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353 results for “Molecular modeling”
Models for molecular replacement (7QHY)
<p>data for the paper "Best practices of using AI-based models in crystallography and their impact in Structural Biology"</p>
Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase.
<p>The data deposited here accompany the manuscript "Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase" and include the molecular dynamics trajectories and the AMBER topology (parm) files. Detailed file contents are summarized in the README file.</p>
Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites: data set
<p>Abstract:<br> from [1]</p> <blockquote> <p>Polymer nanocomposites are an important class of materials for engineering applications due to their high versatility and good mechanical properties combined with low density. By directly attaching the polymer chains to the nanofillers, the so-called grafting, a better load transfer between matrix and filler is achieved, and, in addition, a better dispersion of the fillers is obtained. Both result in enhanced mechanical properties. Since experimental investigations on the nanoscale are extremely challenging, complementary numerical studies are needed to unravel the mechanical behavior of polymer nanocomposites. To this end, molecular dynamics is ideally suited since it captures the microstructure, but is also numerically expensive. Therefore, this contribution presents a fast coarse-grained molecular dynamics model for the investigation of the mechanical behavior of grafted polymer nanocomposites. For this purpose, we extend an existing model by grafting bonds, which allows us to compare the effect of untreated and grafted fillers directly. In particular, we investigate the influence of filler content, grafting degree, and filler size on the stiffness and strength of the polymer (grafted) nanocomposites. We conclude that the grafting bonds have little effect on the stiffness, while the strength is significantly improved compared to the untreated fillers, which is in agreement with the literature. The presented molecular dynamics model for polymer grafted nanocomposites provides the basis for further investigations, particularly of the crucial matrix-filler interphase. In addition, this contribution translates molecular dynamics insights into mechanical properties, which bridges the gap to the engineering scale and thus represents a step towards exploiting the full potential of polymer (grafted) nanocomposites.</p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All MD simulations were performed with LAMMPS [2,3], version: 29 Oct 2020 / 20201029</p> <p>Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11</p> <p>Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p>Installed packages:<br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, S. Reber, P. Steinmann, & S. Pfaller, “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p><strong>Content:</strong></p> <p>structure of data set:</p> <ul> <li>04_Equilibration<br> folders containing the sample equilibration used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> <li>05_UT<br> folders containing the uniaxial tension simulations used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li> <p>lammps input file (*.in) of the specific simulation</p> </li> <li> <p>data file (*.data) containing the initial sample configuration</p> </li> <li> <p>input.prm: input parameters of the specific simulation (read by the input file)</p> </li> <li> <p>meta.info: meta data of the specific simulation run</p> </li> <li> <p>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <ul> <li> <p>thermo_out.Dat: raw output </p> </li> <li> <p>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> </li> <li> <p>thermo_out_STD.Dat: standard deviation of raw output</p> </li> </ul> </li> </ul> <p>Output quantities (columns of *.Dat files):<br> Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <ul> <li> <p>Step: time step </p> </li> <li> <p>Time: time </p> </li> <li> <p>TotEng: total energy </p> </li> <li> <p>PotEng: potential energy</p> </li> <li> <p>KinEng: kinetic energy </p> </li> <li> <p>E_pair: pair energy </p> </li> <li> <p>E_bond: bond energy </p> </li> <li> <p>E_angle: angle energy </p> </li> <li> <p>E_dihed: dihedral energy </p> </li> <li> <p>Temp: temperature</p> </li> <li> <p>Press: hydrostatic pressure</p> </li> <li> <p>Pxx: xx component of pressure tensor </p> </li> <li> <p>Pyy: yy component of pressure tensor </p> </li> <li> <p>Pzz: zz component of pressure tensor </p> </li> <li> <p>Pxy: xy component of pressure tensor</p> </li> <li> <p>Pxz: xz component of pressure tensor</p> </li> <li> <p>Pyz: yz component of pressure tensor</p> </li> <li> <p>Volume: volume of simulation box </p> </li> <li> <p>Lx: box length in x direction </p> </li> <li> <p>Ly: box length in y direction </p> </li> <li> <p>Lz: box length in z direction </p> </li> <li> <p>Density: density </p> </li> <li> <p>c_RG: radius of gyration scalar </p> </li> <li> <p>c_RG[1]: squared radius of gyration tensor (xx component) </p> </li> <li> <p>c_RG[2]: squared radius of gyration tensor (yy component) </p> </li> <li> <p>c_RG[3]: squared radius of gyration tensor (zz component) </p> </li> <li> <p>c_RG[4]: squared radius of gyration tensor (xy component) </p> </li> <li> <p>c_RG[5]: squared radius of gyration tensor (xz component) </p> </li> <li> <p>c_RG[6]: squared radius of gyration tensor (yz component) </p> </li> <li> <p>c_bondave[1]: bond energy averaged over all atoms </p> </li> <li> <p>c_bondave[2]: bond distance averaged over all atoms </p> </li> <li> <p>c_bondave[3]: squared bond distance averaged over all atoms </p> </li> <li> <p>c_angleave[1]: angle energy averaged over all atoms </p> </li> <li> <p>c_angleave[2]: angle averaged over all atoms degree</p> </li> <li> <p>c_angleave[3]: cosine of angle </p> </li> <li> <p>c_angleave[4]: squared cosine of angle </p> </li> <li> <p>c_MSD[1]: mean squared displacement x-direction </p> </li> <li> <p>c_MSD[2]: mean squared displacement y-direction </p> </li> <li> <p>c_MSD[3]: mean squared displacement z-direction </p> </li> <li> <p>c_MSD[4]: total mean squared displacement </p> </li> <li> <p>c_COM[1]: x coordinate of center of mass </p> </li> <li> <p>c_COM[2]: y coordinate of center of mass </p> </li> <li> <p>c_COM[3]: z coordinate of center of mass </p> </li> <li> <p>v_strain_xx: xx component of engineering strain tensor </p> </li> <li> <p>v_strain_yy: yy component of engineering strain tensor </p> </li> <li> <p>v_strain_zz: zz component of engineering strain tensor </p> </li> <li> <p>v_vMisesequivstress: von Mises equivalent stress </p> </li> <li> <p>v_Cauchy_xx: xx component of stress tensor </p> </li> <li> <p>v_Cauchy_yy: yy component of stress tensor</p> </li> <li> <p>v_Cauchy_zz: zz component of stress tensor</p> </li> <li> <p>v_Cauchy_xy: xy component of stress tensor </p> </li> <li> <p>v_Cauchy_xz: xz component of stress tensor </p> </li> <li> <p>v_Cauchy_yz: yz component of stress tensor </p> </li> <li> <p>v_strain_xy: xy component of engineering strain tensor </p> </li> <li> <p>v_strain_xz: xz component of engineering strain tensor </p> </li> <li> <p>v_strain_yz: yz component of engineering strain tensor </p> </li> </ul> <p><strong>References</strong>:</p> <p>[1] M. Ries et al., “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” <em>Journal of computational physics</em>, <strong>1995</strong>, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” <em>Computer Physics Communications</em>, vol. 271, p. 108171, <strong>2022</strong>.</p> <p>[4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, <em>Zenodo</em>, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a></p>
A human genome editing-based MLL-AF4 acute lymphoblastic leukemia model recapitulates key cellular and molecular leukemogenic features. (Processed data)
<p>The prognosis of infant B-cell acute lymphoblastic leukemia (iB-ALL) remains dismal, especially in patients harboring the MLL-AF4 (KTM2A-AFF1) rearrangement, which arises prenatally in early hematopoietic stem/progenitor cells (HSPCs) and accounts for 80% of iB-ALL and 10% of non-infant cases. MLL-AF4+ B-ALL shows a bimodal localization of the MLL gene breakpoint within the MLL break cluster region, and two subgroups of patients based on the gene expression pattern of the HOXA/MEIS cluster have been identified. The pathogenic mechanisms in MLL- AF4+ B-ALL are challenging to study functionally due to the absence of faithful human cellular models recapitulating the disease phenotype and latency. Here, we assess the molecular contribution and leukemogenic capacity of MLL breakpoints occurring in either intron 10 (MLL i10 , centromeric) or intron 12 (MLL i12 , telomeric) in ontogenically-different human HSPCs sourced prenatally (fetal liver) and neonatally (cord blood). CRISPR-Cas9-induced MLL-AF4 (MA) targeting either MLL i10 (M i10 A) or MLL i12 (M i12 A) causes MA-driven in vitro myeloid immortalization in both fetal liver- and cord blood-CD34+ HSPCs. The centromeric location of the MLL breakpoint, but not the cellular ontogeny, determined the expression of HOXA/MEIS1 genes in MLL-edited cells. Centromeric MLL breakpoints endowed enhanced myeloid clonogenic replating to MLL- edited CD34+ HSPCs. The cellular ontogeny and the location of the MLL breakpoint also influenced the capacity of MLL-edited CD34+ HSPCs to initiate pro-B-ALL in vivo, which faithfully recapitulated the molecular, transcriptomic and methylome profiles of patients with primary MA+ iB-ALL. Our data provide key insights into the cellular and molecular leukemogenic determinants of MA+ iB-ALL. This dataset contains processed RNAseq and DNA methylation data from the abovementioned study.</p>
Revisiting the multispecies coalescent model fit with an example from a complete molecular phylogeny of the Liolaemus wiegmannii species group (Squamata: Liolaemidae)
Open the record for dataset details and reuse information.
Molecular modeling of Momordica cochinchinensis asparagine endopeptidase 2 and its interaction with MCoTI-II peptide
<p>Cyclotides are a large family of plant defense peptides, which display a cyclic backbone. The enzymes responsible for the backbone cyclization of these peptides are called asparagine endopeptidases, and the three-dimensional crystallographic structure of one of these cyclization enzyme has recently been described. The group of David Craik has recently discovered another cyclization enzyme, this time from the plant <em>Momordica cochinchinensis</em>, which is selective for cyclotides belonging to the trypsin inhibitor cyclotide family I have carried out molecular dynamics simulations of this new enzyme in an apo state and in an intermediate state when covalently linked with a substrate peptide. This dataset provides the necessary files to reproduce these molecular dynamics simulations carried out with the software pmemd from the Amber 18 simulation package.</p>
Dataset for molecular simulations of human IRE1 tetramer models.
<p>Dataset from SymmDock protein-protein docking of different conformers of IRE1 tetramers (dimer of back-to-back dimers); input files scripts and trajectories from subsequent MD simulations using Gromacs. Videos showing PC1 and PC2 of tetramers <em>y</em>IRE1<sub>4</sub> (S1, S2), <em>h</em>IRE1<sub>4</sub>(R) (S3, S4), <em>h</em>IRE1<sub>4</sub>(L) (S5,S6) and <em>h</em>IRE1<sub>4</sub>(S) (S7,S8), in avi format.</p>
Data associated to the article "A semiclassical Thomas–Fermi model to tune the metallicity of electrodes in molecular simulations"
<p>Contains input files and data used to generate the figures of the article:</p> <p>A semiclassical Thomas–Fermi model to tune the metallicity of electrodes in molecular simulations</p> <p>Laura Scalfi, Thomas Dufils, Kyle G. Reeves, Benjamin Rotenberg and Mathieu Salanne, J. Chem. Phys. 153, 174704 (2020)</p> <p>https://doi.org/10.1063/5.0028232</p> <p>The folder typical_input_files contains typical MetalWalls input files used to perform the simulations.</p> <p>The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper.</p>
Modeling of the bacterial molecular chaperone GroEL using 3D EM data and cnmultifit
<p>These scripts demonstrate the use of IMP, MODELLER and Chimera in the modeling of the bacterial molecular chaperone GroEL. First, MODELLER is used to generate structures for the individual components in the GroEL complex. Then, IMP is used to fit these components together into the electron microscopy density map of the entire complex.</p>
pLMMoRF: A web server that accurately predicts membrane-interacting molecular recognition features by employing a protein language model
<p>pLMMMoRF predictor scrips and MemMoRF prediction of the human proteome.</p>
Dataset for the publication titlted "A computational mechanics model for producing molecular assembly using molecularly woven pantographs" in the journal Cell Reports Physical Science, authored by Byeonghwa Goh and Joonmyung Choi.
<p>Dataset for the publication titlted "A computational mechanics model for producing molecular assembly using molecularly woven pantographs" in the journal Cell Reports Physical Science, authored by Byeonghwa Goh and Joonmyung Choi.</p>
Maps and real-space refined models for 2024 Frontiers in Molecular Biosciences Perspective
<p>Maps and real-space refined models for our 2024 Frontiers in Molecular Biosciences perspective examining the impact of ML-based map modification tools on cryoEM maps.</p>
Multifaceted Activity of Fabimycin: Insights from Molecular Dynamics Studies on Bacterial Membrane models
<p>This dataset presents a comprehensive collection of input data for Molecular Dynamics (MD) simulations performed using the GROMACS simulation software. The included systems cover various membrane environments, each with distinctive properties. The systems consist of:</p> <ol> <li><strong>IM (Inner Membrane):</strong> Simulations involving the bacteral mimicking inner membrane environment.</li> <li><strong>IM_OM (Inner Membrane and Outer Membrane Complex):</strong> Complex systems encompassing both inner and outer bacterial membrane models.</li> <li><strong>OM_D (Double Symmetric Outer Membrane):</strong> Simulations featuring a symmetric outer membrane structure.</li> <li><strong>OM (Asymmetric Outer Membrane):</strong> Simulations with an asymmetric outer membrane configuration.</li> <li><strong>PC Membrane (Phosphatidylcholine Membrane):</strong> Simulations involving membranes composed of phosphatidylcholine.</li> </ol> <p>For each membrane type, the dataset provides three replicas. The dataset includes initial and final structures (.gro files), simulation parameter files (.mdp), index files (.ndx), and topology files (.itp and .top) applicable to all systems. </p>
Dataset related to manuscript: Rapid iPSC inclusionopathy models shed light on formation, consequence and molecular subtype of a-synuclein inclusions
<p>Key Resources Table and tabular data related to Lam, Ndayisaba et al 2024.</p> <p>Code for generating graphs can be found at: doi:10.5281/zenodo.12574231 (version 1), doi:10.5281/zenodo.12574230 (all versions)</p>
Unveiling the link between Phytoplankton Molecular Physiology and Biogeochemical Cycling via Genome-Scale Modeling
<p>Data used for the manuscript "Unveiling the link between Phytoplankton Molecular Physiology and Biogeochemical Cycling via Genome-Scale Modeling"</p>
molxspec: Deep learning models for predicting MS2 spectra from molecular structures
<p>This repository contains a pre-processed dataset derived from the <a href="https://gnps.ucsd.edu">GNPS public repository</a> of natural product mass spectra as well as pretrained model weights for four different types of model architectures using pytorch (version 1.9.0). The contents are as follows:</p> <ul> <li>gnps_processed_data.tgz: Contains tab separated files of molecule/MS2 spectra pairs derived from GNPS after filtering for invalid structures, too large molecules (bigger than 2000 M/Z spectra), and structures that yielded valid 3D geometry optimization. The processing steps were done for positive ionization mode (pos_* files), though negative ionization data is also included (neg_* files)</li> <li>models.tgz: Contains pytorch format pretrained models for four different architecutres: MLP (a residual block multilayer perceptron trained on ECFP molecular fingerprints), BERT (the same MLP but trained on pretrained representations from the Zinc V1 pretrained ChemBERTa models on SMILES), GCN (a graph convolution architecture), and EGNN (an equivariant graph neural network). Models were trained on pos_processed_gnps_shuffled_with_3d_train.tsv found in the gnps_processed_data.tgz file described previously.</li> </ul>
Source molecular simulation data for calculating energy and friction profiles and permeability coefficients through model lipid membranes
<p>Energy files from GROMACS molecular dynamics simulations with enhanced free energy sampling contain time-dependent evolution of the free energy profiles and friction profiles (and other energies and simulation properties) that were used for calculating permeability coefficients in the publication https://www.biorxiv.org/content/10.1101/2021.07.16.452599v1</p> <p>Simulation system contains a lipid POPC or DPPC bilayer with a varying amount of cholesterol (specified as mol% in the file name). Hydrophobic level of the permeating particle is specified as "level-I", "level-II" etc. When unspecified in the file name, the particle is hydrophobic level "III". Lipids D-C14-PC denote PC lipids with both tails monounsaturated of length 14 carbon atoms. DOPC is equivalent to D-C18-PC. (Detailed description in the publication)</p> <p>Adaptive Weighted Histogram (AWH) method was used to sample the free energy profile of translocating small molecule through the lipid bilayer.</p> <p>GROMACS tool `gmx awh` reads the files and provides the described profiles.</p> <p>Files were generated by GROMACS `mdrun` simulation engine version 2019.3.</p> <p> </p> <p>Coarse-grained MARTINI 3.0 model was used for modeling the biomolecular interactions.</p> <p>Scripts to perform the simulations and the files with initial configurations and simulation settings are stored in a public GitHub repository depozited on Zenodo.org: <a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p> <p> </p> <p>Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).</p> <p>Lindahl, V., Lidmar, J. & Hess, B. Accelerated weight histogram method for exploring free energy landscapes. J. Chem. Phys. 141, 044110 (2014).</p> <p>Souza, P. C. T. et al. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat. Methods 18, 382–388 (2021).</p> <p>Melcr, J. Git repository with analysis scripts for MD simulations of permeability through lipid membranes. (2021) doi:<a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p>
Multi-objective optimization of equation of state molecular parameters: SAFT-VR Mie models for water
<p>Supporting document containing information on the Pareto points and thermodynamic calculations.</p>
Discovering molecular regulators of ageing using mixture models with RNA-sequencing data
<p>Identifying the molecular regulators that control ageing is challenging because the ageing process is influenced by a combination of genetic and environmental factors which makes it difficult to source the contribution of a single gene. Multiple studies have demonstrated that as humans age, increased gene expression heterogeneity results in the dysregulation of key regulators and pathways. Given the dynamic nature of gene expression, it is vital that this data be modelled by statistical approaches that can appropriately account for changes in variability to understand the contribution of heterogeneity during the aging process and properly identify its regulators. This study demonstrates the utility of using mixture models to model biological variability of gene expression occurring during ageing and how novel potential regulators of ageing can be identified.</p> <p>Our mixture modelling approach was applied to gene expression data from the Genotype-Tissue Expression (GTEx) cohort. For every gene, the expression profile was modelled using a mixture model across the cohort where the subset of donors corresponding to each mode was tested for a significant change in age group. The multi-tissue aspect of GTEx was leveraged to find ageing regulators based on this mixture model approach genes that were common across multiple tissues, suggesting that the regulation of ageing may also be controlled through a set of genes that have non-tissue-specific activity.</p> <p>Our approach identified well-documented ageing regulators <em>mTOR </em>and <em>RICTOR</em> and other potential ageing regulators such as <em>IL4</em> and <em>GPR4</em> which were detected only by our approach. Genes identified by edgeR, DESeq2 and the mixture model-based approach were enriched for similar biological pathways. This suggests that while the specific ageing regulators identified from our approach may be distinct, they generally belong in the same pathways as the genes identified by standard approaches. Overall, these results indicate that modelling gene expression variability using mixture models in conjunction with standard differential gene expression can help uncover new regulators that have a potential role for understanding human ageing.</p> <p>I</p>
Advanced Molecular Dynamics Model for Investigating Biological-Origin Microfibril Structures
<p>This data contains all necessary input file to construct the micro fibril.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.