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Scripts and sample information for: Molecular mechanisms of Eda-mediated adaptation to freshwater in threespine stickleback
<p><span>A main goal of evolutionary biology is to understand the genetic basis of adaptive evolution. Although the genes that underlie some adaptive phenotypes are now known, the molecular pathways and regulatory mechanisms mediating the phenotypic effects of those genes often remain a black box. Unveiling this black box is necessary to fully understand the genetic basis of adaptive phenotypes, and to understand why particular genes might be used during phenotypic evolution. Here, we investigated which genes and regulatory mechanisms are mediating the phenotypic effects of the <em>Eda</em> haplotype, a locus responsible for the loss of lateral plates and changes in the sensory lateral line of freshwater threespine stickleback (<em>Gasterosteus aculeatus</em>) populations. Using a combination of RNAseq and a cross design that isolated the Eda haplotype on a fixed genomic background, we found that the Eda haplotype affects both gene expression and alternative splicing of genes related to bone development, neuronal development and immunity. These include genes in conserved pathways, like the BMP, netrin and bradykinin signalling pathways, known to play a role in these biological processes. Furthermore, we found that differentially expressed and differentially spliced genes had different levels of connectivity and expression, suggesting that these factors might influence which regulatory mechanisms are used during phenotypic evolution. Taken together, these results provide a better understanding of the mechanisms mediating the effects of an important adaptive locus in stickleback and suggest that alternative splicing could be an important regulatory mechanism mediating adaptive phenotypes.</span></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>
Molecular mechanisms underlying plasticity in a thermally varying environment
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Scripts and sample information for: Molecular mechanisms of Eda-mediated adaptation to freshwater in threespine stickleback
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Elucidating molecular mechanisms of protoxin-2 state-specific binding to the human NaV1.7 channel
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Molecular geometries and energies from quantum mechanical calculations and small molecule force field evaluations.
<p>Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction.<br> The quality of these studies relies on the quality of the force fields used to represent the systems. <br> Focusing on small molecules of fewer than 50 heavy atoms, this data compares nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2.<br> On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data.<br> <br> The data was created using scripts of the <a href="https://github.com/MobleyLab/benchmarkff/commit/fa45247aa9867f504c02eb6f62d8459a94a0a936">benchmarkff github repository</a>.</p> <p>A corresponding manuscript is submitted, a preprint is available on ChemRxiv:<br> <a href="https://doi.org/10.26434/chemrxiv.12551867.v2 ">Lim, Victoria T.; Hahn, David F.; Tresadern, Gary; Bayly, Christopher I.; Mobley, David (2020): Benchmark Assessment of Molecular Geometries and Energies from Small Molecule Force Fields. ChemRxiv. Preprint</a></p> <p>Read below or the file README.md for further information and description of the content:</p> <pre><code class="language-markdown"># README Version: 04 Nov 2020 For Python scripts that are NOT found in these directories, please check the [BenchmarkFF Github repo](https://github.com/MobleyLab/benchmarkff/tree/master/tools). ## Procedure 1. Prep OPLS3e file for analysis: standardize format by OpenEye in case of differences and convert from kJ/mol to kcal/mol. ``` cd prep python convert_extension.py -i opls3e_minimized.sd -o opls3e.sdf ``` 2. Remove mols that couldn't parameterize by ALL FFs. ``` python get_by_tag.py -i opls3e.sdf -s "SMILES QCArchive" -list trim3.txt -o trim3_full_opls3e.sdf ``` 3. Run analysis. ``` conda activate parsley # calc ddE, RMSD, and TFD distributions python compare_ffs.py -i match.in -t 'SMILES QCArchive' --plot > metrics.out # match_minima, only in 01_analysis_all and 02_analysis_all_smaller_cutoff python match_minima.py -i match.in --plot --cutoff 1.0 --readpickle # look at specific subsets, only in 01_analysis_all python color_by_moiety.py -i match.in -p metrics.pickle -s N-N.dat azetidine.dat octahydrotetracene.dat -o scatter_tfd_3_ # look at outliers,only in 01_analysis_all and 02_analysis_all_smaller_cutoff python tailed_parameters.py -i refdata_trim_overlap_full_openff_unconstrained-1.2.0.sdf -f <offxml file> --metric 'TFD' --cutoff 0.12 --tag "TFD to trim_overlap_full_qcarchive.sdf" --tag_smiles "SMILES QCArchive" > output_tfd.dat ``` ## Brief description of contents * High level: ``` . ├── 00_prep │ ├── convert_extension.py │ ├── opls3e_minimized.sd OPLS3e minimized structures from Schrodinger Maestro │ ├── opls3e.sdf standardized through OpenEye tools │ ├── opt_openff*.sdf OpenFF minimized conformations ├── 01_analysis_all compare all ffs (qm, GAFF(2), MMFF94(S), Smirnoff, OpenFF-X.X, OPLS3e) ├── 02_analysis_all_smaller_cutoff compare all ffs (qm, GAFF(2), MMFF94(S), Smirnoff, OpenFF-X.X, OPLS3e) with a smaller cutoff of .3 for match_minima ├── 03_analysis_latest_ffs compare only the latest versions of ffs (qm, GAFF2, MMFF94S, OpenFF-1.2, OPLS3e) ├── 04_analysis_openff_only compare only OpenFF ffs (qm, Smirnoff, OpenFF-X.X) └── README.md ``` * Inside an output directory: ``` YY_analysis_* various output files of above mentioned scripts, some are listed and described below: ├── bar*.png parameter coverage bar plots ├── ddE.dat relative energies data ├── fig_density_*.png scatter plots of ddE vs (RMSD or TFD) for each force field ├── match.in input file for compare_ffs.py ├── metrics.out output file for compare_ffs.py ├── metrics.pickle pickle file for compare_ffs.py -- you can read this into compare_ffs instead of rerunning the full analysis ├── refdata_*.sdf output SDF files with stored RMSD / TFD scores with reference to QM for each structure ├── relene_*.dat relative energies of matched conformers ├── ridge_dde.png compared energies plot ├── ridge_rmsd.svg compared rmsds plot ├── ridge_tfd.svg compared tfds plot ├── fig_scatter_*.png scatter plots of ddE vs (RMSD or TFD). these are noisy; I don't use these ├── trim3_*.sdf input SDF files for compare_ffs.py listed in match.in file ├── violin*.* violin plot showing ddE distributions ``` </code></pre>
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>
Transcriptome analysis of Drosophila suzukii reveals molecular mechanisms conferring pyrethroid and spinosad resistance
<p class="MsoNormal"><em>Drosophila suzukii</em> possess a serrated ovipositor that enables them to lay eggs in soft-skinned, ripening fruits, making this insect<em> </em>a serious threat to berry production. Since its 2008 introduction into North America, growers have used insecticides as the primary approach for <em>D. suzukii</em> management, resulting in detections of insecticide resistance in this pest. This study sought to identify the molecular mechanisms conferring insecticide resistance in these resistant populations. We sequenced the transcriptomes of two pyrethroid- and two spinosad-resistant isogenic lines. In both pyrethroid-resistant lines and one spinosad-resistant line, we identified overexpression of metabolic genes that are implicated in resistance in other insect pests. In the other spinosad-resistant line, we observed an overexpression of cuticular genes that have been linked to resistance. Our findings enabled the development of molecular diagnostics that we used to confirm persistence of insecticide resistance in California. To validate these findings, we leveraged <em>D. melanogaster </em>mutants deficient in either metabolic or cuticular genes that were upregulated in resistant <em>D. suzukii </em>to demonstrate that these genes are involved in promoting resistance. This study is the first to characterize the molecular mechanisms of insecticide resistance in <em>D. suzukii</em> and provides insights into how current management practices can be optimized.</p> <p class="MsoNormal"> </p>
Data for "Ultimate molecular mechanical properties of polyolefin chains"
<p>LAMMPS input and data files, force field files, sample simulation outputs, and Jupyter notebooks used for the data analysis.</p>
Data from: Comparative transcriptomics revealed parallel evolution and innovation of photosymbiosis molecular mechanisms in a marine bivalve
<p>Photosymbioses between heterotrophic hosts and autotrophic symbionts are evolutionarily prevalent and ecologically significant. However, molecular mechanisms behind such symbioses remain less elucidated, which hinders our understanding of their origin and adaptive evolution. This study compared gene expression patterns in a photosymbiotic bivalve (<em>Fragum sueziense</em>) and a closely related non-symbiotic species (<em>Trigoniocardia granifera</em>) under different light conditions to detect potential molecular pathways involved in mollusk photosymbiosis. We discovered that the presence of algal symbionts greatly impacted host gene expression in symbiont-containing tissues. We found that the host immune functions were suppressed under normal light compared to those in the dark. In addition, we found that cilia in the symbiont-containing tissues play important roles in symbiont regulation or photoreception. Interestingly, many potential photosymbiosis genes could not be annotated or do not exhibit orthologs in <em>T. granifera</em> transcriptomes, indicating unique molecular functions in photosymbiotic bivalves. Overall, we found both novel and known molecular mechanisms involved in animal-algal photosymbiosis within bivalves. Given that many of the molecular pathways are shared among distantly related host lineages, such as mollusks and cnidarians, it indicates that parallel and/or convergent evolution is instrumental in driving host-symbiont adaptations in diverse organisms.</p>
The proteolytic cleavage of TLR8 Z-loop by furin protease - molecular recognition, reaction mechanism and role of water molecules DATASET_v2
<p>The dataset comprises:<br>i) AlphaFold-Multimer predictions for TLR8LRR-furin complex<br>ii) The optimised structures of QM cluster models for reactant (RE), intermediate1-3 (INT1-INT3), and product (PROD)<br>iii) The optimised structures of QM/MM model for RE, INT1-INT3, PROD<br>iv) Input structures used in MD simulations and parameterization files for non-standard residues for RE, INT1-INT3, PROD<br>v) PyMOL sessions from AQUA-DUCT calculations for RE, INT1-INT3, PROD</p>
All-atom simulations elucidate the molecular mechanism underlying RNA-membrane interactions
<p>Topology files and frames extracted from the minimum of the free energy profile F(d_z) (or F(d_min) for single-stranded RNAs), within 2.5kBT. These files can be used to reproduce the hydrogen bond analyses in the manuscript.</p> <p>Scripts which were used to extract hydrogen bond information are available on <a href="https://github.com/salvatoredimarco/rna-membrane">https://github.com/salvatoredimarco/rna-membrane</a></p> <p><strong>Systems:</strong></p> <p>4xN: nucleosides</p> <p>4xN2: dinucleotides</p> <p>4xN3: trinucleotides</p> <p>4xN_OPC: nucleosides simulated with OPC water model. Energy threshold is here 1.0*kBT, because of weaker binding.</p> <p>1xGA, 1xGU, 1xGC, 1xCU</p> <p>1xGGC, 1xGCG</p> <p>1xquadruplex: G-quadruplex</p> <p>1xstrand: 19-mer RNA strand</p> <p>1xhairpin: 16-mer folded hairpin</p> <p>1x16mer_elong: 16-mer unfolded, restrained</p> <p>2x16mer_loose1/2: 16-mer unfolded, unrestrained</p>
Data for: Molecular mechanisms behind safranal's toxicity to liver cancer cells from dual omics
<p>The spice saffron (<em>Crocus sativus</em>) has anticancer activity in several human tissues, but the molecular mechanisms underlying potential therapeutic effects are poorly understood. We investigated the impact of safranal, a small molecule secondary metabolite from saffron, on the HCC cell line HEP-G2 using untargeted metabolomics (HPLC-MS) and transcriptomics (RNAseq). Increases in glutathione disulfide and other biomarkers for oxidative damage contrasted with lower levels of the antioxidants biliverdin IX (139-fold decrease, p=5.3E-5), the ubiquinol precursor 3-4-dihydroxy-5-all-trans-decaprenylbenzoate (3-fold decrease, p=1.9E-5), and resolvin E1 (-3,282-fold decrease, p=4E-5), which indicates sensitization to reactive oxygen species. We observed a significant increase in intracellular hypoxanthine (538-fold increase, p=7.7E-6) that may be primarily responsible for oxidative damage in HCC after safranal treatment. The accumulation of free fatty acids and other biomarkers, such as S-methyl-5'-thioadenosine, are consistent with safranal-induced mitochondrial de-uncoupling and explain the sharp increase in hypoxanthine we observed. Overall, the dual omics datasets describe routes to widespread protein destabilization and DNA damage from safranal-induced oxidative stress in HCC cells.</p>
Reaction Mechanism of the PET Degrading Enzyme PETase Studied with DFT/MM Molecular Dynamics Simulations
<p>Raw simulations of the acylation step by PETase on a PET dimer model substrate, ran with CP2K 6.1 software at the PBE:AMBER level. Details can be found in the original manuscript (<a href="https://doi.org/10.1021/acscatal.1c03700">https://doi.org/10.1021/acscatal.1c03700</a>): Molecular topology in AMBER Parameter Topology format and Trajectories in CHARMM binary coordinate format DCD.</p> <p>RESIDUE LIST:<br> GLY57<br> TYR58<br> SER131<br> MET132<br> TRP156<br> ASP177<br> SER178<br> ILE179<br> ALA180<br> HID208<br> MOL262</p> <p>VMD selection:<br> (name CA C O HA2 HA3 and resname GLY and resid 57) or (name N CA CB H HA HB2 HB3 and resname TYR and resid 58) or (name CA C O OG CB HA HB2 HB3 HG and resname SER and resid 131) or (name N CA SD CE CB CG H HA HB2 HB3 HG2 HG3 HE1 HE2 HE3 and resname MET and resid 132) or (name CB CG CD1 CD2 CE2 CE3 NE1 CZ2 CZ3 CH2 HB2 HB3 HD1 HE1 HE3 HZ2 HZ3 HH2 and resname TRP and resid 156) or (name CG OD1 OD2 CB HB2 HB3 and resname ASP and resid 177) or (name C O and resname SER and resid 178) or (name N CA C O CG2 CD1 CB CG1 H HA HB HG12 HG13 HG21 HG22 HG23 HD11 HD12 HD13 and resname ILE and resid 179) or (name N CA H HA and resname ALA and resid 180) or (name CB CG CD2 ND1 CE1 NE2 HB2 HB3 HD1 HD2 HE1 and resname HID and resid 208) or (name C1 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C2 C20 C3 C4 C5 C6 C7 C8 C9 H1 H10 H11 H12 H13 H14 H15 H16 H17 H2 H3 H4 H5 H6 H7 H8 H9 O1 O2 O3 O4 O5 O6 O7 O8 O9 and resname MOL and resid 262)</p> <p>PYMOL selection:<br> (name CA+C+O+HA2+HA3 & resn GLY & resi 57) | (name N+CA+CB+H+HA+HB2+HB3 & resn TYR & resi 58) | (name CA+C+O+OG+CB+HA+HB2+HB3+HG & resn SER & resi 131) | (name N+CA+SD+CE+CB+CG+H+HA+HB2+HB3+HG2+HG3+HE1+HE2+HE3 & resn MET & resi 132) | (name CB+CG+CD1+CD2+CE2+CE3+NE1+CZ2+CZ3+CH2+HB2+HB3+HD1+HE1+HE3+HZ2+HZ3+HH2 & resn TRP & resi 156) | (name CG+OD1+OD2+CB+HB2+HB3 & resn ASP & resi 177) | (name C+O & resn SER & resi 178) | (name N+CA+C+O+CG2+CD1+CB+CG1+H+HA+HB+HG12+HG13+HG21+HG22+HG23+HD11+HD12+HD13 & resn ILE & resi 179) | (name N+CA+H+HA & resn ALA & resi 180) | (name CB+CG+CD2+ND1+CE1+NE2+HB2+HB3+HD1+HD2+HE1 & resn HID & resi 208) | (name C1+C10+C11+C12+C13+C14+C15+C16+C17+C18+C19+C2+C20+C3+C4+C5+C6+C7+C8+C9+H1+H10+H11+H12+H13+H14+H15+H16+H17+H2+H3+H4+H5+H6+H7+H8+H9+O1+O2+O3+O4+O5+O6+O7+O8+O9 & resn MOL & resi 262)</p>
Use of A Molecular Switch Probe to Activate or Inhibit GIRK1 Heteromers In Silico Reveals a Novel Gating Mechanism
<p>GIRK channel structure models (PDB structure files) used for Molecular Dynamics simulations.</p>
Decoding diabetes biomarkers and related molecular mechanisms using machine learning, text mining, and gene expression analysis
<p>The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA- DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top ten DEGs. These genes were enriched in aerobic respiration, T-Cell antigen receptor pathway, Tricarboxylic acid metabolic process, vitamin D receptor pathway, Toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for ML analysis . The "Decision tree", "Extra-tree regressor" and "Random forest" algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. ML algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.</p>
Molecular Dynamics (MD) Simulation Data for Dynamics Underlie the Drug Recognition Mechanism by the Efflux Transporter EmrE
<p>MD simulations on the proton bound (PDB 8UWU), deprotonated on E14A (PDB 8UWU), TPP Bound (PDB 8UWU) on our NMR derived structures.</p> <p> </p> <p>MD simulations on the proton bound (7MH6) and deprotonated on E14A (7MH6) on X-ray structures. </p> <p> </p> <p>Total raw simulation data would be too large for uploading to repositories. To reduce size of file, starting structure and tpr files are uploaded. Final structure at 2.5 μs are also uploaded. </p>
Supervised Molecular Dynamics Movies from: Deciphering the molecular recognition mechanism of multidrug resistance Staphylococcus aureus NorA efflux pump using a Supervised Molecular Dynamics approach
<p>Molecular Recognition pathway of Supervised molecular dynamic simulations of MdfA-CLM NorA-CPX and NorA-CPX.</p>
Genomic dissection of 43 serum urate-associated loci provides multiple insights into molecular mechanisms of urate control
<p>Summary statistics for trans-ancestral analysis presented in "Genomic dissection of 43 serum urate-associated loci provides multiple insights into molecular mechanisms of urate control" by Boocock et al.</p> <p>Columns in order with descriptions are:</p> <p>SNP(rsid) CHROM POS(genome position) REF(reference allele) ALT(alternative allele) Z_EUR(zscore european) Z_EAS(zscore east asian) P_META(p-value from trans-ancestral meta-analysis) </p>
Deciphering the molecular recognition mechanism of multidrug resistance Staphylococcus aureus NorA efflux pump using a Supervised Molecular Dynamics approach.
<p><strong>Legend of Movie-S1</strong></p> <p>The Movie is composed by four synchronized and animated panels that show different aspects of the SuMD simulation. The time evolution is reported in nanosecond. In the first panel (upper left), the molecular representation of the system is shown. The MdfA backbone is represented by the new cartoon style (cyan). The CLM is shown in yellow and by a transparent surface. The protein residues within 3 Å from the ligand are made explicit by a stick representation.</p> <p>In the second panel (upper-right), the CM-distance between the protein and the ligand centers of mass is reported.</p> <p>In the third panel (lower left), the MMGBSA energy profile is reported.</p> <p>In the fourth panel (lower-right) cumulative electrostatic interactions are reported for the 15 MdfA residues most contacted by CLM during the whole simulation.</p> <p> </p> <p><strong>Legend of Video-S2</strong></p> <p>The Movie shows the SuMD trajectory of CLM on MdfA compared to the CLM crystallographic pose. MdfA is represented in cyan new cartoon transparency. The crystallographic pose is showed in yellow while the experimental one in light green. At 16.69 ns a RMSD value of 1.77 Å is highlighted.</p> <p> </p> <p><strong>Legend of Video-S3</strong></p> <p>The Movie is composed by four synchronized and animated panels that show different aspects of the SuMD simulation. The time evolution is reported in nanosecond. In the first panel (upper left), the system is shown. The NorA backbone is represented by the new cartoon style (red) and the protein residues within 3 Å of CPX are showed in stick. CPX is rendered by a green stick.</p> <p>In the second panel (upper-right), the distance between the centre of mass of the ligand and the protein during the trajectory is reported.</p> <p>In the third panel (lower left), the MMGBSA energy profile is reported. In the fourth panel (lower-right) cumulative electrostatic interactions are reported for the 15 NorA residues most contacted by CPX during the whole SuMD trajectory.</p> <p>It is important to note that the following video has a duration that is half of the simulation of SuMD. However, this straid does not alter the description of the trajectory performed by the ligand.</p> <p> </p> <p><strong>Legend of Video-S4</strong></p> <p>The Movie depicts the clustering analysis of CPX during the whole SuMD simulation. The NorA protein is shown in red new cartoon transparency. CPX is rendered by a light-green stick and by a transparent surface. The spheres are shown in 7 different colours, according to the different clusters. Each sphere dimension is in according to the cluster dimensions. After a first recognition site, the ligand conformations are clustered in different sites of the NorA channel. It is important to note that the following video has a duration that is half of the simulation of SuMD. However, this straid does not alter the description of the trajectory performed by the ligand.</p>
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Allen Brain Atlas
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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.
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