STAP Refinement of the NMR database: a database of 2405 refined solution NMR structures

Core packing of well‐defined X‐ray and NMR structures is the same

Numerous studies have investigated the differences and similarities between protein structures determined by solution NMR spectroscopy and those determined by X-ray crystallography. A fundamental question is whether any observed differences are due to differing methodologies or to differences in the behavior of proteins in solution versus in the crystalline state. Here, we compare the properties of the hydrophobic cores of high-resolution protein crystal structures and those in NMR structures, determined using increasing numbers and types of restraints. Prior studies have reported that many NMR structures have denser cores compared with those of high-resolution X-ray crystal structures. Our current work investigates this result in more detail and finds that these NMR structures tend to violate basic features of protein stereochemistry, such as small non-bonded atomic overlaps and few Ramachandran and sidechain dihedral angle outliers. We find that NMR structures solved with more restraints, and which do not significantly violate stereochemistry, have hydrophobic cores that have a similar size and packing fraction as their counterparts determined by X-ray crystallography at high resolution. These results lead us to conclude that, at least regarding the core packing properties, high-quality structures determined by NMR and X-ray crystallography are the same, and the differences reported earlier are most likely a consequence of methodology, rather than fundamental differences between the protein in the two different environments.

Comparative studies of the expression of creatine kinase isoforms under immune stress in Pelodiscus sinensis

The expression and localization of different isoforms of creatine kinase in Pelodiscus sinensis (PSCK) were studied to reveal the role of PSCK isozymes (PSCK-B, PSCK-M, PSCK-S) under bacterial infection-induced immunologic stress. The computational molecular dynamics simulations predicted that PSCK-S would mostly possess a kinase function in a structural aspect when compared to PSCK-B and PSCK-M. The assay of biochemical parameters such as total superoxide dismutase (T-SOD), lactate dehydrogenase (LDH), malondialdehyde (MDA), catalase (CAT), and the content of ATP were measured along with total PSCK activity in different tissue samples under bacterial infection. The expression detections of PSCK isozymes in vitro and in vivo were overall well-matched where PSCK isozymes were expressed differently in P. sinensis tissues. The results showed that PSCK-B mostly contributes to the spleen, followed by the liver and myocardium; PSCK-M mostly contributes to the liver, followed by the myocardium and skeletal muscle, while PSCK-S contributes to the spleen and is uniquely expressed in skeletal muscle. Our study suggests that the various alterations of PSCK isozymes in tissues of P. sinensis are prone to defense the bacterial infection and blocking energetic imbalance before severe pathogenesis turned on in P. sinensis.

Finding the needle in the haystack: towards solving the protein-folding problem computationally

Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.

Improving NMR Structures of RNA

Here, we show that modern solution nuclear magnetic resonance (NMR) structures of RNA exhibit more steric clashes and conformational ambiguities than their crystallographic X-ray counterparts. To tackle these issues, we developed RNA-ff1, a new force field for structure calculation with Xplor-NIH. Using seven published NMR datasets, RNA-ff1 improves covalent geometry and MolProbity validation criteria for clashes and backbone conformation in most cases, relative to both the previous Xplor-NIH force field and the original structures associated with the experimental data. In addition, with smaller base-pair step rises in helical stems, RNA-ff1 structures enjoy more favorable base stacking. Finally, structural accuracy improves in the majority of cases, as supported by complete residual dipolar coupling cross-validation. Thus, the reported advances show great promise in bridging the quality gap that separates NMR and X-ray structures of RNA.

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.

Protein NMR structures refined with Rosetta have higher accuracy relative to corresponding X-ray crystal structures

We have found that refinement of protein NMR structures using Rosetta with experimental NMR restraints yields more accurate protein NMR structures than those that have been deposited in the PDB using standard refinement protocols. Using 40 pairs of NMR and X-ray crystal structures determined by the Northeast Structural Genomics Consortium, for proteins ranging in size from 5-22 kDa, restrained Rosetta refined structures fit better to the raw experimental data, are in better agreement with their X-ray counterparts, and have better phasing power compared to conventionally determined NMR structures. For 37 proteins for which NMR ensembles were available and which had similar structures in solution and in the crystal, all of the restrained Rosetta refined NMR structures were sufficiently accurate to be used for solving the corresponding X-ray crystal structures by molecular replacement. The protocol for restrained refinement of protein NMR structures was also compared with restrained CS-Rosetta calculations. For proteins smaller than 10 kDa, restrained CS-Rosetta, starting from extended conformations, provides slightly more accurate structures, while for proteins in the size range of 10-25 kDa the less CPU intensive restrained Rosetta refinement protocols provided equally or more accurate structures. The restrained Rosetta protocols described here can improve the accuracy of protein NMR structures and should find broad and general for studies of protein structure and function.

Statistical torsion angle potential energy functions for protein structure modeling: a bicubic interpolation approach

A set of grid type knowledge-based energy functions is introduced for ϕ-χ₁ , ψ-χ₁ , ϕ-ψ, and χ₁ -χ₂ torsion angle combinations. Boltzmann distribution is assumed for the torsion angle populations from protein X-ray structures, and the functions are named as statistical torsion angle potential energy functions. The grid points around periodic boundaries are duplicated to force periodicity, and the remedy relieves the derivative discontinuity problem. The devised functions rapidly improve the quality of model structures. The potential bias in the functions and the usefulness of additional secondary structure information are also investigated. The proposed guiding functions are expected to facilitate protein structure modeling, such as protein structure prediction, protein design, and structure refinement.

Smooth statistical torsion angle potential derived from a large conformational database via adaptive kernel density estimation improves the quality of NMR protein structures

Statistical potentials that embody torsion angle probability densities in databases of high-quality X-ray protein structures supplement the incomplete structural information of experimental nuclear magnetic resonance (NMR) datasets. By biasing the conformational search during the course of structure calculation toward highly populated regions in the database, the resulting protein structures display better validation criteria and accuracy. Here, a new statistical torsion angle potential is developed using adaptive kernel density estimation to extract probability densities from a large database of more than 10⁶ quality-filtered amino acid residues. Incorporated into the Xplor-NIH software package, the new implementation clearly outperforms an older potential, widely used in NMR structure elucidation, in that it exhibits simultaneously smoother and sharper energy surfaces, and results in protein structures with improved conformation, nonbonded atomic interactions, and accuracy.

The effect of Zn(2+) on Pelodiscus sinensis creatine kinase: unfolding and aggregation studies

We studied the effects of Zn(2+) on creatine kinase from the Chinese soft-shelled turtle, Pelodiscus sinensis (PSCK). Zn(2+) inactivated the activity of PSCK (IC(50) = .079 ± .004 mM) following first-order kinetics consistent with multiple phases. The spectrofluorimetry results showed that Zn(2+) induced significant tertiary structural changes of PSCK with exposure to hydrophobic surfaces and that Zn(2+) directly induced PSCK aggregation. The addition of osmolytes such as glycine, proline, and liquaemin successfully blocked PSCK aggregation, recovering the conformation and activity of PSCK. We measured the ORF gene sequence of PSCK by rapid amplification of cDNA end and simulated the 3D structure of PSCK. The results of molecular dynamics simulations showed that eight Zn(2+) bind to PSCK and one Zn(2+) is predicted to bind in a plausible active site of creatine and ATP. The interaction of Zn(2+) with the active site could mostly block the activity of PSCK. Our study provides important insight into the action of Zn(2+) on PSCK as well as more insights into the PSCK folding and ligand-binding mechanisms, which could provide important insight into the metabolic enzymes of P. sinensis.