RNA unrestrained molecular dynamics ensemble improves agreement with experimental NMR data compared to single static structure: a test case

Molecular dynamics(MD) simulations are coming of age in the studyof nucleic acids, including specific tertiary structures such as G-quadruplexes.While being precious for providing structural and dynamic informationinaccessible to experiments at the atomistic level of resolution,MD simulations in this field may still be limited by several factors.These include the force fields used, different models for ion parameters,ionic strengths, and water models. We address various aspects of thisproblem by analyzing and comparing microsecond-long atomistic simulationsof the G-quadruplex structure formed by the human immunodeficiencyvirus long terminal repeat (HIV LTR)-III sequence for which nuclearmagnetic resonance (NMR) structures are available. The system is studiedin different conditions, systematically varying the ionic strengths,ion numbers, and water models. We comparatively analyze the dynamicbehavior of the G-quadruplex motif in various conditions and assessthe ability of each simulation to satisfy the nuclear magnetic resonance(NMR)-derived experimental constraints and structural parameters.The conditions taking into account K+-ions to neutralizethe system charge, mimicking the intracellular ionic strength, andusing the four-atom water model are found to be the best in reproducingthe experimental NMR constraints and data. Our analysis also revealsthat in all of the simulated environments residues belonging to theduplex moiety of HIV LTR-III exhibit the highest flexibility.

A major bottleneck in protein structure determination via nuclear magnetic resonance (NMR) is the lengthy and laborious process of assigning resonances and nuclear Overhauser effect (NOE) cross peaks. Recent studies have shown that accurate backbone folds can be determined using sparse NMR data, such as residual dipolar couplings (RDCs) or backbone chemical shifts. This opens a question of whether we can also determine the accurate protein sidechain conformations using sparse or unassigned NMR data. We attack this question by using unassigned nuclear Overhauser effect spectroscopy (NOESY) data, which record the through-space dipolar interactions between protons nearby in 3D space. We propose a Bayesian approach with a Markov random field (MRF) model to integrate the likelihood function derived from observed experimental data, with prior information (i.e., empirical molecular mechanics energies) about the protein structures. We unify the side-chain structure prediction problem with the side-chain structure determination problem using unassigned NMR data, and apply the deterministic dead-end elimination (DEE) and A* search algorithms to provably find the global optimum solution that maximizes the posterior probability. We employ a Hausdorff-based measure to derive the likelihood of a rotamer or a pairwise rotamer interaction from unassigned NOESY data. In addition, we apply a systematic and rigorous approach to estimate the experimental noise in NMR data, which also determines the weighting factor of the data term in the scoring function that is derived from the Bayesian framework. We tested our approach on real NMR data of three proteins, including the FF Domain 2 of human transcription elongation factor CA150 (FF2), the B1 domain of Protein G (GB1), and human ubiquitin. The promising results indicate that our approach can be applied in high-resolution protein structure determination. Since our approach does not require any NOE assignment, it can accelerate the NMR structure determination process.

A novel method for NMR data denoising based on discrete cosine transform and variable length windows

The Natural Products Magnetic Resonance Database (NP-MRD) is a comprehensive, freely available electronic resource for the deposition, distribution, searching and retrieval of nuclear magnetic resonance (NMR) data on natural products, metabolites and other biologically derived chemicals. NMR spectroscopy has long been viewed as the ‘gold standard’ for the structure determination of novel natural products and novel metabolites. NMR is also widely used in natural product dereplication and the characterization of biofluid mixtures (metabolomics). All of these NMR applications require large collections of high quality, well-annotated, referential NMR spectra of pure compounds. Unfortunately, referential NMR spectral collections for natural products are quite limited. It is because of the critical need for dedicated, open access natural product NMR resources that the NP-MRD was funded by the National Institute of Health (NIH). Since its launch in 2020, the NP-MRD has grown quickly to become the world's largest repository for NMR data on natural products and other biological substances. It currently contains both structural and NMR data for nearly 41,000 natural product compounds from >7400 different living species. All structural, spectroscopic and descriptive data in the NP-MRD is interactively viewable, searchable and fully downloadable in multiple formats. Extensive hyperlinks to other databases of relevance are also provided. The NP-MRD also supports community deposition of NMR assignments and NMR spectra (1D and 2D) of natural products and related meta-data. The deposition system performs extensive data enrichment, automated data format conversion and spectral/assignment evaluation. Details of these database features, how they are implemented and plans for future upgrades are also provided. The NP-MRD is available at https://np-mrd.org.

Structural rearrangements during sub-Tg relaxation and nucleation in lithium disilicate glass revealed by a solid-state NMR and MD strategy

Single-stranded regions of RNA are important for folding of sequences into 3D structures and for design of therapeutics targeting RNA. Prediction of ensembles of 3D structures for single-stranded regions often involves classical mechanical approximations of interactions defined by quantum mechanical calculations on small model systems. Nuclear magnetic resonance (NMR) spectra and molecular dynamics (MD) simulations of short single strands provide tests for how well the approximations model many of the interactions. Here, the NMR spectra for UCUCGU at 2, 15, and 30 °C are compared to simulations with the AMBER force fields, OL3 and ROC-RNA. This is the first such comparison to an oligoribonucleotide containing an internal guanosine nucleotide (G). G is particularly interesting because of its many H-bonding groups, large dipole moment, and proclivity for both syn and anti conformations. Results reveal formation of a G amino to phosphate non-bridging oxygen H-bond. The results also demonstrate dramatic differences in details of the predicted structures. The variations emphasize the dependence of predictions on individual parameters and their balance with the rest of the force field. The NMR data can serve as a benchmark for future force fields.

In this paper, a scheme for estimating frequencies and damping factors of multidimensional nuclear magnetic resonance (NMR) data is presented, multidimensional NMR data can be modeled as the sum of several multidimensional damped sinusoids. The estimated frequencies and damping factors of multidimensional NMR data play important roles in determining protein structures. In this paper we present a high-resolution subspace method for estimating the parameters of NMR data. Unlike other methods, this algorithm makes full use of the rank-deficiency and Hankel properties of the prediction matrix composed of NMR data. Hence, it can estimate the signal parameters under low signal-to-noise ratio (SNR) by using a few data points. The effectiveness of the new algorithm is confirmed by computer simulations and it is tested by experimental data.

To evaluate the ability of molecular dynamics (MD) simulations using atomic force-fields to correctly predict stable folded conformations of a peptide in solution, we show results from MD simulations of the reversible folding of an octapeptide rich in alpha-aminoisobutyric acid (2-amino-2-methyl-propanoic acid, Aib) solvated in di-methyl-sulfoxide (DMSO). This solvent generally prevents the formation of secondary structure, whereas Aib-rich peptides show a high propensity to form secondary structural elements, in particular 3(10)- and alpha-helical structures. Aib is, moreover, achiral, so that Aib-rich peptides can form left- or right-handed helices depending on the overall composition of the peptide, the temperature, and the solvation conditions. This makes the system an interesting case to study the ensembles of peptide conformations as a function of temperature by MD simulation. Simulations involving the folding and unfolding of the peptide were performed starting from two initial structures, a right-handed alpha-helical structure and an extended structure, at three temperatures, 298 K, 340 K, and 380 K, and the results are compared with experimental nuclear magnetic resonance (NMR) data measured at 298 K and 340 K. The simulations generally reproduce the available experimental nuclear Overhauser effect (NOE) data, even when a wide range of conformations is sampled at each temperature. The importance of adequate statistical sampling in order to reliably interpret the experimental data is discussed.

The Tengiz reservoir is known for its substantial bitumen content, requiring careful differentiation during formation evaluation processes. To accurately estimate the effective porosity, wireline nuclear magnetic resonance (NMR) tools were used in conjunction with other logging tools. However, the increasing number of deviated wells drilled in Tengiz posed conveyance challenges for the wireline-based NMR tool due to high deviation angles. In light of this, TCO (Tengizchevroil) geoscientists sought opportunities to acquire NMR data using logging-while-drilling (LWD) technology in the reservoir section. Although the LWD NMR tool was not previously available for the required hole size, recent commercialization made it feasible. A team of geoscientists at TCO meticulously planned and executed a field trial of the LWD NMR tool to assess its potential future utilization in situations where the conveyance of wireline NMR tools may encounter difficulties. The LWD NMR tool was successfully deployed in a deviated well during drilling operations, followed by a subsequent measurement after drilling (MAD) pass to ensure log quality control. To verify and compare the acquired data, wireline-based NMR measurements were also conducted in the same well. The results of the field trial demonstrated the successful acquisition of high-quality LWD NMR data, corroborated by the MAD pass data and wireline log data. Furthermore, we revised and tested internal NMR data processing workflows, which conclusively established LWD NMR as a viable alternative to wireline-based NMR tools. Notably, the acquisition of NMR data while drilling offers additional benefits, as the near wellbore zone is not fully flushed with drilling mud, potentially enhancing fluid typing techniques (T1 and T2 distribution). This promising outcome opens up new avenues for more efficient and reliable estimation of effective porosity in deviated wells, as well as in wells with potential restrictions to run WL tools within the Tengiz reservoir and beyond, contributes to improved reservoir characterization and decision-making processes in asset development.

Accuracy of MD solvent models in RNA structure refinement assessed via liquid-crystal NMR and spin relaxation data

Decision letter: Dynamic Na+/H+ exchanger 1 (NHE1) – calmodulin complexes of varying stoichiometry and structure regulate Ca2+-dependent NHE1 activation

Mixture separation can be a challenging but an engaging problem in a wide variety of fields. It is of particular interest for identification of compounds using nuclear magnetic resonance (NMR) spectroscopy. Currently, NMR data is most often acquired from isolated purified compounds as it can become very challenging to identify the components in a complex mixture system. NMR can identify individual samples with respect to their unique fingerprint spectra; however it has been traditionally poor at handling mixtures. Robustly designed techniques for separation of NMR spectra into subspectra from individual components would be of great interest in, for example, the pharmaceutical industry. Structural information about promising candidates from the drug design process must be extracted from samples that contain side products and impurities in addition to the target molecule. Diffusion ordered spectroscopy (DOSY) is one of the few NMR techniques that can be used for mixture separation [1]. It uses translational diffusion to effect a partial separation but requires that individual compounds have a substantial difference in their diffusion constants. In addition, the separation problem becomes more difficult when the component molecules have similar chemical functional group and overlap in their NMR spectra. Alternatively, blind source separation (BSS) is a technique that can be used to extract an individual NMR subspectrum from mixture spectra; further, if the molecules do not interact strongly — these individual subspectra are nearly identical to those of the corresponding pure compounds acquired under the same conditions [2,3]. BSS has been successfully demonstrated for separation using NMR [2,3]. However, application remains problematic for certain cases in which the data are of limited quality. Here, we present a new approach that can operate in the presence of phase or baseline imperfections in the NMR data which can affect the fidelity of the results when using the conventional BSS algorithm. Coined as blind iterative source identification (BISI), the method will be benchmarked against both conventional BSS and DOSY (Fig. 1). Our initial results showed that the reliability and quality of the results were improved greatly compared to previous methods (Fig.1).

We estimate parameters from the Katz and Thompson permeability model using laboratory complex electrical conductivity (CC) and nuclear magnetic resonance (NMR) data to build permeability models parameterized with geophysical measurements. We use the Katz and Thompson model based on the characteristic hydraulic length scale, determined from mercury injection capillary pressure estimates of pore throat size, and the intrinsic formation factor, determined from multisalinity conductivity measurements, for this purpose. Two new permeability models are tested, one based on CC data and another that incorporates CC and NMR data. From measurements made on forty‐five sandstone cores collected from fifteen different formations, we evaluate how well the CC relaxation time and the NMR transverse relaxation times compare to the characteristic hydraulic length scale and how well the formation factor estimated from CC parameters compares to the intrinsic formation factor. We find: (1) the NMR transverse relaxation time models the characteristic hydraulic length scale more accurately than the CC relaxation time ( of 0.69 and 0.33 and normalized root mean square errors (NRMSE) of 0.16 and 0.21, respectively); (2) the CC estimated formation factor is well correlated with the intrinsic formation factor (NRMSE=0.23). We demonstrate that that permeability estimates from the joint‐NMR‐CC model (NRMSE=0.13) compare favorably to estimates from the Katz and Thompson model (NRMSE=0.074). This model advances the capability of the Katz and Thompson model by employing parameters measureable in the field giving it the potential to more accurately estimate permeability using geophysical measurements than are currently possible.

Attenuated total reflection (ATR) Fourier transform infrared (FT-IR) and nuclear magnetic resonance (NMR) data are used to characterize the hydrogen bonding of the secondary amide N-H group of several structurally similar benzoyl derivatives of p-aminobenzoic acid esters (retinoids) in chloroform solution. The amide N-H can form intermolecular hydrogen bonds to several proton acceptors in these molecules or it can form an intramolecular hydrogen bond to a fluorine or oxygen atom in some of the molecules. The concentration dependence of the solution N-H infrared absorption bands is used to determine the formation of intramolecular and/or intermolecular H-bonds. Proton NMR spectra were obtained from deuterated chloroform solutions and the sec-amide N-H resonance was assigned for each compound. The downfield shift in the N-H resonance is correlated to intramolecular H-bond formation. Also, the NMR spectra of fluorine-containing compounds provide J(F-H) through-space coupling values. Using infrared and NMR data, the relative intramolecular hydrogen bond strengths (N-H...F or N-H...O) of the compounds are approximately ranked.

Efficient computation of three-dimensional protein structures in solution from nuclear magnetic resonance data using the program DIANA and the supporting programs CALIBA, HABAS and GLOMSA