Finding rigid bodies in protein structures: Application to flexible fitting into cryoEM maps

We present RIBFIND, a method for detecting flexibility in protein structures via the clustering of secondary structural elements (SSEs) into rigid bodies. To test the usefulness of the method in refining atomic structures within cryoEM density we incorporated it into our flexible fitting protocol (Flex-EM). Our benchmark includes 13 pairs of protein structures in two conformations each, one of which is represented by a corresponding cryoEM map. Refining the structures in simulated and experimental maps at the 5–15 Å resolution range using rigid bodies identified by RIBFIND shows a significant improvement over using individual SSEs as rigid bodies. For the 15 Å resolution simulated maps, using RIBFIND-based rigid bodies improves the initial fits by 40.64% on average, as compared to 26.52% when using individual SSEs. Furthermore, for some test cases we show that at the sub-nanometer resolution range the fits can be further improved by applying a two-stage refinement protocol (using RIBFIND-based refinement followed by an SSE-based refinement). The method is stand-alone and could serve as a general interactive tool for guiding flexible fitting into EM maps.     

Binding mode prediction for a flexible ligand in a flexible pocket using multi-conformation simulated annealing pseudo crystallographic refinement

We describe multi-conformation simulated annealing-pseudo-crystallographic refinement (MCSA-PCR), a technique developed for predicting the binding mode of a flexible ligand in a flexible binding pocket. To circumvent the local-minimum problem efficiently, this method performs multiple independent cycles of simulated annealing with explicit solvent, "growing" the ligand in the binding pocket each time. From the ensemble of structures, a pseudo-crystallographic electron density map is calculated, and then conventional crystallographic refinement methods are used to best fit a single, optimal structure into the density map. The advantage of the MCSA-PCR method is that it provides a direct means to evaluate the accuracy and uniqueness of the calculated solution, provides a measure of ligand and protein dynamics from the refined B-factors, and facilitates comparison with X-ray crystallographic data. Here, we show that our MCSA-PCR method succeeds in predicting the correct binding mode of the VSV8 peptide to the major histocompatibility complex (MHC) receptor. Importantly, there is a significant correlation between the experimentally determined crystallographic water molecules and water density observed in the pseudo map by MCSA-PCR. Furthermore, comparison of different approaches for extracting a single, most probable structure from the calculated ensemble reveals the power of the PCR method and provides insights into the nature of the energetic landscape.     

Allostery in Coagulation Factor VIIa Revealed by Ensemble Refinement of Crystallographic Structures

A critical step in injury-induced initiation of blood coagulation is the formation of the complex between the trypsin-like protease coagulation factor VIIa (FVIIa) and its cofactor tissue factor (TF), which converts FVIIa from an intrinsically poor enzyme to an active protease capable of activating zymogens of downstream coagulation proteases. Unlike its constitutively active ancestor trypsin, FVIIa is allosterically activated (by TF). Here, ensemble refinement of crystallographic structures, which uses multiple copies of the entire structure as a means of representing structural flexibility, is applied to explore the impacts of inhibitor binding to trypsin and FVIIa, as well as cofactor binding to FVIIa. To assess the conformational flexibility and its role in allosteric pathways in these proteases, main-chain hydrogen bond networks are analyzed by calculating the hydrogen-bond propensity. Mapping pairwise propensity differences between relevant structures shows that binding of the inhibitor benzamidine to trypsin has a minor influence on the protease flexibility. For FVIIa, in contrast, the protease domain is “locked” into the catalytically competent trypsin-like configuration upon benzamidine binding as indicated by the stabilization of key structural features: the nonprime binding cleft and the oxyanion hole are stabilized, and the effect propagates from the active site region to the calcium-binding site and to the vicinity of the disulphide bridge connecting with the light chain. TF binding to FVIIa furthermore results in stabilization of the 170 loop, which in turn propagates an allosteric signal from the TF-binding region to the active site. Analyses of disulphide bridge energy and flexibility reflect the striking stability difference between the unregulated enzyme and the allosterically activated form after inhibitor or cofactor binding. The ensemble refinement analyses show directly, for the first time to our knowledge, whole-domain structural footprints of TF-induced allosteric networks present in x-ray crystallographic structures of FVIIa, which previously only have been hypothesized or indirectly inferred.     

Validation of X-ray Crystal Structure Ensemble Representations of SARS-CoV-2 Main Protease by Solution NMR Residual Dipolar Couplings

Considerable debate has focused on whether sampling of molecular dynamics trajectories restrained by crystallographic data can be used to develop realistic ensemble models for proteins in their natural, solution state. For the SARS-CoV-2 main protease, M^pro, we evaluated agreement between solution residual dipolar couplings (RDCs) and various recently reported multi-conformer and dynamic-ensemble crystallographic models. Although Phenix-derived ensemble models showed only small improvements in crystallographic R_free, substantially improved RDC agreement over fits to a conventionally refined 1.2-Å X-ray structure was observed, in particular for residues with above average disorder in the ensemble. For a set of six lower resolution (1.55–2.19 Å) M^pro X-ray ensembles, obtained at temperatures ranging from 100 to 310 K, no significant improvement over conventional two-conformer representations was found. At the residue level, large differences in motions were observed among these ensembles, suggesting high uncertainties in the X-ray derived dynamics. Indeed, combining the six ensembles from the temperature series with the two 1.2-Å X-ray ensembles into a single 381-member “super ensemble” averaged these uncertainties and substantially improved agreement with RDCs. However, all ensembles showed excursions that were too large for the most dynamic fraction of residues. Our results suggest that further improvements to X-ray ensemble refinement are feasible, and that RDCs provide a sensitive benchmark in such endeavors. Remarkably, a weighted ensemble of 350 PDB M^pro X-ray structures provided slightly better cross-validated agreement with RDCs than any individual ensemble refinement, implying that differences in lattice confinement also limit the fit of RDCs to X-ray coordinates.     

Refinement of the influenza virus hemagglutinin by simulated annealing

We have applied the method of simulated annealing to the refinement of the 3 A resolution crystal structure of the influenza virus hemagglutinin glycoprotein, using the program X-PLOR. Two different methods were introduced into X-PLOR to treat the non-crystallographic symmetry present in this and in other crystal structures. In the first, only the unique protomer atoms are refined; by application of the non-crystallographic symmetry operators to the protomer atoms, the X-ray structure factor derivatives are effectively averaged, and a non-bonded energy term models the interactions of the protomer with its neighbors in the oligomer without explicit refinement of the other protomers in the crystallographic asymmetric unit. In the second method, the entire asymmetric unit is refined, but an effective energy term is added to the empirical energy that restrains symmetry-related atomic positions to their average values after least-squares superposition. Several other modifications and additions were made to previously published X-PLOR protocols, including weighting of the X-ray terms, maintenance of the temperature of the molecular dynamics simulation, treatment of charged groups, changes in the values of certain empirical energy parameters, and the use of N-linked carbohydrate empirical energy parameters. The hemagglutinin refinement proceeded in several stages. An initial round of simulated annealing of the monomer was followed by rigid-body refinement of the 3-fold non-crystallographic symmetry axis position and a second round of monomer refinement. A third round was performed on the trimer using non-crystallographic symmetry restraints in all regions except those in lattice contacts showing obvious derivations from 3-fold symmetry. The refinement was completed with several rounds of conventional positional and isotropic temperature factor refinement needed to correct bad model geometry introduced by high-temperature molecular dynamics in regions of weak electron density. This structure was then used as the basis for refinement of three crystallographically isomorphous hemagglutinin structures, including complexes with the influenza virus receptor, sialic acid. Model geometry comparable to well-refined high-resolution structures was obtained with relatively little manual intervention, demonstrating the ability of simulated annealing refinement to produce highly idealized structures at moderate resolution.     

Sampling of the native conformational ensemble of myoglobin via structures in different crystalline environments

Proteins sample multiple conformational substates in their native environment, but the process of crystallization selects the conformers that allow for close packing. The population of conformers can be shifted by varying the environment through a range of crystallization conditions, often resulting in different space groups and changes in the packing arrangements. Three high resolution structures of myoglobin (Mb) in different crystal space groups are presented, including one in a new space group P6(1)22 and two structures in space groups P2(1)2(1)2(1) and P6. We compare coordinates and anisotropic displacement parameters (ADPs) from these three structures plus an existing structure in space group P2(1). While the overall changes are small, there is substantial variation in several external regions with varying patterns of crystal contacts across the space group packing arrangements. The structural ensemble containing four different crystal forms displays greater conformational variance (Calpha rmsd of 0.54-0.79 A) in comparison to a collection of four Mb structures with different ligands and mutations in the same crystal form (Calpha rmsd values of 0.28-0.37 A). The high resolution of the data enables comparison of both the magnitudes and directions of ADPs, which are found to be suppressed by crystal contacts. A composite dynamic profile of Mb structural variation from the four structures was compared with an independent structural ensemble developed from NMR refinement. Despite the limitations and biases of each method, the ADPs of the crystallographic ensemble closely match the positional variance from the solution NMR ensemble with linear correlation of 0.8. This suggests that crystal packing selects conformers representative of the solution ensemble, and several different crystal forms give a more complete view of the plasticity of a protein structure.     

COCO: a simple tool to enrich the representation of conformational variability in NMR structures

NMR structures are typically deposited in databases such as the PDB in the form of an ensemble of structures. Generally, each of the models in such an ensemble satisfies the experimental data and is equally valid. No unique solution can be calculated because the experimental NMR data is insufficient, in part because it reflects the conformational variability and dynamical behavior of the molecule in solution. Even for relatively rigid molecules, the limited number of structures that are typically deposited cannot completely encompass the structural diversity allowed by the observed NMR data, but they can be chosen to try and maximize its representation. We describe here the adaptation and application of techniques more commonly used to examine large ensembles from molecular dynamics simulations, to the analysis of NMR ensembles. The approach, which is based on principal component analysis, we call COCO ("Complementary Coordinates"). The COCO approach analyses the distribution of an NMR ensemble in conformational space, and generates a new ensemble that fills "gaps" in the distribution. The method is very rapid, and analysis of a 25-member ensemble and generation of a new 25 member ensemble typically takes 1-2 min on a conventional workstation. Applied to the 545 structures in the RECOORD database, we find that COCO generates new ensembles that are as structurally diverse-both from each other and from the original ensemble-as are the structures within the original ensemble. The COCO approach does not explicitly take into account the NMR restraint data, yet in tests on selected structures from the RECOORD database, the COCO ensembles are frequently good matches to this data, and certainly are structures that can be rapidly refined against the restraints to yield high-quality, novel solutions. COCO should therefore be a useful aid in NMR structure refinement and in other situations where a richer representation of conformational variability is desired-for example in docking studies. COCO is freely accessible via the website www.ccpb.ac.uk/COCO.     

Can local energy minimization refine the PDB structures of different resolution universally?

Local energy minimization was statistically tested as the refinement strategy for PDB structure pairs of different resolution. The 13 pairs of structures with the only difference being the resolution were extracted from PDB and represented structures of 11 identical proteins obtained with different x-ray diffraction techniques. The rmsd distribution was calculated for these pairs before and after local energy minimization of each structure. MMFF94 was used for energy calculations and the quasi-Newtonian method was used for local energy minimization. By comparison of these two rmsd distributions, the local energy minimization was proved to statistically increase the structural differences in pairs, so it cannot be used for refinement purposes. To explore the prospects of complex refinement strategies based on energy minimization, randomized structures were obtained by moving the initial PDB structures as far as the minimized structures had been moved in the multidimensional space of atomic coordinates. For these randomized structures the rmsd distribution was calculated and compared with the one for minimized structures. The significant differences in their mean values proved the energy surface of the protein to have only few minima near the conformations of different resolution obtained by x-ray analysis for PDB. Some other results we obtained exploring the energy surface near these conformations are also presented. These results are expected to be useful for the development of new protein refinement strategies based on energy minimization.     

Evaluating the quality of NMR structures by local density of protons

Evaluating the quality of experimentally determined protein structural models is an essential step toward identifying potential errors and guiding further structural refinement. Herein, we report the use of proton local density as a sensitive measure to assess the quality of nuclear magnetic resonance (NMR) structures. Using 256 high-resolution crystal structures with protons added and optimized, we show that the local density of different proton types display distinct distributions. These distributions can be characterized by statistical moments and are used to establish local density Z-scores for evaluating both global and local packing for individual protons. Analysis of 546 crystal structures at various resolutions shows that the local density Z-scores increase as the structural resolution decreases and correlate well with the ClashScore (Word et al. J Mol Biol 1999;285(4):1711-1733) generated by all atom contact analysis. Local density Z-scores for NMR structures exhibit a significantly wider range of values than for X-ray structures and demonstrate a combination of potentially problematic inflation and compression. Water-refined NMR structures show improved packing quality. Our analysis of a high-quality structural ensemble of ubiquitin refined against order parameters shows proton density distributions that correlate nearly perfectly with our standards derived from crystal structures, further validating our approach. We present an automated analysis and visualization tool for proton packing to evaluate the quality of NMR structures.     

Flexibility observed in high resolution structures of <Emphasis Type="Italic">Streptomyces aureofaciens</Emphasis> ribonucleases determined by diffraction methods

It has been widely accepted to distinguish between static structures determined by diffraction methods and dynamic structures determined by nuclear magnetic resonance (NMR). The dynamics of NMR structures is demonstrated by an ensemble of a number of overlaid structures. This cannot be seen in one structure determined by diffraction methods. However, it is possible to see the flexibility of a protein molecule in a number of structures of the same protein determined by X-ray techniques which is manifested by different conformations of main-chain. Multiple protein structure determination does not provide identical structures as a result of various factors including flexibility. Overlap of structures of a protein determined at atomic resolution with high accuracy shows that the root-mean-square deviations (rmsd) of main-chain atoms exceed several fold the accuracy of the positional parameters of each structure. Overlap of a number of structures of a protein determined by diffraction methods shows a similar distribution as that determined by NMR. These observations are demonstrated using high resolution structures of Streptomyces aureofaciens ribonucleases, their mutants and complexes with ligands.     

Selecting High Quality Protein Structures from Diverse Conformational Ensembles

Protein structure prediction encompasses two major challenges: 1), the generation of a large ensemble of high resolution structures for a given amino-acid sequence; and 2), the identification of the structure closest to the native structure for a blind prediction. In this article, we address the second challenge, by proposing what is, to our knowledge, a novel iterative traveling-salesman problem-based clustering method to identify the structures of a protein, in a given ensemble, which are closest to the native structure. The method consists of an iterative procedure, which aims at eliminating clusters of structures at each iteration, which are unlikely to be of similar fold to the native, based on a statistical analysis of cluster density and average spherical radius. The method, denoted as ICON, has been tested on four data sets: 1), 1400 proteins with high resolution decoys; 2), medium-to-low resolution decoys from Decoys ‘R’ Us; 3), medium-to-low resolution decoys from the first-principles approach, ASTRO-FOLD; and 4), selected targets from CASP8. The extensive tests demonstrate that ICON can identify high-quality structures in each ensemble, regardless of the resolution of conformers. In a total of 1454 proteins, with an average of 1051 conformers per protein, the conformers selected by ICON are, on an average, in the top 3.5% of the conformers in the ensemble.     

Principles of flexible protein-protein docking

Treating flexibility in molecular docking is a major challenge in cell biology research. Here we describe the background and the principles of existing flexible protein-protein docking methods, focusing on the algorithms and their rational. We describe how protein flexibility is treated in different stages of the docking process: in the preprocessing stage, rigid and flexible parts are identified and their possible conformations are modeled. This preprocessing provides information for the subsequent docking and refinement stages. In the docking stage, an ensemble of pre-generated conformations or the identified rigid domains may be docked separately. In the refinement stage, small-scale movements of the backbone and side-chains are modeled and the binding orientation is improved by rigid-body adjustments. For clarity of presentation, we divide the different methods into categories. This should allow the reader to focus on the most suitable method for a particular docking problem.     

Novel recombinant insulin analogue with flexible C-terminus in B chain. NMR structure of biosynthetic engineered A22G-B31K-B32R human insulin monomer in water/acetonitrile solution

A tertiary structure of recombinant A22^G-B31^K-B32^R-human insulin monomer (insulin GKR) has been characterized by ¹H, ¹³C NMR at natural isotopic abundance using NOESY, TOCSY, ¹H/¹³C-GHSQC, and ¹H/¹³C-GHSQC-TOCSY spectra. Translational diffusion studies indicate the monomer structure in water/acetonitrile (65/35 vol.%). CSI analysis confirms existence of secondary structure motifs present in human insulin standard (HIS). Both techniques allow to establish that in this solvent recombinant insulin GKR exists as a monomer. Starting from structures calculated by the program CYANA, two different refinement protocols used molecular dynamics simulated annealing with the program AMBER; in vacuum (AMBER_VC), and including a generalized Born solvent model (AMBER_GB). From these calculations an ensemble of 20 structures of lowest energy was chosen which represents the tertiary structure of studied insulin. Here we present novel insulin with added A22^G amino acid which interacts with β-turn environment resulting in high flexibility of B chain C-terminus.     

Protein NMR Structures Refined without NOE Data

The refinement of low-quality structures is an important challenge in protein structure prediction. Many studies have been conducted on protein structure refinement; the refinement of structures derived from NMR spectroscopy has been especially intensively studied. In this study, we generated flat-bottom distance potential instead of NOE data because NOE data have ambiguity and uncertainty. The potential was derived from distance information from given structures and prevented structural dislocation during the refinement process. A simulated annealing protocol was used to minimize the potential energy of the structure. The protocol was tested on 134 NMR structures in the Protein Data Bank (PDB) that also have X-ray structures. Among them, 50 structures were used as a training set to find the optimal “width” parameter in the flat-bottom distance potential functions. In the validation set (the other 84 structures), most of the 12 quality assessment scores of the refined structures were significantly improved (total score increased from 1.215 to 2.044). Moreover, the secondary structure similarity of the refined structure was improved over that of the original structure. Finally, we demonstrate that the combination of two energy potentials, statistical torsion angle potential (STAP) and the flat-bottom distance potential, can drive the refinement of NMR structures.     

Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking

One approach to incorporate protein flexibility in molecular docking is the use of an ensemble consisting of multiple protein structures. Sequentially docking each ligand into a large number of protein structures is computationally too expensive to allow large-scale database screening. It is challenging to achieve a good balance between docking accuracy and computational efficiency. In this work, we have developed a fast, novel docking algorithm utilizing multiple protein structures, referred to as ensemble docking, to account for protein structural variations. The algorithm can simultaneously dock a ligand into an ensemble of protein structures and automatically select an optimal protein structure that best fits the ligand by optimizing both ligand coordinates and the conformational variable m, where m represents the m-th structure in the protein ensemble. The docking algorithm was validated on 10 protein ensembles containing 105 crystal structures and 87 ligands in terms of binding mode and energy score predictions. A success rate of 93% was obtained with the criterion of root-mean-square deviation <2.5 A if the top five orientations for each ligand were considered, comparable to that of sequential docking in which scores for individual docking are merged into one list by re-ranking, and significantly better than that of single rigid-receptor docking (75% on average). Similar trends were also observed in binding score predictions and enrichment tests of virtual database screening. The ensemble docking algorithm is computationally efficient, with a computational time comparable to that for docking a ligand into a single protein structure. In contrast, the computational time for the sequential docking method increases linearly with the number of protein structures in the ensemble. The algorithm was further evaluated using a more realistic ensemble in which the corresponding bound protein structures of inhibitors were excluded. The results show that ensemble docking successfully predicts the binding modes of the inhibitors, and discriminates the inhibitors from a set of noninhibitors with similar chemical properties. Although multiple experimental structures were used in the present work, our algorithm can be easily applied to multiple protein conformations generated by computational methods, and helps improve the efficiency of other existing multiple protein structure(MPS)-based methods to accommodate protein flexibility.     

Exploration of disorder in protein structures by X-ray restrained molecular dynamics

Conformational disorder in crystal structures of ribonuclease-A and crambin is studied by including two independent structures in least-squares optimizations against X-ray data. The optimizations are carried out by X-ray restrained molecular dynamics (simulated annealing refinement) and by conventional least-squares optimization. Starting from two identical structures, the optimizations against X-ray data lead to significant deviations between the two, with rms backbone displacements of 0.45 A for refinement of ribonuclease at 1.53 A resolution, and 0.31 A for crambin at 0.945 A. More than 15 independent X-ray restrained molecular dynamics runs have been carried out for ribonuclease, and the displacements between the resulting structures are highly reproducible for most atoms. These include residues with two or more conformations with significant dihedral angle differences and alternative hydrogen bonding, as well as groups of residues that undergo displacements that are suggestive of rigid-body librations. The crystallographic R-values obtained are approximately 13%, as compared to 15.3% for a comparable refinement with a single structure. Least-squares optimization without an intervening restrained molecular dynamics stage is sufficient to reproduce most of the observed displacements. Similar results are obtained for crambin, where the higher resolution of the X-ray data allows for refinement of unconstrained individual anisotropic temperature factors. These are shown to be correlated with the displacements in the two-structure refinements.     

Biased coarse-grained molecular dynamics simulation approach for flexible fitting of X-ray structure into cryo electron microscopy maps

Several approaches have been introduced to interpret, in terms of high-resolution structure, low-resolution structural data as obtained from cryo-EM. As conformational changes are often observed in biological molecules, these techniques need to take into account the flexibility of proteins. Flexibility has been described in terms of movement between rigid domains and between rigid secondary structure elements, which present some limitations for studying dynamical properties. Normal mode analysis has also been used, but is limited to medium resolution data. All-atom molecular dynamics fitting techniques are more appropriate to fit structures into higher-resolution data as full protein flexibility is considered, but are cumbersome in terms of computational time. Here, we introduce a coarse-grained approach; a Go-model was used to represent biological molecules, combined with biased molecular dynamics to reproduce accurately conformational transitions. Illustrative examples on simulated data are shown. Accurate fittings can be obtained for resolution ranging from 5 to 20 Å. The approach was also tested on experimental data of Elongation Factor G and Escherichia coli RNA polymerase, where its validity is compared to previous models obtained from different techniques. This comparison demonstrates that quantitative flexible techniques, as opposed to manual docking, need to be considered to interpret low-resolution data.     

Efficient molecular docking of NMR structures: application to HIV-1 protease

Docking ligands into an ensemble of NMR conformers is essential to structure-based drug discovery if only NMR structures are available for the target. However, sequentially docking ligands into each NMR conformer through standard single-receptor-structure docking, referred to as sequential docking, is computationally expensive for large-scale database screening because of the large number of NMR conformers involved. Recently, we developed an efficient ensemble docking algorithm to consider protein structural variations in ligand binding. The algorithm simultaneously docks ligands into an ensemble of protein structures and achieves comparable performance to sequential docking without significant increase in computational time over single-structure docking. Here, we applied this algorithm to docking with NMR structures. The HIV-1 protease was used for validation in terms of docking accuracy and virtual screening. Ensemble docking of the NMR structures identified 91% of the known inhibitors under the criterion of RMSD < 2.0 A for the best-scored conformation, higher than the average success rate of single docking of individual crystal structures (66%). In the virtual screening test, on average, ensemble docking of the NMR structures obtained higher enrichments than single-structure docking of the crystal structures. In contrast, docking of either the NMR minimized average structure or a single NMR conformer performed less satisfactorily on both binding mode prediction and virtual screening, indicating that a single NMR structure may not be suitable for docking calculations. The success of ensemble docking of the NMR structures suggests an efficient alternative method for standard single docking of crystal structures and for considering protein flexibility.     

Resolution-by-proxy: a simple measure for assessing and comparing the overall quality of NMR protein structures

In protein X-ray crystallography, resolution is often used as a good indicator of structural quality. Diffraction resolution of protein crystals correlates well with the number of X-ray observables that are used in structure generation and, therefore, with protein coordinate errors. In protein NMR, there is no parameter identical to X-ray resolution. Instead, resolution is often used as a synonym of NMR model quality. Resolution of NMR structures is often deduced from ensemble precision, torsion angle normality and number of distance restraints per residue. The lack of common techniques to assess the resolution of X-ray and NMR structures complicates the comparison of structures solved by these two methods. This problem is sometimes approached by calculating "equivalent resolution" from structure quality metrics. However, existing protocols do not offer a comprehensive assessment of protein structure as they calculate equivalent resolution from a relatively small number (<5) of protein parameters. Here, we report a development of a protocol that calculates equivalent resolution from 25 measurable protein features. This new method offers better performance (correlation coefficient of 0.92, mean absolute error of 0.28 ?) than existing predictors of equivalent resolution. Because the method uses coordinate data as a proxy for X-ray diffraction data, we call this measure "Resolution-by-Proxy" or ResProx. We demonstrate that ResProx can be used to identify under-restrained, poorly refined or inaccurate NMR structures, and can discover structural defects that the other equivalent resolution methods cannot detect. The ResProx web server is available at http://www.resprox.ca.     

X‐ray vs. NMR structures as templates for computational protein design

Certain protein‐design calculations involve using an experimentally determined high‐resolution structure as a template to identify new sequences that can adopt the same fold. This approach has led to the successful design of many novel, well‐folded, native‐like proteins. Although any atomic‐resolution structure can serve as a template in such calculations, most successful designs have used high‐resolution crystal structures. Because there are many proteins for which crystal structures are not available, it is of interest whether nuclear magnetic resonance (NMR) templates are also appropriate. We have analyzed differences between using X‐ray and NMR templates in side‐chain repacking and design calculations. We assembled a database of 29 proteins for which both a high‐resolution X‐ray structure and an ensemble of NMR structures are available. Using these pairs, we compared the rotamericity, χ₁‐angle recovery, and native‐sequence recovery of X‐ray and NMR templates. We carried out design using RosettaDesign on both types of templates, and compared the energies and packing qualities of the resulting structures. Overall, the X‐ray structures were better templates for use with Rosetta. However, for ～20% of proteins, a member of the reported NMR ensemble gave rise to designs with similar properties. Re‐evaluating RosettaDesign structures with other energy functions indicated much smaller differences between the two types of templates. Ultimately, experiments are required to confirm the utility of particular X‐ray and NMR templates. But our data suggest that the lack of a high‐resolution X‐ray structure should not preclude attempts at computational design if an NMR ensemble is available. Proteins 2009. © 2009 Wiley‐Liss, Inc.