Reference-free NOE NMR analysis.
Ontology highlight
ABSTRACT:
SUBMITTER: Koos MRM
PROVIDER: S-EPMC8162118 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC4184813 | biostudies-literature
Project description:We introduce AUDANA (Automated Database-Assisted NOE Assignment), an algorithm for determining three-dimensional structures of proteins from NMR data that automates the assignment of 3D-NOE spectra, generates distance constraints, and conducts iterative high temperature molecular dynamics and simulated annealing. The protein sequence, chemical shift assignments, and NOE spectra are the only required inputs. Distance constraints generated automatically from ambiguously assigned NOE peaks are validated during the structure calculation against information from an enlarged version of the freely available PACSY database that incorporates information on protein structures deposited in the Protein Data Bank (PDB). This approach yields robust sets of distance constraints and 3D structures. We evaluated the performance of AUDANA with input data for 14 proteins ranging in size from 6 to 25 kDa that had 27-98 % sequence identity to proteins in the database. In all cases, the automatically calculated 3D structures passed stringent validation tests. Structures were determined with and without database support. In 9/14 cases, database support improved the agreement with manually determined structures in the PDB and in 11/14 cases, database support lowered the r.m.s.d. of the family of 20 structural models.
| S-EPMC4921114 | biostudies-literature
Project description:Heteronuclear cross polarization (CP) has been commonly used to enhance the sensitivity of dilute low-γ nuclei in almost all solid-state NMR experiments. However, CP relies on heteronuclear dipolar couplings, and therefore the magnetization transfer efficiency becomes inefficient when the dipolar couplings are weak, as is often the case for mobile components in solids. Here, we demonstrate methods that combine CP with heteronuclear Overhauser effect (referred to as CP-NOE) or with refocused-INEPT (referred to as CP-RINEPT) to overcome the efficiency limitation of CP and enhance the signal-to-noise ratio (S/N) for mobile components. Our experimental results reveal that, compared to the conventional CP, significant S/N ratio enhancement can be achieved for resonances originating from mobile components, whereas the resonance signals associated with rigid groups are not significantly affected due to their long spin-lattice relaxation times. In fact, the S/N enhancement factor is also dependent on the temperature, CP contact time as well as on the system under investigation. Furthermore, we also demonstrate that CP-RINEPT experiment can be successfully employed to independently detect mobile and rigid signals in a single experiment without affecting the data collection time. However, the resolution of CP spectrum obtained from the CP-RINEPT experiment could be slightly compromised by the mandatory use of continuous wave (CW) decoupling during the acquisition of signals from rigid components. In addition, CP-RINEPT experiment can be used for spectral editing utilizing the difference in dynamics of different regions of a molecule and/or different components present in the sample, and could also be useful for the assignment of resonances from mobile components in poorly resolved spectra. Therefore, we believe that the proposed approaches are beneficial for the structural characterization of multiphase and heterogeneous systems, and could be used as a building block in multidimensional solid-state NMR experiments.
| S-EPMC4851575 | biostudies-literature
Project description:MotivationA prerequisite for any protein study by NMR is the assignment of the resonances from the (15)N-(1)H HSQC spectrum to their corresponding atoms of the protein backbone. Usually, this assignment is obtained by analyzing triple resonance NMR experiments. An alternative assignment strategy exploits the information given by an already available 3D structure of the same or a homologous protein. Up to now, the algorithms that have been developed around the structure-based assignment strategy have the important drawbacks that they cannot guarantee a high assignment accuracy near to 100%.ResultsWe propose here a new program, called NOEnet, implementing an efficient complete search algorithm that ensures the correctness of the assignment results. NOEnet exploits the network character of unambiguous NOE constraints to realize an exhaustive search of all matching possibilities of the NOE network onto the structural one. NOEnet has been successfully tested on EIN, a large protein of 28 kDa, using only NOE data. The complete search of NOEnet finds all possible assignments compatible with experimental data that can be defined as an assignment ensemble. We show that multiple assignment possibilities of large NOE networks are restricted to a small spatial assignment range (SAR), so that assignment ensembles, obtained from accessible experimental data, are precise enough to be used for functional proteins studies, like protein-ligand interaction or protein dynamics studies. We believe that NOEnet can become a major tool for the structure-based backbone resonance assignment strategy in NMR.AvailabilityThe NOEnet program will be available under: http://www.icsn.cnrs-gif.fr/download/nmr.
| S-EPMC2642640 | biostudies-literature
Project description:SummaryPangenomes are replacing single reference genomes as the definitive representation of DNA sequence within a species or clade. Pangenome analysis predominantly leverages graph-based methods that require computationally intensive multiple genome alignments, do not scale to highly complex eukaryotic genomes, limit their scope to identifying structural variants (SVs), or incur bias by relying on a reference genome. Here, we present PanKmer, a toolkit designed for reference-free analysis of pangenome datasets consisting of dozens to thousands of individual genomes. PanKmer decomposes a set of input genomes into a table of observed k-mers and their presence-absence values in each genome. These are stored in an efficient k-mer index data format that encodes SNPs, INDELs, and SVs. It also includes functions for downstream analysis of the k-mer index, such as calculating sequence similarity statistics between individuals at whole-genome or local scales. For example, k-mers can be "anchored" in any individual genome to quantify sequence variability or conservation at a specific locus. This facilitates workflows with various biological applications, e.g. identifying cases of hybridization between plant species. PanKmer provides researchers with a valuable and convenient means to explore the full scope of genetic variation in a population, without reference bias.Availability and implementationPanKmer is implemented as a Python package with components written in Rust, released under a BSD license. The source code is available from the Python Package Index (PyPI) at https://pypi.org/project/pankmer/ as well as Gitlab at https://gitlab.com/salk-tm/pankmer. Full documentation is available at https://salk-tm.gitlab.io/pankmer/.
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Project description:The downscaling of NMR tensorial interactions, such as dipolar couplings, from tens of kilohertz to a few hertz in low-order media is the result of dynamics spanning several orders of magnitudes, including vibrational modes (~ns-fs), whole-molecule reorientation (~ns) and higher barrier internal conformational exchange (<ms). In this work, we propose to employ these dynamically averaged interactions to drive an "alignment-tensor-free" molecular dynamic simulation with orientation constraints (MDOC) in order to efficiently access the conformational space sampled by flexible small molecules such as natural products. Key to this approach is the application of tensorial pseudo-force restraints which simultaneously guide the overall reorientation and conformational fluctuations based on defined memory function over the running trajectory. With the molecular mechanics force-field, which includes bond polarization theory (BPT), and complemented with other available NMR parameters such as NOEs and scalar J-couplings, MDOC efficiently arrives at dynamic ensembles that reproduce the entire NMR dataset with exquisite accuracy and theoretically reveal the systems conformational space and equilibrium. The method as well as its potential towards configurational elucidation is presented on diastereomeric pairs of flexible molecules: a small 1,4-diketone 1 with a single rotatable bond as well as a 24-ring macrolide related to the natural product mandelalide A 2.
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Project description:We propose that the NMR solvent signal be utilized as a universal concentration reference because most solvents can be observed by NMR and solvent concentrations can be readily calculated or determined independently. In particular, a highly protonated solvent such as water can serve as a primary concentration standard for its stability, availability, and ease of observation. The potential problems of radiation damping associated with a strong NMR signal can be alleviated by small pulse angle excitation. The solvent signal then can be detected by the NMR receiver with the same efficiency as a dilute analyte. We demonstrated that the analyte's proton concentration can be accurately determined from 4 microM to more than 100 M, referenced by solvent (water) protons of concentrations more than 10 M. The proposed method is robust and indifferent to probe tuning and does not require any additional concentration standard.
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Project description:MotivationRecently there has been increasing interest in the effects of cell mixture on the measurement of DNA methylation, specifically the extent to which small perturbations in cell mixture proportions can register as changes in DNA methylation. A recently published set of statistical methods exploits this association to infer changes in cell mixture proportions, and these methods are presently being applied to adjust for cell mixture effect in the context of epigenome-wide association studies. However, these adjustments require the existence of reference datasets, which may be laborious or expensive to collect. For some tissues such as placenta, saliva, adipose or tumor tissue, the relevant underlying cell types may not be known.ResultsWe propose a method for conducting epigenome-wide association studies analysis when a reference dataset is unavailable, including a bootstrap method for estimating standard errors. We demonstrate via simulation study and several real data analyses that our proposed method can perform as well as or better than methods that make explicit use of reference datasets. In particular, it may adjust for detailed cell type differences that may be unavailable even in existing reference datasets.Availability and implementationSoftware is available in the R package RefFreeEWAS. Data for three of four examples were obtained from Gene Expression Omnibus (GEO), accession numbers GSE37008, GSE42861 and GSE30601, while reference data were obtained from GEO accession number GSE39981.Contactandres.houseman@oregonstate.eduSupplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Transcriptome of long-lived naked-mole rat (NMR) oligodendrocyte progenitor cells (OPCs) were compared with OPCs of other species.
2022-03-31 | E-MTAB-9160 | biostudies-arrayexpress
Project description:Staphylococcal protein A (SpA) is a virulence factor from Staphylococcus aureus that is able to bind to immunoglobulins. The 3D structures of its immunoglobulin (Ig) binding domains have been extensively studied by NMR and X-ray crystallography, and are often used as model structures in developing de novo or ab initio strategies for predicting protein structure. These small three-helix-bundle structures, reported in free proteins or Ig-bound complexes, have been determined previously using medium- to high-resolution data. Although the location and relative orientation of the three helices in most of these published 3D domain structures are consistent, there are significant differences among the reported structures regarding the tilt angle of the first helix (helix 1). We have applied residual dipolar coupling data, together with nuclear Overhauser enhancement and scalar coupling data, in refining the NMR solution structure of an engineered IgG-binding domain (Z domain) of SpA. Our results demonstrate that the three helices are almost perfectly antiparallel in orientation, with the first helix tilting slightly away from the other two helices. We propose that this high-accuracy structure of the Z domain of SpA is a more suitable target for theoretical predictions of the free domain structure than previously published lower-accuracy structures of protein A domains.
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