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31 results for “NMR spectra”
2D NMR HSQC spectra of proteins and mouse urine with peaks picked by DEEP Picker
<p>2D 15N-1H HSQC NMR spectra of Im7 and a-synuclein with peak lists produced by DEEP Picker.</p> <p>2D 13C-1H HSQC NMR spectrum of mouse urine with peak lists determined by DEEP Picker.</p>
1H NMR spectra of commercial honey from 400 and 700 MHz spectrometers and tables with data after processing and binning
<p>Datasets contain the 1H NMR original raw spectral data (Bruker format) of commercial honey from 400 MHz and 700 MHz NMR spectrometers and Tables (.xlsx) with data after processing and binning.</p> <p> </p> <p> </p>
Triple-resonance NMR spectra of Tau 1-239 protein fragment acquired at 5 C, 10 C and 15 C
<p>Dataset used in a paper "Using temperature coefficients to support resonance<br>assignment of intrinsically disordered proteins" by Paulina Putko, Javier Agustin Romero, Christian F. Pantoja, Markus<br>Zweckstetter, Krzysztof Kazimierczuk, and Anna Zawadzka-Kazimierczuk</p> <p>The following spectra have been collected:</p> <table> <tbody> <tr> <td>Experiments<br>T : 5 C</td> <td>NUS points<br>(Recorded)</td> <td>NS (scans)<br>(Recorded)</td> </tr> <tr> <td>HNCO</td> <td>600</td> <td>4</td> </tr> <tr> <td>HN(CA)CO</td> <td>1000</td> <td>16</td> </tr> <tr> <td>HNCA</td> <td>600</td> <td>8</td> </tr> <tr> <td>HN(CO)CA</td> <td>450</td> <td>8</td> </tr> <tr> <td> <p>CBCA(CO)NH</p> <p>(HAHB)CBCA(CO)NH</p> </td> <td>1000</td> <td> </td> </tr> <tr> <td>Experiments<br>T : 10 C</td> <td>NUS points<br>(Recorded)</td> <td>NS (scans)<br>(Recorded)</td> </tr> <tr> <td>HNCO</td> <td>600</td> <td>4</td> </tr> <tr> <td>HN(CO)CA</td> <td>450</td> <td>8</td> </tr> <tr> <td>(HAHB)CBCA(CO)NH</td> <td>1000</td> <td>8</td> </tr> <tr> <td>Experiments<br>T : 15 C</td> <td>NUS points<br>(Recorded)</td> <td>NS (scans)<br>(Recorded)</td> </tr> <tr> <td>HNCO</td> <td> 600</td> <td> 4</td> </tr> <tr> <td>HN(CO)CA</td> <td> 450</td> <td> 8</td> </tr> <tr> <td>(HAHB)CBCA(CO)NH</td> <td> 1000</td> <td> 8</td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table>
NMR spectra for ambocidin A, A1, B and B1.
<p>This dataset contains raw NMR spectra for the calcium dependent lipopeptide antibiotics ambocidin A, A1, B and B1. Zipped folders contain FIDs for the compounds indicated in the file name. Discovery of these compounds is described in<em> Calcium-Dependent Lipopeptide Antibiotics against Drug-Resistant Pathogens Discovered via Host-Dependent Heterologous Expression of a Cloned Biosynthetic Gene Cluster,</em> Angew. Chem. Int. Ed. 2024, e202410286.</p>
FTIR and NMR spectra of impurified and purified acrylonitrile (AN) (Original data)
<p>This dataset contains FTIR and NMR spectra for the successful purification of acrylonitrile (AN) monomer by a simple column technique which is related to our published paper with DOI 10.3390/polym13040660</p> <p>1. Fig. 2a FTIR non-Pure AN (httpswebbook.nist.govcgicbook.cgiID=C107131&Units=SI&Mask=80#IR-Spec)<br> 2. FTIR_ Pure AN_ Raw Data (Excel)<br> 3. SigmaPlot FTIR_ Pure AN_ Raw Data<br> 4. Pure AN_1H (1)<br> 5. Pure AN_1H (2)<br> 6. Pure AN_13C(1)<br> 7. Pure AN_13C (2)<br> 8. Non Pure AN_1H (1)<br> 9. Non Pure AN_1H (2)<br> 10. Non Pure AN_13C (1)<br> 11. Non Pure AN_13C (2)</p> <p>12. Figure (1) -A schematic diagram of a simple column's advantages versus the distillation technique's drawbacks</p> <p>13. Figure (6)- Experimental setup of monomer purification procedure</p> <p>14. Editing Figures (1) & (6)</p> <p>15. Fig. 3. Chemial Structure of AN, AA, and MeHQ</p> <p>16. Related research paper dataset</p> <p> </p> <p>In fact, As known, purification is of the utmost significance in any chemistry process. Besides, eliminating impurities enhances the quality and standard of the product. The primary method for monomer purification, like acrylonitrile (AN), is the distillation technique. However, this technique is unsafe and hard to set up or handle. A straightforward, risk-free, and low-cost method like the column technique resolves these issues. Therefore, the data will be helpful in numerous applications, especially in polymerization reactions that require the removal of inhibitors before the polymerization initiation process.</p>
nmr spectra for paper
<p><a href="https://www.nmrium.org/nmrium#?toc=https%3A%2F%2Fzenodo.org%2Frecords%2F10403692%2Ffiles%2FManuscript.json">https://www.nmrium.org/nmrium#?toc=https%3A%2F%2Fzenodo.org%2Frecords%2F10403692%2Ffiles%2FManuscript.json</a></p> <p><a href="https://www.nmrium.org/nmrium#?toc=https%3A%2F%2Fzenodo.org%2Frecords%2F10403692%2Ffiles%2FSupporting_Info.json">https://www.nmrium.org/nmrium#?toc=https%3A%2F%2Fzenodo.org%2Frecords%2F10403692%2Ffiles%2FSupporting_Info.json</a></p>
NMR spectra of A-rich oligonucleotides that pause polymerase progression
<p>1D proton NMR spectra of HHV6A-A and HPV18b-A in lithium cacodylate buffer with and without 100 mM KCl, recorded at 25 °C and 0 °C on 600 MHz NMR spectrometer.</p>
alfa-synuclein spectra for the paper "Linear discriminant analysis reveals hidden patterns in NMR chemical shifts of intrinsically disordered proteins"
<p>The experimental data for the paper "Linear discriminant analysis<br> reveals hidden patterns in NMR chemical shifts of intrinsically<br> disordered proteins" - four spectra of an intrinsically disordered<br> protein alfa-synuclein:</p> <p>3D HNCO</p> <p>4D HabCab(CO)NH</p> <p>4D (H)N(CA)CONH</p> <p>4D HNCACO</p> <p>All spectra were acquired using non-uniform sampling (schedules included<br> as 'schedule.txt' files in a format of 'Kozminski' method in VnmrJ). 3D<br> spectrum (HNCO_nuFT.ucsf) was processed using multidimensional Fourier transform<br> (http://nmr.cent3.uw.edu.pl/software, program 'toastd'). Additionally, we provide a spectrum cleaned of NUS artifacts (HNCO_artifacts_cleaned.ucsf) using program handy (http://nmr.cent3.uw.edu.pl/software, program 'handy').</p> <p>4D spectra were processed using sparse multidimensional Fourier transform<br> (http://nmr.cent3.uw.edu.pl/software, program 'reduced'), based on a<br> HNCO peak list ('peak.list' file). Files with processing parameters are<br> included as 'parameters.txt' files.</p> <p>The resulting spectra are included as Sparky .ucsf files. The files for<br> 4D spectra are the collections of 2D cross-sections with the<br> cross-section number corresponding to the numbering of HNCO peaks in the<br> 'peak.list' file.</p> <p>The folder 'Application2_assignment_transfer' contains Sparky 'ucsf' and 'save' files of the two-dimensional NH projection of the HNCO spectrum with the experimental (HSQC_exp.list) and BMRB (HSQC_bmrb.list) peak lists read into the spectrum. The experimental peaks are marked with arbitrary numbers and BMRB peaks - with names of the preceding aa-residues. The correct assignment is shown in the file 'assignment.txt'.</p>
Data set related to the manuscript "Investigating the effect of particle size distribution and complex exchange dynamics on NMR spectra of ions diffusing in disordered porous carbons through a mesoscopic model"
<p>Graphical files in the agr format for all the figures in the manuscript entitled "Investigating the effect of particle size distribution and complex exchange dynamics on NMR spectra of ions diffusing in disordered porous carbons through a mesoscopic model". XYZ files giving the particles and bulk positions in the lattices are also provided.</p>
Datasets for "Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning"
<p>This upload contains the datasets used for the experiments in:</p> <p>Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning</p> <p>Frank Hu, Michael S. Chen, Grant M. Rotskoff, Matthew W. Kanan, and Thomas E. Markland</p> <p>https://arxiv.org/abs/2408.08284</p> <p> </p> <p>For file descriptions and usage, please refer to the supplied README.md file.</p> <p> </p> <p> </p>
Data set related to the manuscript "Mesoscopic simulations of the in situ NMR spectra of porous carbon based supercapacitors: Electronic structure and adsorbent reorganisation effects"
<p>Graphical files in the agr format for all the figures in the manuscript entitled "Mesoscopic simulations of the in situ NMR spectra of porous carbon based supercapacitors: Electronic structure and adsorbent reorganisation effects". Examples of input files for the lattice simulations are also provided.</p>
Solid-state NMR spectra of the complex of PD-L1 with the anti-PD-L1 fusion protein and of the mixture of PD-L1 with a non-binding mAb
<p>Solid-state NMR spectra used in the analysis of the interaction between PD-L1 and anti-PDL1 fusion protein.</p> <p>Reference paper: <strong>Epitope mapping and binding assessment by solid-state NMR provide a way for development of biologics under the Quality by Design paradigm</strong></p>
Raw data and scripts for "Non-uniform sampling of similar NMR spectra and its application to studies of the interaction between alpha-synuclein and liposomes" by Shchukina et al.
<p>A series of 15N HSQC spectra of aSyn at temperatures 15,17..43C acquired with and without the addition of POPG-based liposomes. The spectra can be processed with sparse undersampling at various levels (scripts are provided).</p>
Data set related to the manuscript "Investigating particle size effects on NMR spectra of ions diffusing in porous carbons through a mesoscopic model"
<p>Graphical files in the agr format for all the figures in the manuscript entitled "Investigating particle size effects on NMR spectra of ions diffusing in porous carbons through a mesoscopic model". Examples of input files for the lattice simulations are also provided.</p>
Silicon-29 NMR Experimental Datasets used in Statistical Learning of NMR tensors from 2D Isotropic/Anisotropic Correlation Nuclear Magnetic Resonance Spectra
<p>Processed silicon-29 Magic-Angle Flipping and Magic-Angle Turning Nuclear Magnetic Resonance spectra used as input to the smooth-LASSO linear inversion algorithm along with their corresponding NMR tensor parameter distributions as described in the paper "Statistical Learning of NMR tensors from 2D Isotropic/Anisotropic Correlation Nuclear Magnetic Resonance Spectra", by Srivastava and Grandinetti. </p> <p>Files with names containing "MAF" or "MAT" are the corresponding experimental Si-29 NMR MAF or MAT dataset on the composition given in the filename. Files with names containing "inverse" are the corresponding NMR tensor parameter distributions obtained from the inversion of the corresponding experimental MAF and MAT dataset with the composition given in the filename. </p> <p><br> </p> <p>Details of the csdf dataset format are given in <a href="https://doi.org/10.1371/journal.pone.0225953"><em>PLOS ONE,</em> 15(1): e0225953 (2020)</a>, "Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data," D. Srivastava, T. Vosegaard, D. Massiot, and P.J. Grandinetti. The data within csdf files can be accessed with the Python package <a href="https://csdmpy.readthedocs.io/en/stable">csdmpy</a>, or other CSDM-compliant software.</p> <p> </p>
1D and 2D NMR spectra of coffee from 27 countries
<p>715 green coffee samples were gathered by Almacafé S.A. (Bogotá, Colombia) between 2012 and 2014 from 27 different countries and analyzed at the Nuclear Magnetic Resonance (NMR) laboratory at Universidad del Valle (Cali, Colombia). Over 1000 methanolic coffee extracts were prepared and a total of 4563 spectra were acquired in fully automatic mode using a 400MHz NMR spectrometer (Bruker Biospin, Germany). The dataset spans the variance that could be expected for an industrial application of origin monitoring, including samples from different harvest time, collected over several years and processed by at least two distinct operators. The resulting 1D and 2D spectra can be used to develop and evaluate feature extraction methods, multivariate algorithms and automation monitoring techniques, as a dataset for teaching, or as a reference for new studies on similar samples and approach.</p>
NMR spectra of Cephalotes ants gut and cuticle
<p>Across the evolutionary history of insects, the shift from nitrogen-rich carnivore/omnivore diets to nitrogen-poor herbivorous diets was made possible through symbiosis with microbes. The herbivorous turtle ants <i>Cephalotes</i> possess a conserved gut microbiome which enriches the nutrient composition by recycling nitrogen-rich metabolic waste to increase the production of amino acids. This enrichment is assumed to benefit the host, but we do not know to what extent. To gain insights into nitrogen assimilation in the ant cuticle we use gut bacterial manipulation, <sup>15</sup>N isotopic enrichment, isotope-ratio mass spectrometry, and <sup>15</sup>N nuclear magnetic resonance spectroscopy to demonstrate that gut bacteria contribute to the formation of proteins, catecholamine cross-linkers, and chitin in the cuticle. This study identifies the cuticular components which are nitrogen-enriched by gut bacteria highlighting the role of symbionts in insect evolution and provides a framework for understanding the nitrogen flow from nutrients through bacteria into the insect cuticle.</p>
NMR spectra of single-stranded and dimeric oligonucleotide constructs containing oxidative lesions
<p>NMR spectra of single-stranded (g- prefix) and dimeric (m- prefix) oligonucleotide constructs containing 8-oxoguanine (X) and dSpacer (S) lesions. Spectra were recorded on 600 and 800 MHz spectrometers. Oligonucleotides were dissolved in 50 mM KCl, 10 mM potassium phosphate buffer at pH 7, in 10% 2H2O.</p>
Primary data of X-ray data and NMR spectra
<p>Primary data of X-ray data and NMR spectra.</p>
Impact of noise on inverse design: The case of NMR spectra matching
<p>Code and data for the corresponding preprint:</p> <p>Impact of noise on inverse design: The case of NMR spectra matching<br> Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld</p> <p>https://doi.org/10.48550/arXiv.2307.03969</p> <p><br> SI_DFT_geo.xyz and SI_DFT_NMR.txt belong to the QM9NMR paper<br> Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules<br> Amit Gupta, Sabyasachi Chakraborty and Raghunathan Ramakrishnan<br> Mach. Learn.: Sci. Technol. 2 (2021) 035010</p> <p>originally hosted here:<br> https://moldis-group.github.io/qm9nmr/<br> https://doi.org/10.17172/NOMAD/2021.10.16-1</p>
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