Use of residual dipolar couplings as restraints inab initio protein structure prediction

Residue proximity information and protein model discrimination using saturation-suppressor mutagenesis

Identification of residue-residue contacts from primary sequence can be used to guide protein structure prediction. Using Escherichia coli CcdB as the test case, we describe an experimental method termed saturation-suppressor mutagenesis to acquire residue contact information. In this methodology, for each of five inactive CcdB mutants, exhaustive screens for suppressors were performed. Proximal suppressors were accurately discriminated from distal suppressors based on their phenotypes when present as single mutants. Experimentally identified putative proximal pairs formed spatial constraints to recover >98% of native-like models of CcdB from a decoy dataset. Suppressor methodology was also applied to the integral membrane protein, diacylglycerol kinase A where the structures determined by X-ray crystallography and NMR were significantly different. Suppressor as well as sequence co-variation data clearly point to the X-ray structure being the functional one adopted in vivo. The methodology is applicable to any macromolecular system for which a convenient phenotypic assay exists.

DOI:http://dx.doi.org/10.7554/eLife.09532.001

Common techniques to determine the three-dimensional structures of proteins can help researchers to understand these molecules’ activities, but are often time-consuming and do not work for all proteins. Proteins are made of chains of amino acids. When a protein chain folds, some of these amino acids interact with other amino acids and these contacts dictate the overall shape of the protein. This means that identifying the pairs of contacting amino acids could make it possible to predict the protein’s structure.

Interactions between pairs of contacting amino acids tend to remain conserved throughout evolution, and if a mutation alters one of the amino acids in a pair then a 'compensatory' change often occurs to alter the second amino acid as well. Compensatory mutations can suggest that two amino acids are close to each other in the three-dimensional shape of a protein, but the computational methods used to identify such amino acid pairs can sometimes be inaccurate.

In 2012, researchers generated mutants of a bacterial protein called CcdB with changes to single amino acids that caused the protein to fail to fold correctly. Now, Sahoo et al. – who include two of the researchers involved in the 2012 work – have developed an experimental method to identify contacting amino acids and use the CcdB protein as a test case. The approach involved searching for additional mutations that could restore the activity of five of the original mutant proteins when the proteins were produced in yeast cells. The rationale was that any secondary mutations that restored the activity must have corrected the folding defect caused by the original mutation. Sahoo et al. then predicted how close the amino acids affected by the secondary mutations were to the amino acids altered by the original mutations. This information was used to select reliable three-dimensional models of CcdB from a large set of possible structures that had been generated previously using computer models.

Next, the technique was applied to a protein called diacylglycerol kinase A. The structure of this protein had previously been inferred using techniques such as X-ray crystallography and nuclear magnetic resonance, but there was a mismatch between the two methods. Sahoo et al. found that the amino acid contacts derived from their experimental method matched those found in the crystal structure, suggesting that the functional protein structure in living cells is similar to the crystal structure. In the future, the experimental approach developed in this work could be combined with existing methods to reliably guide protein structure prediction.

DOI:http://dx.doi.org/10.7554/eLife.09532.002

Modeling helical proteins using residual dipolar couplings, sparse long-range distance constraints and a simple residue-based force field

We present a fast and simple protocol to obtain moderate-resolution backbone structures of helical proteins. This approach utilizes a combination of sparse backbone NMR data (residual dipolar couplings and paramagnetic relaxation enhancements) or EPR data with a residue-based force field and Monte Carlo/simulated annealing protocol to explore the folding energy landscape of helical proteins. By using only backbone NMR data, which are relatively easy to collect and analyze, and strategically placed spin relaxation probes, we show that it is possible to obtain protein structures with correct helical topology and backbone RMS deviations well below 4 Å. This approach offers promising alternatives for the structural determination of proteins in which nuclear Overha-user effect data are difficult or impossible to assign and produces initial models that will speed up the high-resolution structure determination by NMR spectroscopy.

Parallel tempering: theory, applications, and new perspectives

We review the history of the parallel tempering simulation method. From its origins in data analysis, the parallel tempering method has become a standard workhorse of physicochemical simulations. We discuss the theory behind the method and its various generalizations. We mention a selected set of the many applications that have become possible with the introduction of parallel tempering, and we suggest several promising avenues for future research.

Structure determination of a new protein from backbone-centered NMR data and NMR-assisted structure prediction

Targeting of proteins for structure determination in structural genomic programs often includes the use of threading and fold recognition methods to exclude proteins belonging to well-populated fold families, but such methods can still fail to recognize preexisting folds. The authors illustrate here a method in which limited amounts of structural data are used to improve an initial homology search and the data are subsequently used to produce a structure by data-constrained refinement of an identified structural template. The data used are primarily NMR-based residual dipolar couplings, but they also include additional chemical shift and backbone-nuclear Overhauser effect data. Using this methodology, a backbone structure was efficiently produced for a 10 kDa protein (PF1455) from Pyrococcus furiosus. Its relationship to existing structures and its probable function are discussed.

A general method for the unbiased improvement of solution NMR structures by the use of related X-ray data, the AUREMOL-ISIC algorithm

Background

Rapid and accurate three-dimensional structure determination of biological macromolecules is mandatory to keep up with the vast progress made in the identification of primary sequence information. During the last few years the amount of data deposited in the protein data bank has substantially increased providing additional information for novel structure determination projects. The key question is how to combine the available database information with the experimental data of the current project ensuring that only relevant information is used and a correct structural bias is produced. For this purpose a novel fully automated algorithm based on Bayesian reasoning has been developed. It allows the combination of structural information from different sources in a consistent way to obtain high quality structures with a limited set of experimental data. The new ISIC (Intelligent Structural Information Combination) algorithm is part of the larger AUREMOL software package.

Results

Our new approach was successfully tested on the improvement of the solution NMR structures of the Ras-binding domain of Byr2 from Schizosaccharomyces pombe, the Ras-binding domain of RalGDS from human calculated from a limited set of NMR data, and the immunoglobulin binding domain from protein G from Streptococcus by their corresponding X-ray structures. In all test cases clearly improved structures were obtained. The largest danger in using data from other sources is a possible bias towards the added structure. In the worst case instead of a refined target structure the structure from the additional source is essentially reproduced. We could clearly show that the ISIC algorithm treats these difficulties properly.

Conclusion

In summary, we present a novel fully automated method to combine strongly coupled knowledge from different sources. The combination with validation tools such as the calculation of NMR R-factors strengthens the impact of the method considerably since the improvement of the structures can be assessed quantitatively. The ISIC method can be applied to a large number of similar problems where the quality of the obtained three-dimensional structures is limited by the available experimental data like the improvement of large NMR structures calculated from sparse experimental data or the refinement of low resolution X-ray structures. Also structures may be refined using other available structural information such as homology models.

Determination of protein backbone structures from residual dipolar couplings

There are a number of circumstances in which a focus on determination of the backbone structure of a protein, as opposed to a complete all-atom structure, may be appropriate. This is particularly the case for structures determined as a part of a structural genomics initiative in which computational modeling of many sequentially related structures from the backbone of a single family representative is anticipated. It is, however, also the case when the backbone may be a stepping-stone to more targeted studies of ligand interaction or protein-protein interaction. Here an NMR protocol is described that can produce a backbone structure of a protein without the need for extensive experiments directed at side chain resonance assignment or the collection of structural information on side chains. The procedure relies primarily on orientational constraints from residual dipolar couplings as opposed to distance constraints from NOEs. Procedures for sample preparation, data acquisition, and data analysis are described, along with examples from application to small target proteins of a structural genomics project.

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 (15)N-(1)H 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 (15)N-(1)H 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.