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

De novo structure generation using chemical shifts for proteins with high-sequence identity but different folds

Proteins with high-sequence identity but very different folds present a special challenge to sequence-based protein structure prediction methods. In particular, a 56-residue three-helical bundle protein (GA(95)) and an alpha/beta-fold protein (GB(95)), which share 95% sequence identity, were targets in the CASP-8 structure prediction contest. With only 12 out of 300 submitted server-CASP8 models for GA(95) exhibiting the correct fold, this protein proved particularly challenging despite its small size. Here, we demonstrate that the information contained in NMR chemical shifts can readily be exploited by the CS-Rosetta structure prediction program and yields adequate convergence, even when input chemical shifts are limited to just amide (1)H(N) and (15)N or (1)H(N) and (1)H(alpha) values.

Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction

Local structures predicted from protein sequences are used extensively in every aspect of modeling and prediction of protein structure and function. For more than 50 years, they have been predicted at a low-resolution coarse-grained level (e.g., three-state secondary structure). Here, we combine a two-state classifier with real-value predictor to predict local structure in continuous representation by backbone torsion angles. The accuracy of the angles predicted by this approach is close to that derived from NMR chemical shifts. Their substitution for predicted secondary structure as restraints for ab initio structure prediction doubles the success rate. This result demonstrates the potential of predicted local structure for fragment-free tertiary-structure prediction. It further implies potentially significant benefits from using predicted real-valued torsion angles as a replacement for or supplement to the secondary-structure prediction tools used almost exclusively in many computational methods ranging from sequence alignment to function prediction.

Prediction of Xaa-Pro peptide bond conformation from sequence and chemical shifts

We present a program, named Promega, to predict the Xaa-Pro peptide bond conformation on the basis of backbone chemical shifts and the amino acid sequence. Using a chemical shift database of proteins of known structure together with the PDB-extracted amino acid preference of cis Xaa-Pro peptide bonds, a cis/trans probability score is calculated from the backbone and (13)C(beta) chemical shifts of the proline and its neighboring residues. For an arbitrary number of input chemical shifts, which may include Pro-(13)C(gamma), Promega calculates the statistical probability that a Xaa-Pro peptide bond is cis. Besides its potential as a validation tool, Promega is particularly useful for studies of larger proteins where Pro-(13)C(gamma) assignments can be challenging, and for on-going efforts to determine protein structures exclusively on the basis of backbone and (13)C(beta) chemical shifts.

Structural and thermodynamic characterization of Vibrio fischeri CcdB

CcdB(Vfi) from Vibrio fischeri is a member of the CcdB family of toxins that poison covalent gyrase-DNA complexes. In solution CcdB(Vfi) is a dimer that unfolds to the corresponding monomeric components in a two-state fashion. In the unfolded state, the monomer retains a partial secondary structure. This observation correlates well with the crystal and NMR structures of the protein, which show a dimer with a hydrophobic core crossing the dimer interface. In contrast to its F plasmid homologue, CcdB(Vfi) possesses a rigid dimer interface, and the apparent relative rotations of the two subunits are due to structural plasticity of the monomer. CcdB(Vfi) shows a number of non-conservative substitutions compared with the F plasmid protein in both the CcdA and the gyrase binding sites. Although variation in the CcdA interaction site likely determines toxin-antitoxin specificity, substitutions in the gyrase-interacting region may have more profound functional implications.

DANGLE: A Bayesian inferential method for predicting protein backbone dihedral angles and secondary structure

This paper introduces DANGLE, a new algorithm that employs Bayesian inference to estimate the likelihood of all possible values of the backbone dihedral angles phi and psi for each residue in a query protein, based on observed chemical shifts and the conformational preferences of each amino acid type. The method provides robust estimates of phi and psi within realistic boundary ranges, an indication of the degeneracy in the relationship between shift measurements and conformation at each site, and faithful secondary structure state assignments. When a simple degeneracy-based filtering procedure is applied, DANGLE offers an ideal compromise between accuracy and coverage when compared with other shift-based dihedral angle prediction methods. In addition, per residue analysis of shift/structure degeneracy has potential to be a useful new approach for studying the properties of unfolded proteins, with sufficient sensitivity to identify regions of residual structure in the acid denatured state of apomyoglobin.

DNA structures from phosphate chemical shifts

For B-DNA, the strong linear correlation observed by nuclear magnetic resonance (NMR) between the (31)P chemical shifts (deltaP) and three recurrent internucleotide distances demonstrates the tight coupling between phosphate motions and helicoidal parameters. It allows to translate deltaP into distance restraints directly exploitable in structural refinement. It even provides a new method for refining DNA oligomers with restraints exclusively inferred from deltaP. Combined with molecular dynamics in explicit solvent, these restraints lead to a structural and dynamical view of the DNA as detailed as that obtained with conventional and more extensive restraints. Tests with the Jun-Fos oligomer show that this deltaP-based strategy can provide a simple and straightforward method to capture DNA properties in solution, from routine NMR experiments on unlabeled samples.

4D prediction of protein (1)H chemical shifts

A 4D approach for protein (1)H chemical shift prediction was explored. The 4th dimension is the molecular flexibility, mapped using molecular dynamics simulations. The chemical shifts were predicted with a principal component model based on atom coordinates from a database of 40 protein structures. When compared to the corresponding non-dynamic (3D) model, the 4th dimension improved prediction by 6-7%. The prediction method achieved RMS errors of 0.29 and 0.50 ppm for Halpha and HN shifts, respectively. However, for individual proteins the RMS errors were 0.17-0.34 and 0.34-0.65 ppm for the Halpha and HN shifts, respectively. X-ray structures gave better predictions than the corresponding NMR structures, indicating that chemical shifts contain invaluable information about local structures. The (1)H chemical shift prediction tool 4DSPOT is available from http://www.uku.fi/kemia/4dspot .

TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts

NMR chemical shifts in proteins depend strongly on local structure. The program TALOS establishes an empirical relation between 13C, 15N and 1H chemical shifts and backbone torsion angles phi and psi (Cornilescu et al. J Biomol NMR 13 289-302, 1999). Extension of the original 20-protein database to 200 proteins increased the fraction of residues for which backbone angles could be predicted from 65 to 74%, while reducing the error rate from 3 to 2.5%. Addition of a two-layer neural network filter to the database fragment selection process forms the basis for a new program, TALOS+, which further enhances the prediction rate to 88.5%, without increasing the error rate. Excluding the 2.5% of residues for which TALOS+ makes predictions that strongly differ from those observed in the crystalline state, the accuracy of predicted phi and psi angles, equals +/-13 degrees . Large discrepancies between predictions and crystal structures are primarily limited to loop regions, and for the few cases where multiple X-ray structures are available such residues are often found in different states in the different structures. The TALOS+ output includes predictions for individual residues with missing chemical shifts, and the neural network component of the program also predicts secondary structure with good accuracy.

A progesterone receptor co-activator (JDP2) mediates activity through interaction with residues in the carboxyl-terminal extension of the DNA binding domain

Progesterone receptor (PR) belongs to the nuclear receptor family of ligand-dependent transcription factors and mediates the major biological effects of progesterone. Transcriptional co-activators that are recruited by PR through the carboxyl-terminal ligand binding domain have been studied extensively. Much less is known about co-activators that interact with other regions of receptors. Jun dimerization protein 2 (JDP2) is a PR co-activator that enhances the transcriptional activity of the amino-terminal domain by increasing the alpha-helical content and stability of the intrinsically disordered amino-terminal domain. To gain insights into the mechanism of JDP2 co-activation of PR, the structural basis of JDP2-PR interaction was analyzed using NMR. The smallest regions of each protein needed for efficient protein interaction were used for NMR and included the basic region plus leucine zipper (bZIP) domain of JDP2 and the core zinc modules of the PR DNA binding domain plus the intrinsically disordered carboxyl-terminal extension (CTE) of the DNA binding domain. Chemical shift changes in PR upon titration with JDP2 revealed that most of the residues involved in binding of JDP2 reside within the CTE. The importance of the CTE for binding JDP2 was confirmed by peptide competition and mutational analyses. Point mutations within CTE sites identified by NMR and a CTE domain swapping experiment also confirmed the functional importance of JDP2 interaction with the CTE for enhancement of PR transcriptional activity. These studies provide insights into the role and functional importance of the CTE for co-activator interactions.

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.

Synergy of NMR, computation, and X-ray crystallography for structural biology

NMR spectroscopy and X-ray crystallography are currently the two most widely applied methods for the determination of macromolecular structures at high resolution. More recently, significant advances have been made in algorithms for the de novo prediction of protein structure, and, in favorable cases, the predicted models agree extremely well with experimentally determined structures. Here, we demonstrate a synergistic combination of NMR spectroscopy, de novo structure prediction, and X-ray crystallography in an effective overall strategy for rapidly determining the structure of the coat protein C-terminal domain from the Sulfolobus islandicus rod-shaped virus (SIRV). This approach takes advantage of the most accessible aspects of each structural technique and may be widely applicable for structure determination.

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.

Determination of protein structures in the solid state from NMR chemical shifts

Solid-state NMR spectroscopy does not require proteins to form crystalline or soluble samples and can thus be applied under a variety of conditions, including precipitates, gels, and microcrystals. It has recently been shown that NMR chemical shifts can be used to determine the structures of the native states of proteins in solution. By considering the cases of two proteins, GB1 and SH3, we provide an initial demonstration here that this type of approach can be extended to the use of solid-state NMR chemical shifts to obtain protein structures in the solid state without the need for measuring interatomic distances.

De novo protein structure generation from incomplete chemical shift assignments

NMR chemical shifts provide important local structural information for proteins. Consistent structure generation from NMR chemical shift data has recently become feasible for proteins with sizes of up to 130 residues, and such structures are of a quality comparable to those obtained with the standard NMR protocol. This study investigates the influence of the completeness of chemical shift assignments on structures generated from chemical shifts. The Chemical-Shift-Rosetta (CS-Rosetta) protocol was used for de novo protein structure generation with various degrees of completeness of the chemical shift assignment, simulated by omission of entries in the experimental chemical shift data previously used for the initial demonstration of the CS-Rosetta approach. In addition, a new CS-Rosetta protocol is described that improves robustness of the method for proteins with missing or erroneous NMR chemical shift input data. This strategy, which uses traditional Rosetta for pre-filtering of the fragment selection process, is demonstrated for two paramagnetic proteins and also for two proteins with solid-state NMR chemical shift assignments.