Find research datasets worth reusing

opencc-by-4.0Jul 2021View details →

Data for "Detection of metabolite-protein interactions in complex biological samples by high-resolution relaxometry: towards interactomics by NMR"

Raw NMR data for relaxometry experiments, divided by donor sample. For every donor sample 2 or 3 different samples were used in order to record data at 19 different magnetic fields. Data from fast field-cycling relaxometry. All the data is in one xlsx file, divided by donor sample. Relaxometry results for alanine, lactate, creatinine and glutamine, obtained from the fitting of their relaxation decays recorded at 19 different fields, divided by donor sample.

opencc-by-4.0Sep 2009View details →

Titration_DB: NMR-monitored protein pH titration chemical shift data.

This archive contains a backup of the NMR titration_db database raw chemical shift data. Reference: D. Farrell et al., “Titration_DB: Storage and analysis of NMR-monitored protein pH titration curves.,” Proteins, vol. 78, no. 4, pp. 843–857, 2009. This data was formerly hosted on an interactive website which is no longer active. The files are stored as csv format, one per protein. They are also split into three folders according to the nuclei whose chemical shifts were used to detect the measurement (1H,13C or 15 N). Much of this data was obtained from literature. A table in the zip file (titration_dataset_info.csv) associates the protein name with the original paper.  Note that the database stored the pka values fit using a model to each residue dataset. This is not stored here. Also present is a file titdb.fs. This is a binary file that can only be opened using the PEATDB software. PEATDB (Protein Engineering Analysis Tool - Database) was an application for sharing, analysing and storing experimental and theoretical data from Protein Engineering experiments. This is now legacy software and no longer maintained. It was used to allow interactive fitting of the titration data using the Ekin module. It is still possible to install this software if you really need to. It is stored on github at https://github.com/dmnfarrell/peat. D. Farrell May 2019

opencc-by-4.0Aug 2015View details →

Critical Assessment of automated Structure Determination of Proteins by NMR

The community-wide initiative "Critical Assessment of Automated Structure Determination of Proteins by NMR (CASD-NMR)" was launched in 2009 to to evaluate the ability of automated methods to produce 3D protein structures from NMR data that closely match structures manually determined by experts. This dataset includes all the experimental data made available to the participants of CASD-NMR in the two completed rounds of the initiative. Also refer to http://www-nmr.cabm.rutgers.edu/blindtest/blind.html for additional details, including first release date and link to each final PDB entry

opencc-by-4.0Aug 2021View details →

2D NMR HSQC spectra of proteins and mouse urine with peaks picked by DEEP Picker

2D 15N-1H HSQC NMR spectra of Im7 and a-synuclein with peak lists produced by DEEP Picker. 2D 13C-1H HSQC NMR spectrum of mouse urine with peak lists determined by DEEP Picker.

opencc-by-4.0Dec 2020View details →

MD Data for Patterns in protein flexibility: a comparison of NMR "ensembles", MD trajectories and crystallographic B-factors

This data set comprises five zipped directories that contain the scripts and intermediate molecular dynamics (MD) results used in (initially as of April 24, 2017, updated with additional directories on December 15, 2020) a soon to be submitted paper, "Patterns in protein flexibility: a comparison of NMR 'ensembles', MD trajectories and crystallographic B-factors" written by the authors of this entry. An earlier version of this paper is available via BioRxiv, DOI: https://doi.org/10.1101/240655. This paper explores patterns in coordinate variance and coordinate uncertainty in MD trajectories and in protein structures derived from NMR and compares coordinate variances/uncertainties with those crystallographic B-factors. The files, MD_data.zip and MD_data2.zip, each unzip to contain input files and scripts for reproducing the MD trajectories used in this paper (using DESMOND): MD_data.zip contains input files/scripts for the MD trajectories used in the preprint; MD_data2.zip contains input files/scripts for trajectories ran following publication of the preprint. The file btab_analysis_scripts.zip contains key scripts for analyzing those trajectories (following file conversion with VMD and superimposition with THESEUS) in MATLAB (this analysis assumes the presence of the FindCore Toolbox, written by David Snyder and available via the MATLAB Central File Exchange, as well as the MATLAB Statistics and Machine Learning Toolbox). And the files, superimposed_MD_trajectories.zip and superimposed_MD_trajectories2.zip, each unzip to yield the trajectories (superimposed using THESEUS and in PDB multimodel file format) analyzed in the soon to be submitted paper: superimposed_MD_trajectories.zip contains trajectories reported in the preprint and superimposed_MD_trajectories2.zip contains the results of subsequent simulations.

opencc-by-4.0Nov 2024View details →

Supplementary data frames, AlphaFold models, Normal Mode Analysis (NMA) Data, and NMA of Corresponding NMR Ensembles in the S2RCI, MD, and S2 Datasets for "Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics"

<h1>Changes applied to V2</h1> In addition to the supplementary dataframes and AlphaFold models from each dataset in V1, V2 includes the additional data outlined below. The S2RCI and MD datasets include comprehensive analyses of AlphaFold2 models (both before and after truncation). These datasets feature:   <ul> <li>AlphaFold2 Models: Both original and truncated structures.  </li> <li>WEBnma Modes: `modes.txt` files generated from WEBnma analysis, available for both non-truncated and truncated AF2 models.  </li> <li>Root-Mean-Square-Fluctuations (RMSF): Profiles calculated before and after truncation of AF2 models.  </li> <li>NMR Data: Normal Mode Analysis (NMA): Performed on corresponding NMR ensembles (see below).  </li> </ul>   The NMR Data of NMA in these datasets includes:   <ul> <li>NMR ensembles  </li> <li>Individual NMR models extracted from each ensemble  </li> <li>STRIDE secondary structure calculations per-individual NMR models</li> <li>RMSF profiles per-individual NMR models</li> </ul> For detailed information, please refer to the `Readme.txt` file within each corresponding folder.   The S2 dataset includes all the features listed above, except for the NMR analysis.

opencc-by-4.0Jun 2024View details →

Triple-resonance NMR spectra of Tau 1-239 protein fragment acquired at 5 C, 10 C and 15 C

Dataset used in a paper "Using temperature coefficients to support resonance assignment of intrinsically disordered proteins" by Paulina Putko, Javier Agustin Romero, Christian F. Pantoja, Markus Zweckstetter, Krzysztof Kazimierczuk, and Anna Zawadzka-Kazimierczuk The following spectra have been collected: <table> <tbody> <tr> <td>Experiments T : 5 C</td> <td>NUS points (Recorded)</td> <td>NS (scans) (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> CBCA(CO)NH (HAHB)CBCA(CO)NH </td> <td>1000</td> <td> </td> </tr> <tr> <td>Experiments T : 10 C</td> <td>NUS points (Recorded)</td> <td>NS (scans) (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 T : 15 C</td> <td>NUS points (Recorded)</td> <td>NS (scans) (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>

opencc-by-4.0Sep 2024View details →

A combined NMR and Deep Neural Network approach for enhancing the spectral resolution of aromatic side chains in proteins

Nuclear magnetic resonance (NMR) spectroscopy has become an important technique in structural biology for characterising the structure, dynamics and interactions of macromolecules. While a plethora of NMR methods are now available to inform on backbone and methyl-bearing side-chains of proteins, a characterisation of aromatic side chains is more challenging and often requires specific labelling or 13C-detection. Here we present a deep neural network (DNN) named FID-Net-2, which transforms NMR spectra recorded on simple uniformly 13C labelled samples to yield high-quality 1H-13C correlation spectra of the aromatic side chains. Key to the success of the DNN is the design of a complementary set of NMR experiments that produce spectra with unique features to aid the DNN produce high-resolution aromatic 1H-13C correlation spectra with accurate intensities. The reconstructed spectra can be used for quantitative purposes as FID-Net-2 predicts uncertainties in the resulting spectra. We have validated the new methodology experimentally on protein samples ranging from 7 to 40 kDa in size. We demonstrate that the method can accurately reconstruct high resolution two-dimensional aromatic 1H-13C correlation maps, high resolution three-dimensional aromatic-methyl NOESY spectra to facilitate aromatic 1H-13C assignments, and that the intensities of peaks from the reconstructed aromatic 1H-13C correlation maps can be used to quantitatively characterise the kinetics of protein folding.  More generally, we believe that this strategy of devising new NMR experiments specifically for analysis using customised DNNs represents a substantial advance that will have a major impact on the study of molecules using NMR in the years to come.

opencc-by-4.0Jun 2023View details →

Combining Solid-State NMR with Structural and Biophysical Techniques to Design Challenging Protein-Drug Conjugates

Solid-state NMR spectra (DARR and NCA) of rehydrated freeze-dried free TTR and TTR in the presence of Tafamidis and Taf-PTX Reference citation:  Combining Solid-State NMR with Structural and Biophysical Techniques to Design Challenging Protein-Drug Conjugates. Angew Chem Int Ed Engl. 2023 Jun 5:e202303202. doi: 10.1002/anie.202303202. PMID: 37276329.

opencc-by-4.0May 2022View details →

alfa-synuclein spectra for the paper "Linear discriminant analysis reveals hidden patterns in NMR chemical shifts of intrinsically disordered proteins"

The experimental data for the paper  "Linear discriminant analysis reveals hidden patterns in NMR chemical shifts of intrinsically disordered proteins" - four spectra of an intrinsically disordered protein alfa-synuclein: 3D HNCO 4D HabCab(CO)NH 4D (H)N(CA)CONH 4D HNCACO All spectra were acquired using non-uniform sampling (schedules included as 'schedule.txt' files in a format of 'Kozminski' method in VnmrJ). 3D spectrum (HNCO_nuFT.ucsf) was processed using multidimensional Fourier transform (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'). 4D spectra were processed using sparse multidimensional Fourier transform (http://nmr.cent3.uw.edu.pl/software, program 'reduced'), based on a HNCO peak list ('peak.list' file). Files with processing parameters are included as 'parameters.txt' files. The resulting spectra are included as Sparky .ucsf files. The files for 4D spectra are the collections of 2D cross-sections with the cross-section number corresponding to the numbering of HNCO peaks in the 'peak.list' file. 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'.

opencc-by-4.0Sep 2019View details →

Simulated and experimental data distributed to the CASP13 participants in protein structure prediction assisted with sparse NMR data

All simulated and experimental data distributed to the CASP participants in protein structure prediction assisted with sparse NMR data in CASP13. Also available at http://predictioncenter.org/casp13/results.cgi?view=targets&model=first&tr_type=others&sub_type=N&groups_id=

opencc-by-4.0Jan 2023View details →

NMR Relaxometry data for publication "Engineered Nonviral Protein Cages Modified for MR Imaging"

NMRD profiles collected with field-cycling NMR relaxometry of a water solution of Gd-C4-IA, and of AaLS-13 and OP protein cages labeled with the gadolinium(III) complex. Published in: https://doi.org/10.1021/acsabm.2c00892 NMRD data acquisition and analysis was performed with the support of the PRIN 2017A2KEPL project “Rationally designed nanogels embedding paramagnetic ions as MRI. probes”, and the European Commision through H2020 FET-Open project HIRES-MULTIDYN award no. 899683 and H2020 INFRAIA iNEXT-Discovery (Structural Biology Research Infrastructures for Translational Research and Discovery) award no. 871037.

opencc-by-4.0Mar 2022View details →

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

Solid-state NMR spectra used in the analysis of the interaction between PD-L1 and anti-PDL1 fusion protein. Reference paper: Epitope mapping and binding assessment by solid-state NMR provide a  way for development of biologics under the Quality by Design paradigm

opencc-by-4.0Nov 2020View details →

Accurate and Cost-Effective NMR Chemical Shift Predictions for Proteins using Molecules-in-Molecules Fragmentation-Based Method

Files and coordinates associated with the paper titled 'Accurate and Cost-Effective NMR Chemical Shift Predictions for Proteins  using Molecules-in-Molecules Fragmentation Based Method. '

opencc-by-4.0Jan 2019View details →

Data for the "Systems NMR: simultaneous quantification of RNA, protein, and metabolite reaction dynamics for biomolecular network analysis."

This dataset contains raw NMR data used in the publication. Detailed protocol for the presented NMR setup and analysis is included in the publication, and at https://github.com/systemsnmr/ivtnmr. v0.2 includes the integr_results_31P_pure_PO4.txt files - phosphate-spectra integration files which were missing in v0.1 submission.

opencc-by-4.0Aug 2024View details →

Supporting data for "Reengineering of a Carotenoid-binding Protein Based on NMR Structure"

List of PDB codes used for MPNN benchmarking, parameters for AXT simulations, molecular dynamics trajectories and domain swapping predictions.

opencc-by-4.0Nov 2022View details →

Atomic-Resolution Structure of the Protein Encoded by Gene V of fd Bacteriophage in Complex with Viral ssDNA Determined by Magic-Angle Spinning Solid-State NMR

F-specific filamentous phages, elongated particles with circular single-stranded DNA encased in a symmetric protein capsid, undergo an intermediate step, where thousands of homodimers of a non-structural protein, gVp, bind to newly synthesized strands of DNA, preventing further DNA replication and preparing the circular genome in an elongated conformation for assembly of a new virion structure at the membrane. While the structure of the free homodimer is known, the ssDNA-bound conformation has yet to be determined. We report an atomic-resolution structure of the gVp monomer bound to ssDNA of fd phage in the nucleoprotein complex elucidated via Magic-Angle Spinning solid-state NMR. The model presents significant conformational changes with respect to the free form. These modifications facilitate the binding mechanism and possibly promote cooperative binding in the assembly of the gVp-ssDNA complex. The raw NMR data used for structure determination are uploaded as original Bruker directories from topspin version 3.5. Processing details are given in the supporting Information of the manuscript. PDB ID is 8ACZ. BMRB accession number is 51391.