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opencc-by-4.0Nov 2021View details →

Computational Insights into the Unfolding of a Destabilized Superoxide Dismutase 1 Mutant

This data accompanies the article entitled Computational Insights into the Unfolding of a Destabilized Superoxide Dismutase 1 Mutant and published in Biology. SOD1_WT_I35A_REST2.zip: The zip archive includes REST2 trajectories for the two SOD1 constructs and the two force fields investigated in the paper. The trajectories are saved in the GROMACS XTC file format, separately for each temperature (i=0,...,23). Owing to the considerable trajectory sizes, only protein coordinates are reported, and the output frequency is reduced to 100 ps. The initial geometry (in the Gromos87 GRO format) after a short relaxation is provided for each REST2 simulation (conf_prot.gro). Furthermore, for each REST2 simulation, an xarray (http://xarray.pydata.org) dataset, saved in the netCDF file format, is included and contains the following observables: fraction of native contacts relative to the crystal structure, secondary-structure content (i.e., the fraction of protein residues found in an alpha-helix, beta-sheet, beta-bridge, or a turn), as well as the number of residues with the beta-sheet secondary structure per each beta-strand and beta-sheet of the SOD1 barrel.

opencc-by-4.0Nov 2020View details →

Surveyance of apparent unfolding curves via thiol labelling

Dataset contains raw and preprocessed data for fluorescence and proteomic studies respectively. In each case, protein foldedness was probed using thiol reactivity. The raw mass spectrometry proteomics data have also been deposited to the ProteomeXchange Consortium via the PRIDE partner repository, with the dataset identifiers PXD022587, PXD022640 and PXD030567.

opencc-by-4.0Nov 2019View details →

Pythia/Herwig + Delphes Jet Datasets for OmniFold Unfolding

Datasets of QCD jets used for studying unfolding in <a href="https://arxiv.org/abs/1911.09107">OmniFold: A Method to Simultaneously Unfold All Observables</a>. Four different datasets are present: <ul> <li>Herwig 7.1.5 with the default tune</li> <li>Pythia 8.243 with tune 21 (ATLAS A14 central tune with NNPDF2.3LO)</li> <li>Pythia 8.243 with tune 25 (ATLAS A14 variation 2+ of tune 21)</li> <li>Pythia 8.243 with tune 26 (ATLAS A14 variation 2- of tune 21)</li> </ul> $Z $ + jet events (with the $Z $ set to be stable) were generated for each of the above generator/tune pairs with the $Z$ boson $\hat p_{T}^\text{min}>150\,\text{GeV} $ and $\sqrt{s}=14\,\text{TeV}$. Events were then passed through the Delphes 3.4.2 fast detector simulation of the CMS detector. Jets with radius parameter $R=0.4$ were found with the anti-$k_T$ algorithm at both particle level ("gen"), where all non-neutrino, non-$Z$ particle are used, and detector level ("sim"), where reconstructed energy flow objects (tracks, electromagnetic calorimeter cells, and hadronic calorimeter cells) are used. Only jets with transverse momentum greater than $10\,\text{GeV}$ are kept (note that sim jets have a simple jet energy correction applied by Delphes). The hardest jet from events with a $Z$ boson with a final transverse momentum of $200\,\text{GeV}$ or greater are kept, yielding approximately 1.6 million jets at both gen-level and sim-level for each generator/tune pair. Each zipped NumPy file consists of several arrays, the names of which begin with either 'gen_' or 'sim_' depending on which set of jets they correspond to. The name of each array ends in a key word indicating what it contains. With the exception of 'gen_Zs' (which contains the $(p_T,\,y,\,\phi)$ of the final $Z$ boson), there is both a gen and sim version of each array. The included arrays are (listed by their key words): <ul> <li>'jets' - The jet axis four vector, as $(p_T^\text{jet},\,y^\text{jet},\,\phi^\text{jet},\,m^\text{jet})$ where $y^\text{jet}$ is the jet rapidity, $\phi^\text{jet}$ is the jet azimuthal angle, and $m^\text{jet}$ is the jet mass.</li> <li>'particles' - The (rescaled, translated) constituents of the jets as $(p_T/100,\,y-y^\text{jet},\,\phi-\phi^\text{jet},\,f_\text{PID})$ where $f_\text{PID}$ is a small float corresponding to the PDG ID of the particle. The PIDs are remapped according to $22\to0.0,\,211\to0.1,\,-211\to0.2,$ $130\to0.3,\,11\to0.4,\,-11\to0.5,\,13\to0.6,\,-13\to0.7,\,321\to0.8,\,-321\to0.9,$ $2212\to1.0,\,-2212\to1.1,\,2112\to1.2,\,-2112\to1.3$. Note that ECAL cells are treated as photons (id 22) and HCAL cells are treated as $K_L^0$ (id 130). </li> <li>'mults' - The constituent multiplicity of the jet.</li> <li>'lhas' - The Les Houches ($\beta=1/2$) angularity.</li> <li>'widths' - The jet width ($\beta=1$ angularity).</li> <li>'ang2s' - The $\beta=2$ angularity (note that this is very similar to the jet mass, but does not depend on particle masses).</li> <li>'tau2s' - The 2-subjettiness with $\beta=1$.</li> <li>'sdms' - The groomed mass with Soft Drop parameters $z_\text{cut}=0.1$ and $\beta=0$.</li> <li>'zgs' - The groomed momentum fraction (same Soft Drop parameters as above).</li> </ul> If you use this dataset, please cite this Zenodo record as well as the corresponding paper: <ul> <li>A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, J. Thaler, OmniFold: A Method to Simultaneously Unfold All Observables, <a href="https://arxiv.org/abs/arXiv:1911.09107">arXiv:1911.09107</a>.</li> </ul> The datasets can be downloaded and read into python automatically using the <a href="https://energyflow.network/docs/datasets/#z-jets-with-delphes-simulation">EnergyFlow Python package</a>.

opencc-by-4.0Oct 2024View details →

Dataset and code for article "Explicit description of viral capsid subunit shapes by unfolding dihedrons"

This repository contains data and codes used in a paper "Explicit description of viral capsid subunit shapes by expanding dihedrons" by Toyooka et al. - [grasshopper/] contains a GH file used in the 3D CAD Rhinoceros/Grasshopper. - [docking_axes_viper/] contains notebooks for aligning the subunit coordinates with the VIPER coordinate system (Z-axis is the 2-fold axis, 3-fold axis is on the X-axis, and 5-fold axis is on the Y-axis) - [docking_pairwise/] contains input scipts and analysis notebooks for pairwise docking simulations for subunits   - [1stm_zdock_iter/df.jld2] data containing top docking scores and poses, and screw motion parameters   - [1stm_zdock_iter/rmsd.jld2] RMSD data of docking poses with regard to the experimental structure  - [1stm_zdock_iter/run.sh] is a batch script for conducting docking simulations with ZDOCK   - [1stm_zdock_iter/run_each.sh] is a script for a single docking simulation with a specified random seed. Called from `run.sh`   - [1stm_zdock_iter/skrew_parameters.ipynb] analyzes the result of docking simulations. Sort the results accoring to docking scores and find the skrew axis of each docking pose   - [1stm_zdock_iter/visualize.ipynb] plots the docking score and the screw axis parameters, and also calculates and plots RMSDs from the reference structure (whole-shell structure)   - [1stm_zdock_iter/ref.pdb] is a sympolic link to a refrence structure (whole-shell structure)   - [4v4m_zdock_iter/] PDB ID: 4V4M   - [6s44_zdock_iter/] PDB ID: 6S44   - [7odw_zdock_iter/] PDB ID: 7ODW   - [3r0r_zdock_iter/] PDB ID: 3R0R   - [5zju_zdock_iter/] PDB ID: 5ZJU   - [1vb4_zdock_iter/] PDB ID: 1VB4   - [1m1c_zdock_iter/] PDB ID: 1M1C   - [6r7m_zdock_iter/] PDB ID: 6R7M   - [2m99_zdock_iter/] PDB ID: 2M99

dryad40/100

Data from: The increased effect of spring leaf unfolding on autumn senescence in the northern and southern hemispheres

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publicDec 2025View details →

opencc-by-4.0Feb 2015View details →

Figure 29. Lonchaea hackmani, prothorax unfolding during a lunge, still from Film 13 in Diverse mechanisms of feeding and movement in Cyclorrhaphan larvae (Diptera)

Figure 29. Lonchaea hackmani, prothorax unfolding during a lunge, still from Film 13.

opencc-zeroJul 2015View details →

Molecular dynamics simulations of the temperature-induced unfolding of crambin follow the Arrhenius equation.

These are the output files from the Gromacs simulations of the temperature induced unfolding of crambin.

opencc-zeroJul 2015View details →

Molecular dynamics simulations of the temperature-induced unfolding of crambin follow the Arrhenius equation.

These are the SPSS files for the statistical analysis of the temperature induced unfolding of crambin.

opencc-by-4.0Nov 2024View details →

Three-dimensional Unfolding and Unfaulting Dataset for Structural Interpretation Using Self-Supervised Learning

This dataset accompanies the study "Three-dimensional unfolding and unfaulting for structural interpretation using self-supervised learning." It provides synthetic data used to develop and validate a novel 3-D lightweight neural network framework for restoring deformed geological structures to their flattened state. The data include highly deformed examples with complex faulting and folding, designed to test the model's ability to compute precise shifts that realign stratigraphic layers.

opencc-by-4.0Jun 2022View details →

Dataset from: Sequence grammar underlying unfolding and phase separation of globular proteins

Dataset related to the figures from the paper "Sequence grammar underlying unfolding and phase separation of globular proteins". The raw mass spectrometry proteomics data have been deposited via the PRIDE partner repository to the ProteomeXchange Consortium under the dataset identifier PXD033716.

opencc-by-4.0Aug 2024View details →

Semi-leptonic ttbar full-event unfolding R&D dataset

This dataset was generated for the purpose of developing unfolding methods that leverage generative machine learning models. It consists of two pieces: one piece contains events with the Standard Model (SM) production of a top-quark pair in the semi-leptonic decay mode, and the other contains events with top-quark pair production modified by a non-zero EFT operator. The SM dataset contains 15,015,000 events, and the EFT dataset contains 30,000,000. Both datasets store the following event configurations: <ul> <li>Parton level: configurations of all partons that result from the matrix element calculation done using MadGraph</li> <li>Particle level: configurations of all “truth” jets and leptons that result from the parton shower and hadronization modelled using Pythia</li> <li>Detector level: configurations of all reconstruction level jets and leptons, measured by a detector simulated with Delphes and the default CMS detector card.</li> </ul> Each of these configurations is stored in a dedicated group as described below. Throughout, the units of energy and transverse momentum are GeV. For more details on the generation of this dataset, see Ref. [1]. Parton level data: <ul> <li>No phase space requirements are placed on the events at parton level. </li> <li>The kinematics of the top, anti-top, W+, W-, and all decay products are contained in groups entitled top, antitop, Wp, and Wm respectively. Each of these contains the kinematics of the parton itself in a group called “particle”, as well as the kinematics of two daughter particles, in groups called “d1” and “d2”. In the case of the tops, these daughters are the W’s and b quarks. In the case of the W’s, these are two light quarks, or a lepton and a neutrino. The “pid” vector contains the PDGID for a given particle, used to identify its type. </li> <li>One detail is that the W’s “particle” description is not always the same as the description of the same W stored as the daughter of the tops. This results from when the W radiates some parton before decaying.  </li> </ul> Particle level data: <ul> <li>At particle level all leptons and jets are required to have $p_T > 25$ GeV and absolute pseudo rapidity $|\eta| < 2.5$. </li> <li>Events at particle level are required to have at least one electron or muon and at least 4 jets, of which at least two are b-tagged. Event which pass or fail this criteria are marked by the vector contained in the group “mask”.</li> <li>Electrons and muons are stored in separate groups. Each group contains a vector “mask” which is true only if there is a true particle-level electron or muon in the event, and false if this entry is zero padding. </li> <li>Jets are clustered from stable particle level objects using the anti-kt algorithm with a radius parameter of 0.5. Jet information is stored in the group “jets”, and true jets in the event are again denoted by a true value in the vector “mask”, and zero-padding is marked by a false value. Jets additionally contain a vector “btag” which is 1 if the jet is b-tagged with the default Delphes prescription, and 0 if not.</li> <li>Information on the missing transverse momentum (MET) is contained in the group “met”. The “met” vector gives the magnitude, and the “phi” vector gives the direction in phi of the missing transverse momentum.</li> <li>In addition to the information on the jets, leptons, and MET, the particle level data also contain the configurations for the hadronic top, leptonic top, and ttbar system. These configurations are determined assuming the pseudo-top jet parton assignment algorithm, which is a common method used by LHC experiments when analyzing semileptonic ttbar events.</li> </ul> Detector level data: <ul> <li>Requirements for leptons and jets are the same as for the particle level data.</li> <li>The event selection is the same as the particle level data. Events which pass the selection are again denoted by a true value in the vector “mask”.</li> <li>The data for the leptons, jets, and MET are stored analogously to particle level</li> <li>The configurations of the top quarks and ttbar system are not pre-computed at detector level, since ideally a generative unfolding method would not assume a given jet-carton assignment algorithm when it is being trained. However if the user wishes to pursue such an application, the relevant configurations can be obtained by running the pseudo-top algorithm [2].</li> </ul> Citations: [1] - <a href="https://arxiv.org/abs/2404.14332">https://arxiv.org/abs/2404.14332</a> [2] - <a href="https://twiki.cern.ch/twiki/bin/view/LHCPhysics/ParticleLevelTopDefinitions">https://twiki.cern.ch/twiki/bin/view/LHCPhysics/ParticleLevelTopDefinitions</a>

opencc-zeroDec 2022View details →

Folding-unfolding asymmetry and a RetroFold computational algorithm

We treat protein folding as the molecular self-assembly, while unfolding is viewed as disassembly. Self-assembly and disassembly (fracture) are two opposite non-equilibrium dynamic processes; however, they cannot be converted to each other by a simple time variable reversal. Fracture is typically a much faster process than self-assembly. Self-assembly is often an exponentially decaying process, since energy relaxes due to dissipation, while fracture may be a constant rate process as the driving force is opposed by damping. Typically, protein folding takes two orders of magnitude longer time than unfolding, and it consumes a lot of computational resources to model folding. Based on energy dissipation rates, we suggest a mathematical transformation of variables, which makes it possible to view self-assembly as time-reversed disassembly, thus folding can be studied as reversed unfolding. We investigate the molecular dynamics modeling of folding and unfolding of the short Trp-cage protein. Folding time constitutes about 800 ns while unfolding (denaturation) takes only about 5.0 ns, and therefore, fewer computational resources are needed for its simulation. This "RetroFold" approach can be used for the design of a novel computation algorithm, which, while approximate, is less time-consuming than traditional folding algorithms.

publicDec 2022View details →

Folding-unfolding asymmetry and a RetroFold computational algorithm

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publicAug 2024View details →

The c.119-123dup5bp mutation in human gamma-C-crystallin destabilizes the protein and activates the unfolded protein response to cause highly variable cataracts

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