The Total Solubility of the Co-Solubilized PAHs with Similar Structures Indicated by NMR Chemical Shift.
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ABSTRACT:
SUBMITTER: Chen T
PROVIDER: S-EPMC8125976 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:NMR chemical shift tensors (CSTs) in proteins, as well as their orientations, represent an important new restraint class for protein structure refinement and determination. Here, we present the first determination of both CST magnitudes and orientations for (13)C? and (15)N (peptide backbone) groups in a protein, the ?1 IgG binding domain of protein G from Streptococcus spp., GB1. Site-specific (13)C? and (15)N CSTs were measured using synchronously evolved recoupling experiments in which (13)C and (15)N tensors were projected onto the (1)H-(13)C and (1)H-(15)N vectors, respectively, and onto the (15)N-(13)C vector in the case of (13)C?. The orientations of the (13)C? CSTs to the (1)H-(13)C and (13)C-(15)N vectors agreed well with the results of ab initio calculations, with an rmsd of approximately 8°. In addition, the measured (15)N tensors exhibited larger reduced anisotropies in ?-helical versus ?-sheet regions, with very limited variation (18 ± 4°) in the orientation of the z-axis of the (15)N CST with respect to the (1)H-(15)N vector. Incorporation of the (13)C? CST restraints into structure calculations, in combination with isotropic chemical shifts, transferred echo double resonance (13)C-(15)N distances and vector angle restraints, improved the backbone rmsd to 0.16 ? (PDB ID code 2LGI) and is consistent with existing X-ray structures (0.51 ? agreement with PDB ID code 2QMT). These results demonstrate that chemical shift tensors have considerable utility in protein structure refinement, with the best structures comparable to 1.0-? crystal structures, based upon empirical metrics such as Ramachandran geometries and ?(1)/?(2) distributions, providing solid-state NMR with a powerful tool for de novo structure determination.
| S-EPMC3193204 | biostudies-literature
Project description:NMR relaxation dispersion measurements report on conformational changes occurring on the μs-ms timescale. Chemical shift information derived from relaxation dispersion can be used to generate structural models of weakly populated alternative conformational states. Current methods to obtain such models rely on determining the signs of chemical shift changes between the conformational states, which are difficult to obtain in many situations. Here, we use a "sample and select" method to generate relevant structural models of alternative conformations of the C-terminal-associated region of Escherichia coli dihydrofolate reductase (DHFR), using only unsigned chemical shift changes for backbone amides and carbonyls (1H, 15N, and 13C'). We find that CS-Rosetta sampling with unsigned chemical shift changes generates a diversity of structures that are sufficient to characterize a minor conformational state of the C-terminal region of DHFR. The excited state differs from the ground state by a change in secondary structure, consistent with previous predictions from chemical shift hypersurfaces and validated by the x-ray structure of a partially humanized mutant of E. coli DHFR (N23PP/G51PEKN). The results demonstrate that the combination of fragment modeling with sparse chemical shift data can determine the structure of an alternative conformation of DHFR sampled on the μs-ms timescale. Such methods will be useful for characterizing alternative states, which can potentially be used for in silico drug screening, as well as contributing to understanding the role of minor states in biology and molecular evolution.
| S-EPMC7840400 | biostudies-literature
Project description:BackgroundNMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model.A variety of NMR chemical shift prediction approaches have been presented in the past, but nearly all of these rely on laborious manual data set preparation and the training itself is not automatized, making retraining the model, e.g., if new data is made available, or testing new models a time-consuming manual chore.ResultsIn this work, we present the framework NightShift (NMR Shift Inference by General Hybrid Model Training), which enables automated data set generation as well as model training and evaluation of protein NMR chemical shift prediction.In addition to this main result - the NightShift framework itself - we describe the resulting, automatically generated, data set and, as a proof-of-concept, a random forest model called Spinster that was built using the pipeline.ConclusionBy demonstrating that the performance of the automatically generated predictors is at least en par with the state of the art, we conclude that automated data set and predictor generation is well-suited for the design of NMR chemical shift estimators.The framework can be downloaded from https://bitbucket.org/akdehof/nightshift. It requires the open source Biochemical Algorithms Library (BALL), and is available under the conditions of the GNU Lesser General Public License (LGPL). We additionally offer a browser-based user interface to our NightShift instance employing the Galaxy framework via https://ballaxy.bioinf.uni-sb.de/.
| S-EPMC3682865 | biostudies-literature
Project description:Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.
| S-EPMC6788782 | biostudies-literature
Project description:Data validation plays an important role in ensuring the reliability and reproducibility of studies. NMR investigations of the functional properties, dynamics, chemical kinetics, and structures of proteins depend critically on the correctness of chemical shift assignments. We present a novel probabilistic method named ARECA for validating chemical shift assignments that relies on the nuclear Overhauser effect data . ARECA has been evaluated through its application to 26 case studies and has been shown to be complementary to, and usually more reliable than, approaches based on chemical shift databases. ARECA is available online at http://areca.nmrfam.wisc.edu/.
| S-EPMC4744101 | biostudies-literature
Project description:Understanding the connection between protein structure and function requires a quantitative understanding of electrostatic effects. Structure-based electrostatic calculations are essential for this purpose, but their use has been limited by a long-standing discussion on which value to use for the dielectric constants (ε(eff) and ε(p)) required in Coulombic and Poisson-Boltzmann models. The currently used values for ε(eff) and ε(p) are essentially empirical parameters calibrated against thermodynamic properties that are indirect measurements of protein electric fields. We determine optimal values for ε(eff) and ε(p) by measuring protein electric fields in solution using direct detection of NMR chemical shift perturbations (CSPs). We measured CSPs in 14 proteins to get a broad and general characterization of electric fields. Coulomb's law reproduces the measured CSPs optimally with a protein dielectric constant (ε(eff)) from 3 to 13, with an optimal value across all proteins of 6.5. However, when the water-protein interface is treated with finite difference Poisson-Boltzmann calculations, the optimal protein dielectric constant (ε(p)) ranged from 2 to 5 with an optimum of 3. It is striking how similar this value is to the dielectric constant of 2-4 measured for protein powders and how different it is from the ε(p) of 6-20 used in models based on the Poisson-Boltzmann equation when calculating thermodynamic parameters. Because the value of ε(p) = 3 is obtained by analysis of NMR chemical shift perturbations instead of thermodynamic parameters such as pK(a) values, it is likely to describe only the electric field and thus represent a more general, intrinsic, and transferable ε(p) common to most folded proteins.
| S-EPMC4480918 | biostudies-literature
Project description:BackgroundThe posteromedial ankle structures are at risk during total ankle replacement (TAR). The purpose of our study was to investigate the distance of these structures from the posterior cortex of the tibia and talus in order to determine their anatomy at different levels of bone resection during a TAR and whether plantarflexion of the ankle reliably moved these structures posteriorly.MethodsTen feet in 10 patients with end-stage tibiotalar arthritis indicated for a TAR were included. Preoperative magnetic resonance images were obtained with the foot in a neutral position as well as in maximum plantarflexion to measure the distance of posteromedial ankle structures to the closest part of the posterior cortex of the tibia or talus. Wilcoxon signed-rank rests were used to investigate differences in these distances.ResultsThe mean distance from the posterior tibial cortex to the tibial nerve at 14 and 7 mm above the tibial plafond was 8.7 mm (range 5.0-11.8 mm) and 6.7 mm (range 2.7-10.6 mm), respectively, which represented a statistically significant movement anteriorly (P = .021). The posterior tibial artery was, on average, 8.0 mm (range 3.6-13.9 mm) and 7.2 mm (range 3.1-9.4 mm) from the posterior tibial cortex at 14 and 7 mm above the tibial plafond, respectively. Distal to the tibial plafond, the posterior tibial artery and flexor digitorum longus tendons moved posteriorly by less than 1 mm in plantarflexion (all P < .05); otherwise, plantarflexion of the ankle did not affect the position of the tibial nerve, posterior tibial tendon, or flexor hallucis longus.ConclusionIn patients with end-stage ankle arthritis, the tibial nerve and posterior tibial artery lie, on average, between 6.5 and 10 mm from the posterior tibial and talar cortices. Plantarflexion of the ankle did not reliably move the posteromedial ankle structures posteriorly.Level of evidenceLevel IV, case series, therapeutic.
| S-EPMC8697152 | biostudies-literature
Project description:Protein NMR chemical shifts are highly sensitive to local structure. A robust protocol is described that exploits this relation for de novo protein structure generation, using as input experimental parameters the (13)C(alpha), (13)C(beta), (13)C', (15)N, (1)H(alpha) and (1)H(N) NMR chemical shifts. These shifts are generally available at the early stage of the traditional NMR structure determination process, before the collection and analysis of structural restraints. The chemical shift based structure determination protocol uses an empirically optimized procedure to select protein fragments from the Protein Data Bank, in conjunction with the standard ROSETTA Monte Carlo assembly and relaxation methods. Evaluation of 16 proteins, varying in size from 56 to 129 residues, yielded full-atom models that have 0.7-1.8 A root mean square deviations for the backbone atoms relative to the experimentally determined x-ray or NMR structures. The strategy also has been successfully applied in a blind manner to nine protein targets with molecular masses up to 15.4 kDa, whose conventional NMR structure determination was conducted in parallel by the Northeast Structural Genomics Consortium. This protocol potentially provides a new direction for high-throughput NMR structure determination.
| S-EPMC2290745 | biostudies-literature
Project description:We have carried out chemical shift perturbation titrations on three contrasting proteins. The resulting chemical shifts have been analysed to determine the best way to fit the data, and it is concluded that a simultaneous fitting of all raw shift data to a single dissociation constant is both the most accurate and the most precise method. It is shown that the optimal weighting of 15N chemical shifts to 1H chemical shifts is protein dependent, but is around the consensus value of 0.14. We show that chemical shift changes of individual residues can be fit to give residue-specific affinities. Residues with affinities significantly stronger than average are found in close contact with the ligand and are suggested to form a rigid contact surface, but only when the binding involves little conformational change. This observation may be of value in analysing binding and conformational change.
| S-EPMC9427925 | biostudies-literature
Project description:We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data representation of a molecule. We define multiple channels that describe different spatial resolutions for each atom type that utilizes cropping, pooling, and concatenation to create a multiresolution 3D-DenseNet architecture (MR-3D-DenseNet). Because the training and testing time scale linearly with the number of samples, the MR-3D-DenseNet can exploit data augmentation that takes into account the property of rotational invariance of the chemical shifts, thereby also increasing the size of the training data set by an order of magnitude without additional cost. We obtain very good agreement for 13C, 15N, and 17O chemical shifts when compared to ab initio quantum chemistry methods, with the highest accuracy found for 1H chemical shifts that is comparable to the error between the ab initio results and experimental measurements. Principal component analysis (PCA) is used to both understand these greatly improved predictions for 1H , as well as indicating that chemical shift prediction for 13C, 15N, and 17O, which have far fewer training environments than the 1H atom type, will improve once more unique training samples are made available to exploit the deep network architecture.
| S-EPMC6922005 | biostudies-literature