally 6x His-tagged variant of NCS-1 was co-expressed with yeast N-myristoyltransferase (NMT) in ZYP-5052 auto-induction media supplemented with sodium myristate (100-200 microM). With optimized growth conditions and a high capacity metal affinity purification scheme, >50mg of homogenous myristoylated NCS-1 is obtained from 1L of culture in a single step. The properties of the C-terminally tagged NCS-1 variants are indistinguishable from those reported for untagged NCS-1. Using this system, we have also isolated and characterized mutant NCS-1 proteins that have attenuated (NCS-1 E120Q) and abrogated (NCS-1 DeltaEF) ability to bind calcium. The large quantities of NCS-1 proteins isolated from small culture volumes of auto-inducible media will provide the necessary reagents for further biochemical and structural characterization. The affinity tag at the C-terminus of the protein provides a suitable reagent for easily identifying binding partners of the various NCS-1 constructs. Additionally, this method could be used to produce other recombinant proteins of the NCS family, and may be extended to express and isolate myristoylated variants of other proteins.

CS23D: a web server for rapid protein structure generation using NMR chemical shifts and sequence data

CS23D (chemical shift to 3D structure) is a web server for rapidly generating accurate 3D protein structures using only assigned nuclear magnetic resonance (NMR) chemical shifts and sequence data as input. Unlike conventional NMR methods, CS23D requires no NOE and/or J-coupling data to perform its calculations. CS23D accepts chemical shift files in either SHIFTY or BMRB formats, and produces a set of PDB coordinates for the protein in about 10-15 min. CS23D uses a pipeline of several preexisting programs or servers to calculate the actual protein structure. Depending on the sequence similarity (or lack thereof) CS23D uses either (i) maximal subfragment assembly (a form of homology modeling), (ii) chemical shift threading or (iii) shift-aided de novo structure prediction (via Rosetta) followed by chemical shift refinement to generate and/or refine protein coordinates. Tests conducted on more than 100 proteins from the BioMagResBank indicate that CS23D converges (i.e. finds a solution) for >95% of protein queries. These chemical shift generated structures were found to be within 0.2-2.8 A RMSD of the NMR structure generated using conventional NOE-base NMR methods or conventional X-ray methods. The performance of CS23D is dependent on the completeness of the chemical shift assignments and the similarity of the query protein to known 3D folds. CS23D is accessible at http://www.cs23d.ca.

Structural insights on the pamoic acid and the 8 kDa domain of DNA polymerase beta complex: towards the design of higher-affinity inhibitors

BACKGROUND

DNA polymerase beta (pol beta), the error-prone DNA polymerase of single-stranded DNA break repair as well as base excision repair pathways, is overexpressed in several tumors and takes part in chemotherapeutic agent resistance, like that of cisplatin, through translesion synthesis. For this reason pol beta has become a therapeutic target. Several inhibitors have been identified, but none of them presents a sufficient affinity and specificity to become a drug. The fragment-based inhibitor design allows an important improvement in affinity of small molecules. The initial and critical step for setting up the fragment-based strategy consists in the identification and structural characterization of the first fragment bound to the target.

RESULTS

We have performed docking studies of pamoic acid, a 9 micromolar pol beta inhibitor, and found that it binds in a single pocket at the surface of the 8 kDa domain of pol beta. However, docking studies provided five possible conformations for pamoic acid in this site. NMR experiments were performed on the complex to select a single conformation among the five retained. Chemical Shift Mapping data confirmed pamoic acid binding site found by docking while NOESY and saturation transfer experiments provided distances between pairs of protons from the pamoic acid and those of the 8 kDa domain that allowed the identification of the correct conformation.

CONCLUSION

Combining NMR experiments on the complex with docking results allowed us to build a three-dimensional structural model. This model serves as the starting point for further structural studies aimed at improving the affinity of pamoic acid for binding to DNA polymerase beta.

BioMagResBank (BMRB) as a partner in the Worldwide Protein Data Bank (wwPDB): new policies affecting biomolecular NMR depositions

We describe the role of the BioMagResBank (BMRB) within the Worldwide Protein Data Bank (wwPDB) and recent policies affecting the deposition of biomolecular NMR data. All PDB depositions of structures based on NMR data must now be accompanied by experimental restraints. A scheme has been devised that allows depositors to specify a representative structure and to define residues within that structure found experimentally to be largely unstructured. The BMRB now accepts coordinate sets representing three-dimensional structural models based on experimental NMR data of molecules of biological interest that fall outside the guidelines of the Protein Data Bank (i.e., the molecule is a peptide with 23 or fewer residues, a polynucleotide with 3 or fewer residues, a polysaccharide with 3 or fewer sugar residues, or a natural product), provided that the coordinates are accompanied by representation of the covalent structure of the molecule (atom connectivity), assigned NMR chemical shifts, and the structural restraints used in generating model. The BMRB now contains an archive of NMR data for metabolites and other small molecules found in biological systems.

Analyzing the flexibility of RNA structures by constraint counting

RNA requires conformational dynamics to undergo its diverse functional roles. Here, a new topological network representation of RNA structures is presented that allows analyzing RNA flexibility/rigidity based on constraint counting. The method extends the FIRST approach, which identifies flexible and rigid regions in atomic detail in a single, static, three-dimensional molecular framework. Initially, the network rigidity of a canonical A-form RNA is analyzed by counting on constraints of network elements of increasing size. These considerations demonstrate that it is the inclusion of hydrophobic contacts into the RNA topological network that is crucial for an accurate flexibility prediction. The counting also explains why a protein-based parameterization results in overly rigid RNA structures. The new network representation is then validated on a tRNA(ASP) structure and all NMR-derived ensembles of RNA structures currently available in the Protein Data Bank (with chain length >/=40). The flexibility predictions demonstrate good agreement with experimental mobility data, and the results are superior compared to predictions based on two previously used network representations. Encouragingly, this holds for flexibility predictions as well as mobility predictions obtained by constrained geometric simulations on these networks. Potential applications of the approach to analyzing the flexibility of DNA and RNA/protein complexes are discussed.