X-ray structural and simulation analysis of a protein mutant: the value of a combined approach

The effect of the mutation Arg 96 to His on the stability of bacteriophage T4 lysozyme has been previously studied by calorimetric experiments, X-ray crystallography, and free energy simulation techniques. The experimental and calculated values for the difference between the free energy of denaturation of the mutant and the wild type are in reasonable agreement. However, the two approaches led to different explanations for the loss in stability. To analyze the differences, a series of refinements based on the crystallographic data were performed, a number of aspects of the simulations were reexamined, and continuum electrostatic calculations were done to complement the latter. The results of those comparisons provide a better understanding of the origin of the free energy difference in this mutant. Furthermore, they show the importance of the combined use of simulations and crystallography for interpreting the effects of mutations on the energetics of the system.     

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.     

High-accuracy protein structures by combining machine-learning with physics-based refinement

Protein structure prediction has long been available as an alternative to experimental structure determination, especially via homology modeling based on templates from related sequences. Recently, models based on distance restraints from coevolutionary analysis via machine learning to have significantly expanded the ability to predict structures for sequences without templates. One such method, AlphaFold, also performs well on sequences where templates are available but without using such information directly. Here we show that combining machine-learning based models from AlphaFold with state-of-the-art physics-based refinement via molecular dynamics simulations further improves predictions to outperform any other prediction method tested during the latest round of CASP. The resulting models have highly accurate global and local structures, including high accuracy at functionally important interface residues, and they are highly suitable as initial models for crystal structure determination via molecular replacement.     

Relative stability of protein structures determined by X-ray crystallography or NMR spectroscopy: a molecular dynamics simulation study

The relative stability of protein structures determined by either X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy has been investigated by using molecular dynamics simulation techniques. Published structures of 34 proteins containing between 50 and 100 residues have been evaluated. The proteins selected represent a mixture of secondary structure types including all alpha, all beta, and alpha/beta. The proteins selected do not contain cysteine-cysteine bridges. In addition, any crystallographic waters, metal ions, cofactors, or bound ligands were removed before the systems were simulated. The stability of the structures was evaluated by simulating, under identical conditions, each of the proteins for at least 5 ns in explicit solvent. It is found that not only do NMR-derived structures have, on average, higher internal strain than structures determined by X-ray crystallography but that a significant proportion of the structures are unstable and rapidly diverge in simulations.     

Progress and challenges in high-resolution refinement of protein structure models

Achieving atomic level accuracy in de novo structure prediction presents a formidable challenge even in the context of protein models with correct topologies. High-resolution refinement is a fundamental test of force field accuracy and sampling methodology, and its limited success in both comparative modeling and de novo prediction contexts highlights the limitations of current approaches. We constructed four tests to identify bottlenecks in our current approach and to guide progress in this challenging area. The first three tests showed that idealized native structures are stable under our refinement simulation conditions and that the refinement protocol can significantly decrease the root mean square deviation (RMSD) of perturbed native structures. In the fourth test we applied the refinement protocol to de novo models and showed that accurate models could be identified based on their energies, and in several cases many of the buried side chains adopted native-like conformations. We also showed that the differences in backbone and side-chain conformations between the refined de novo models and the native structures are largely localized to loop regions and regions where the native structure has unusual features such as rare rotamers or atypical hydrogen bonding between beta-strands. The refined de novo models typically have higher energies than refined idealized native structures, indicating that sampling of local backbone conformations and side-chain packing arrangements in a condensed state is a primary obstacle.     

3Drefine: Consistent protein structure refinement by optimizing hydrogen bonding network and atomic‐level energy minimization

One of the major limitations of computational protein structure prediction is the deviation of predicted models from their experimentally derived true, native structures. The limitations often hinder the possibility of applying computational protein structure prediction methods in biochemical assignment and drug design that are very sensitive to structural details. Refinement of these low‐resolution predicted models to high‐resolution structures close to the native state, however, has proven to be extremely challenging. Thus, protein structure refinement remains a largely unsolved problem. Critical assessment of techniques for protein structure prediction (CASP) specifically indicated that most predictors participating in the refinement category still did not consistently improve model quality. Here, we propose a two‐step refinement protocol, called 3Drefine, to consistently bring the initial model closer to the native structure. The first step is based on optimization of hydrogen bonding (HB) network and the second step applies atomic‐level energy minimization on the optimized model using a composite physics and knowledge‐based force fields. The approach has been evaluated on the CASP benchmark data and it exhibits consistent improvement over the initial structure in both global and local structural quality measures. 3Drefine method is also computationally inexpensive, consuming only few minutes of CPU time to refine a protein of typical length (300 residues). 3Drefine web server is freely available at http://sysbio.rnet.missouri.edu/3Drefine/ . Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Macromolecular refinement of X-ray and cryoelectron microscopy structures with Phenix/OPLS3e for improved structure and ligand quality

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RDOCK: refinement of rigid-body protein docking predictions

We present a simple and effective algorithm RDOCK for refining unbound predictions generated by a rigid-body docking algorithm ZDOCK, which has been developed earlier by our group. The main component of RDOCK is a three-stage energy minimization scheme, followed by the evaluation of electrostatic and desolvation energies. Ionic side chains are kept neutral in the first two stages of minimization, and reverted to their full charge states in the last stage of brief minimization. Without side chain conformational search or filtering/clustering of resulting structures, RDOCK represents the simplest approach toward refining unbound docking predictions. Despite its simplicity, RDOCK makes substantial improvement upon the top predictions by ZDOCK with all three scoring functions and the improvement is observed across all three categories of test cases in a large benchmark of 49 non-redundant unbound test cases. RDOCK makes the most powerful combination with ZDOCK2.1, which uses pairwise shape complementarity as the scoring function. Collectively, they rank a near-native structure as the number-one prediction for 18 test cases (37% of the benchmark), and within the top 4 predictions for 24 test cases (49% of the benchmark). To various degrees, funnel-like energy landscapes are observed for these 24 test cases. To the best of our knowledge, this is the first report of binding funnels starting from global searches for a broad range of test cases. These results are particularly exciting, given that we have not used any biological information that is specific to individual test cases and the whole process is entirely automated. Among three categories of test cases, the best results are seen for enzyme/inhibitor, with a near-native structure ranked as the number-one prediction for 48% test cases, and within the top 10 predictions for 78% test cases. RDOCK is freely available to academic users at http://zlab.bu.edu/ approximately rong/dock.     

Conformational energy range of ligands in protein crystal structures: The difficult quest for accurate understanding

In this review, we address a fundamental question: What is the range of conformational energies seen in ligands in protein‐ligand crystal structures? This value is important biophysically, for better understanding the protein‐ligand binding process; and practically, for providing a parameter to be used in many computational drug design methods such as docking and pharmacophore searches. We synthesize a selection of previously reported conflicting results from computational studies of this issue and conclude that high ligand conformational energies really are present in some crystal structures. The main source of disagreement between different analyses appears to be due to divergent treatments of electrostatics and solvation. At the same time, however, for many ligands, a high conformational energy is in error, due to either crystal structure inaccuracies or incorrect determination of the reference state. Aside from simple chemistry mistakes, we argue that crystal structure error may mainly be because of the heuristic weighting of ligand stereochemical restraints relative to the fit of the structure to the electron density. This problem cannot be fixed with improvements to electron density fitting or with simple ligand geometry checks, though better metrics are needed for evaluating ligand and binding site chemistry in addition to geometry during structure refinement. The ultimate solution for accurately determining ligand conformational energies lies in ultrahigh‐resolution crystal structures that can be refined without restraints.     

On the reliability of peptide nonplanarity seen in ultra‐high resolution crystal structures

Ultra‐high resolution protein crystal structures have been considered as relatively reliable sources for defining details of protein geometry, such as the extent to which the peptide unit deviates from planarity. Chellapa and Rose (Proteins 2015; 83:1687) recently called this into question, reporting that for a dozen representative protein structures determined at ～1 Å resolution, the diffraction data could be equally well fit with models restrained to have highly planar peptides, i.e. having a standard deviation of the ω torsion angles of only ～1° instead of the typically observed value of ～6°. Here, we document both conceptual and practical shortcomings of that study and show that the more tightly restrained models are demonstrably incorrect and do not fit the diffraction data equally well. We emphasize the importance of inspecting electron density maps when investigating the agreement between a model and its experimental data. Overall, this report reinforces that modern standard refinement protocols have been well‐conceived and that ultra‐high resolution protein crystal structures, when evaluated carefully and used with an awareness of their levels of coordinate uncertainty, are powerful sources of information for providing reliable information about the details of protein geometry.     

Mimicking the action of GroEL in molecular dynamics simulations: application to the refinement of protein structures

Bacterial chaperonin, GroEL, together with its co-chaperonin, GroES, facilitates the folding of a variety of polypeptides. Experiments suggest that GroEL stimulates protein folding by multiple cycles of binding and release. Misfolded proteins first bind to an exposed hydrophobic surface on GroEL. GroES then encapsulates the substrate and triggers its release into the central cavity of the GroEL/ES complex for folding. In this work, we investigate the possibility to facilitate protein folding in molecular dynamics simulations by mimicking the effects of GroEL/ES namely, repeated binding and release, together with spatial confinement. During the binding stage, the (metastable) partially folded proteins are allowed to attach spontaneously to a hydrophobic surface within the simulation box. This destabilizes the structures, which are then transferred into a spatially confined cavity for folding. The approach has been tested by attempting to refine protein structural models generated using the ROSETTA procedure for ab initio structure prediction. Dramatic improvements in regard to the deviation of protein models from the corresponding experimental structures were observed. The results suggest that the primary effects of the GroEL/ES system can be mimicked in a simple coarse-grained manner and be used to facilitate protein folding in molecular dynamics simulations. Furthermore, the results support the assumption that the spatial confinement in GroEL/ES assists the folding of encapsulated proteins.     

Comparison of NMR and crystal structures of membrane proteins and computational refinement to improve model quality

Membrane proteins are challenging to study and restraints for structure determination are typically sparse or of low resolution because the membrane environment that surrounds them leads to a variety of experimental challenges. When membrane protein structures are determined by different techniques in different environments, a natural question is “which structure is most biologically relevant?” Towards answering this question, we compiled a dataset of membrane proteins with known structures determined by both solution NMR and X‐ray crystallography. By investigating differences between the structures, we found that RMSDs between crystal and NMR structures are below 5 Å in the membrane region, NMR ensembles have a higher convergence in the membrane region, crystal structures typically have a straighter transmembrane region, have higher stereo‐chemical correctness, and are more tightly packed. After quantifying these differences, we used high‐resolution refinement of the NMR structures to mitigate them, which paves the way for identifying and improving the structural quality of membrane proteins.     

Determination of the populations and structures of multiple conformers in an ensemble from NMR data: Multiple-copy refinement of nucleic acid structures using floating weights

A new algorithm is presented for determination of structural conformers and their populations based on NMR data. Restrained Metropolis Monte Carlo simulations or restrained energy minimizations are performed for several copies of a molecule simultaneously. The calculations are restrained with dipolar relaxation rates derived from measured NOE intensities via complete relaxation matrix analysis. The novel feature of the algorithm is that the weights of individual conformers are determined in every refinement step, by the quadratic programming algorithm, in such a way that the restraint energy is minimized. Its design ensures that the calculated populations of the individual conformers are based only on experimental restraints. Presence of internally inconsistent restraints is the driving force for determination of distinct multiple conformers. The method is applied to various simulated test systems. Conformational calculations on nucleic acids are carried out using generalized helical parameters with the program DNAminiCarlo. From different mixtures of A- and B-DNA, minor fractions as low as 10% could be determined with restrained energy minimization. For B-DNA with three local conformers (C2-endo, O4-exo, C3-endo), the minor O4-exo conformer could not be reliably determined using NOE data typically measured for DNA. The other two conformers, C2-endo and C3-endo, could be reproduced by Metropolis Monte Carlo simulated annealing. The behavior of the algorithm in various situations is analyzed, and a number of refinement protocols are discussed. Prior to application of this algorithm to each experimental system, it is suggested that the presence of internal inconsistencies in experimental data be ascertained. In addition, because the performance of the algorithm depends on the type of conformers involved and experimental data available, it is advisable to carry out test calculations with simulated data modeling each experimental system studied.     

FireDock: fast interaction refinement in molecular docking

Here, we present FireDock, an efficient method for the refinement and rescoring of rigid-body docking solutions. The refinement process consists of two main steps: (1) rearrangement of the interface side-chains and (2) adjustment of the relative orientation of the molecules. Our method accounts for the observation that most interface residues that are important in recognition and binding do not change their conformation significantly upon complexation. Allowing full side-chain flexibility, a common procedure in refinement methods, often causes excessive conformational changes. These changes may distort preformed structural signatures, which have been shown to be important for binding recognition. Here, we restrict side-chain movements, and thus manage to reduce the false-positive rate noticeably. In the later stages of our procedure (orientation adjustments and scoring), we smooth the atomic radii. This allows for the minor backbone and side-chain movements and increases the sensitivity of our algorithm. FireDock succeeds in ranking a near-native structure within the top 15 predictions for 83% of the 30 enzyme-inhibitor test cases, and for 78% of the 18 semiunbound antibody-antigen complexes. Our refinement procedure significantly improves the ranking of the rigid-body PatchDock algorithm for these cases. The FireDock program is fully automated. In particular, to our knowledge, FireDock's prediction results are comparable to current state-of-the-art refinement methods while its running time is significantly lower. The method is available at http://bioinfo3d.cs.tau.ac.il/FireDock/.     

Monte Carlo refinement of rigid-body protein docking structures with backbone displacement and side-chain optimization

Structures of hitherto unknown protein complexes can be predicted by docking the solved protein monomers. Here, we present a method to refine initial docking estimates of protein complex structures by a Monte Carlo approach including rigid-body moves and side-chain optimization. The energy function used is comprised of van der Waals, Coulomb, and atomic contact energy terms. During the simulation, we gradually shift from a novel smoothed van der Waals potential, which prevents trapping in local energy minima, to the standard Lennard-Jones potential. Following the simulation, the conformations are clustered to obtain the final predictions. Using only the first 100 decoys generated by a fast Fourier transform (FFT)-based rigid-body docking method, our refinement procedure is able to generate near-native structures (interface RMSD <2.5 A) as first model in 14 of 59 cases in a benchmark set. In most cases, clear binding funnels around the native structure can be observed. The results show the potential of Monte Carlo refinement methods and emphasize their applicability for protein-protein docking.     

Mimicking the action of folding chaperones in molecular dynamics simulations: Application to the refinement of homology-based protein structures

A novel method for the refinement of misfolded protein structures is proposed in which the properties of the solvent environment are oscillated in order to mimic some aspects of the role of molecular chaperones play in protein folding in vivo. Specifically, the hydrophobicity of the solvent is cycled by repetitively altering the partial charges on solvent molecules (water) during a molecular dynamics simulation. During periods when the hydrophobicity of the solvent is increased, intramolecular hydrogen bonding and secondary structure formation are promoted. During periods of increased solvent polarity, poorly packed regions of secondary structures are destabilized, promoting structural rearrangement. By cycling between these two extremes, the aim is to minimize the formation of long-lived intermediates. The approach has been applied to the refinement of structural models of three proteins generated by using the ROSETTA procedure for ab initio structure prediction. A significant improvement in the deviation of the model structures from the corresponding experimental structures was observed. Although preliminary, the results indicate computationally mimicking some functions of molecular chaperones in molecular dynamics simulations can promote the correct formation of secondary structure and thus be of general use in protein folding simulations and in the refinement of structural models of small- to medium-size proteins.     

X-ray structures of three interface mutants of gammaB-crystallin from bovine eye lens.

GammaB-crystallin consists of two domains each comprising two "Greek key" motifs. Both domains fold independently, and domain interactions contribute significantly to the stability of the C-terminal domain. In a previous study (Palme S et al., 1996, Protein Sci 6:1529-1636) it was shown that Phe56 from the N-terminal domain, a residue involved in forming a hydrophobic core at the domain interface, effects the interaction of the two domains, and therefore, the stability of the C-terminal domain. Ala or Asp at position 56 drastically decreased the stability of the C-terminal domain, whereas Trp had a more moderate effect. In this article we present the X-ray structures of these interface mutants and correlate them with the stability data. The mutations do not effect the overall structure of the molecule. No structural changes are observed in the vicinity of the replaced residue, suggesting that the local structure is too rigid to allow compensations for the amino acid replacements. In the mutants gammaB-F56A and -F56D, a solvent-filled groove accessible to the bulk solvent is created by the replacement of the bulky Phe side chain. In gammaB-F56W, the pyrrole moiety of the indole ring replaces the phenyl side chain of the wild type. With the exception of gammaB-F56W, there is a good correlation between the hydrophobicity of the amino acid at position 56 according to the octanol scale and the stability of the C-terminal domain. In gammaB-F56W, the C-terminal domain is less stable than estimated from the hydrophobicity, presumably because the ring nitrogen (Nepsilon1) has no partner to form hydrogen bonds. The data suggest that the packing of hydrophobic residues in the interface core is important for domain interactions and the stability of gammaB-crystallin. Apparently, for protein stability, the same principles apply for hydrophobic cores within domains and at domain interfaces.     

High resolution X-ray structures of mouse major urinary protein nasal isoform in complex with pheromones

In mice, the major urinary proteins (MUP) play a key role in pheromonal communication by binding and transporting semiochemicals. MUP‐IV is the only isoform known to be expressed in the vomeronasal mucosa. In comparison with the MUP isoforms that are abundantly excreted in the urine, MUP‐IV is highly specific for the male mouse pheromone 2‐sec‐butyl‐4,5‐dihydrothiazole (SBT). To examine the structural basis of this ligand preference, we determined the X‐ray crystal structure of MUP‐IV bound to three mouse pheromones: SBT, 2,5‐dimethylpyrazine, and 2‐heptanone. We also obtained the structure of MUP‐IV with 2‐ethylhexanol bound in the cavity. These four structures show that relative to the major excreted MUP isoforms, three amino acid substitutions within the binding calyx impact ligand coordination. The F103 for A along with F54 for L result in a smaller cavity, potentially creating a more closely packed environment for the ligand. The E118 for G substitution introduces a charged group into a hydrophobic environment. The sidechain of E118 is observed to hydrogen bond to polar groups on all four ligands with nearly the same geometry as seen for the water‐mediated hydrogen bond network in the MUP‐I and MUP‐II crystal structures. These differences in cavity size and interactions between the protein and ligand are likely to contribute to the observed specificity of MUP‐IV.     

Generation of pseudonative protein structures for threading

The threading approach to protein structure prediction suffers from the limited number of substantially different folds available as templates. A method is presented for the generation of artificial protein structures, amenable to threading, by modification of native ones. The artificial structures so generated are compared to the native ones and it is shown that, within the accuracy of the pseudoenergy function or force field used, these two types of structures appear equally useful for threading. Since a multitude of pseudonative artificial structures can be generated per native structure, the pool of pseudonative template structures for threading can be enormously enlarged by the inclusion of the pseudonative artificial structures. Proteins 28:522–529, 1997. © 1997 Wiley-Liss, Inc.