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24 results for “in silico model”
Multiscale continuum figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling"
<p>Accessible versions of selected figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling" Environ. Sci. Processes Impacts 19(3): 188-202. DOI: 10.1039/C7EM00053G.</p> <p>The Abstract Art figure shows a classification of variables for predictive/diagnostic models used in silico environmental chemical science, in terms of system scales and variable types. Figure 3 shows a continuum of system scales encompassing the whole scope of predictive/diagnostic modelling for in silico environmental chemical sciences, juxtaposing earth and biological scales.</p> <p>The published version of Figure 3 is tall, for two-column page-layouts, but a wide version of Figure 3 is provided for landscape oriented formats. The 300 dpi versions of each figure should be adequate resolution for most purposes, and therefore are recommended. The large versions of the figures may take significant time to download, but may be useful for high resolution applications.</p> <p>This work is from the perspectives/review paper at the beginning of a themed issue on "Quantitative Structure-Activity Relationships (QSARs) and Computational Chemistry Methods in the Environmental Chemical Sciences", published in the March 2017 issue of the Royal Society of Chemistry journal Environmental Sciences: Process and Impacts. The whole collection of papers can be accessed at rsc.li/qsars.</p>
Personalized in silico model for radiation-induced pulmonary fibrosis | (source code, simulation input+output data)
<p>This repository concerns the supplementary material data of the research article entitled "<em>Personalised in silico model for radiation-induced pulmonary fibrosis</em>" that is published in the Royal Society Interface journal (rsif.royalsocietypublishing.org). More specifically, the repository contains the source code of the radiation-induced pulmonary fibrosis simulator, the results produced from the medical image analysis of this study (CT scans and RT dosage maps) for each patient case, the input files necessary to run the simulator and the corresponding output produced respectively. Each patient ID corresponds to each case documented in the research article.</p>
Dataset related to article "New in silico models to predict in vitro micronucleus induction as marker of genotoxicity"
<p>The .txt file contains the dataset of the in silico model for genotoxicity as induction of micronuclei.</p> <p>The .doc file contains the descriptors of the models and the structural alerts.</p>
A battery of in silico models application for pesticides exerting reproductive health effects: assessment of performance and prioritization of mechanistic studies
<p>Dataset of Table 1-7</p> <p>Data of Table 1, “Pesticides and their classification”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1_Tab1.PNG). Corresponding raw data is regarding classification in the hazard class reproductive toxicity available on line. All further related information are provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK__Tab1_PPP_27_1_M.txt) in txt format.</p> <p> </p> <p>Data of Table 2, “PDB structures of nuclear receptors used in VTL and ED” </p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1_Tab2 15 meta data files as pdf-format with information sources of PDB structures used in employed in silico models (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M15.pdf). All further related information are provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab2_27_2_M.txt) in txt format.</p> <p> </p> <p>Data of Table 3, “Results of in vivo studies (Shepelska et al., 2021; Shepelskaya and Kolyanchuk, 2021; Shepelskaya and Kolianchuk, 2018)”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1_Table3.PNG). Three meta data file as pdf-format with data of in vivo studies (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_3_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_3_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_3_M3.pdf). All further related information are provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab3_27_3_M.txt) in txt format.</p> <p> </p> <p>Data of Table 4, “Results of in silico modelling of pesticides interaction with nuclear receptors”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab4.PNG). Corresponding raw data with in silico modelling results provided as two files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1-17.csv) and seventeen pdf files (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_15.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_16.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_17.pdf). Four meta data file as pdf-format with detailed in silico modelling descriptions (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M3.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_M1.pdf). All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab4_24_1-2_M.txt) in txt format.</p> <p> </p> <p>Data of Tabe 5, “Combination of in silico results with in vitro results by considering as positive result only where both in silico models predict a hit (Combined 1)”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab5.PNG). Corresponding raw data with in silico modelling results provided as two files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1-17.csv) and seventeen pdf files (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_15.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_16.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_17.pdf). Four meta data file as pdf-format with detailed in silico modelling descriptions (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M3.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_M1.pdf).</p> <p>Corresponding raw data with ToxCast results provided as seventeen files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_2.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_3.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_4.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_5.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_6.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_7.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_8.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_9.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_10.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_11.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_12.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_13.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_14.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_15.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_16.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_17.csv)All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab5_24_25_1_M.txt) in txt format.</p> <p> </p> <p>Data of Table 6, “Combination of in silico results with in vitro results by considering as a positive any in silico hit independently of the employed model (Combined 2)”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab6.PNG). Corresponding raw data with in silico modelling results provided as two files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1-17.csv) and seventeen pdf files (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_15.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_16.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_17.pdf). Four meta data file as pdf-format with detailed in silico modelling descriptions (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M3.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_M1.pdf).</p> <p>Corresponding raw data with ToxCast results provided as seventeen files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_2.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_3.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_4.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_5.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_6.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_7.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_8.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_9.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_10.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_11.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_12.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_13.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_14.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_15.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_16.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_17.csv)All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab6_24_25_1_M.txt) in txt format.</p> <p> </p> <p>Data of Table 7, “Metrics of performance of in silico models separately and combined.”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab7.PNG). Corresponding raw data with calculation of relevant performance metrics provided as one file in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_26_1.csv). One meta data file as pdf-format with detailed description of the method used for calculation (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_26_1_M1.pdf).</p> <p>All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab7_26_1_M.txt) in txt format.</p>
ARCHIMED-φ simulation files for the simulation of Design A from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal
<p>Input files for the simulation of Design A in ARCHIMED-φ from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal.</p> <p>See https://archimed-platform.github.io/archimed-phys-user-doc/ for more details on the model.</p> <p>Make a simulation by opening a terminal at the root of the folder and type: `java -jar .\archimed-phys.jar .\DesignA_MockUpA_seed1_MAP_72.yml`.</p>
Identification of potential modulators of IFITM3 by in-silico modeling and virtual screening
<p>Modeled structure of IFITM3 and Desmond MD trajectory files for IFITM3 and IFITM3-ligand complexes. Please see README file.</p>
Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience
<p><strong>Supplementary Data</strong> </p> <ol> <li><strong>Supplementary Data 1</strong> contains the input (non-watertight) surface meshes of the block (shown in Figure 2a) reconstructed within the context of the EPFL-KAUST collaboration, and the corresponding output (watertight) meshes generated by Ultraliser.</li> <li><strong>Supplementary Data 2 </strong>contains a set of 20 non-watertight meshes that were randomly selected from the block shown in <strong>Supplementary Figure S54</strong> and another set of the their watertight counterparts.</li> <li><strong>Supplementary Data 3</strong> contains a set of 25 neuronal morphologies with different morphological types and their corresponding watertight meshes.</li> <li><strong>Supplementary Data 4</strong> contains a set of 25 synthetic astroglial morphologies 15 and their corresponding watertight meshes.</li> <li><strong>Supplementary Data 5</strong> contains the vascular morphology (shown in <strong>Supplementary Fig. S83</strong>) and a corresponding multi-partitioned watertight mesh.</li> <li><strong>Supplementary Data 6</strong> contains the datasets used for the comparative analysis shown in <strong>Supplementary Section 13</strong>.<br> <br> Neuronal, astrocytic and vascular morphologies are stored in SWC, H5 and VMV file formats respectively. The file structures of the SWC and VMV formats are publicly available online. The H5 files of the complete astrocyte cells can be made available from corresponding authors upon request. All the surface meshes are stored in Wavefront OBJ files. Additional STL meshes are generated to be used for TetGen to create corresponding tetrahedral meshes. All the input and generated data files are publicly available on Zenodo (10.5281/zenodo.7105941).</li> </ol> <p><strong>Data Sources</strong> </p> <ol> <li>Cellular and subcellular NGV meshes segmented from the volume shown in Figure 2 are provided by the collaborating co-authors affiliated with KAUST.</li> <li>Neuronal meshes shown in Figure 3, Supplementary Figures S55 - S75 and Supplementary Figures S85 are publicly available from the MICrONS program.</li> <li>Neuronal morphologies shown in Figure 4, Supplementary Figures S80 - S81 and Supplementary Figure S86 are publicly available from NeuroMorpho.Org.</li> <li>Astrocytic morphologies (Figure 5 and Supplementary Figure S82) are provided by Eleftherios Zisis.</li> <li>Vascular morphologies (rat’s cerebral microvasculature) shown in Figure 6 and Supplementary Figures S83 - S84 are courtesy of Bruno Weber, University of Zürich (UZH).</li> <li>The vascular morphology of the arterial arborizations shown in Supplementary Figure S88 is available from the Brain Vasculature (BraVa) database (cng.gmu.edu/brava).</li> </ol>
Fig. 5. Modeled 3D in Response to enantiomers of (Z3Z9)-6,7-epoxy-octadecadiene, sex pheromone component of Ectropis obliqua Prout (Lepidoptera: Geometridae): electroantennagram test, field trapping, and in silico study
Fig. 5. Modeled 3D structure and validation of EoblPBP1. (A) Sequence alignment of EoblPBP1 and template 1DQE_A. α-helices are displayed as squiggles. Identical residues are highlighted in white letters with deep blue background. (B) Overall structure of the EoblPBP1. Three disulfide bonds are in red. N-terminus, C-terminus, and α-helices are labeled. Two potential key residues: Thr117 and Arg 121 are in orange. (C) Ramachandran plot of EoblPBP1.
Recombination and In-silico protein modelling and functional characterization of copLAB genes from Trinidadian Xanthomonas campestris and melonis isolates
<p>RDP, GARD, RaptorX and InterProScan outputs relating to the publication tentatively titled "Heavy metal resistance islands associated with a putative Tn in Trinidadian copper resistant <em>Xanthomonas campestris </em>and <em>melonis </em>strains are strongly linked to homologs from the <em>Stenotrophomonas </em>genus"</p>
Ultrasound Stochastic Tomography simulation data for In-silico 2D Breast Phantom model with tumour
<p>An anatomically realistic numerical breast phantom model (with realistic acoustic properties of speed of sound, density, and attenuation coefficient of tissues) derived from [1] is presented with details of a ultrasound tomography experiment in simulation. Details of source wavelets, geometry of transducer set, observed data at each transducer for each shots are provided with phantom model.</p> <p>References</p> <p>[1] <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a></p>
In silico design, docking simulation, and ANN-QSAR model for predicting the anticoagulant activity of thiourea isosteviol compounds as FXa inhibitors
<p>The present work combined molecular modeling and docking approach for searching and designing novel thiourea isosteviol-based compounds as potential FXa inhibitors. Elaborated regression model establishes the relationships between experimentally determined anticoagulant activity and molecular descriptors and enables the prediction of FXa inhibitory activity for novel compounds. The obtained results proved that the Artificial Neural Network algorithm facilitates the search for the most promising isosteviol derivatives incorporating thiourea fragments as FXa inhibitors. Moreover, docking simulation confirms the prominent binding of the newly in silico designed molecules with the active sites of the protein, which may be the lead molecules and can be further optimized for the efficient pharmacodynamic and pharmacokinetic profiles. The enclosed files are representations of molecular structures of thiourea isosteviol compounds with experimentally tested FXa inhibitory activity (i20-i39) geometrically optimized in hyperchem, newly in silico designed thiourea isosteviol compounds geometrically optimized in hyperchem (e1-e11), one file contains molecular descriptors for optimized structures calculated in Dragon and there is also a code for ANN QSAR model for predicting activity of novel thiourea isosteviol compounds. </p>
A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling
<p>seq.fasta and P96787_1UAZ.fasta are the input files for I-TASSER suite.</p> <p>The command of running I-TASSER suite:<br> path-to-I-TASSER-dir/I-TASSERmod/runI-TASSER.pl -pkgdir path-to-I-TASSER-dir -libdir path-to-I-TASSER-lib-dir -seqname P96787 -datadir path-to-workdir -outdir path-to-outdir -java_home /usr/ -restraint2 P96787_1UAZ.fasta</p> <p>For detailed data please refer to I-TASSER documentation.</p> <p>The P96787_1UAZ_IT.zip is an archieve with I-TASSER output for the best achaerhodopsin-3 prediciton.</p> <p>SCRIPTS_GENERAL.zip contains all the scripts for the structure postprocessing: addition of hydrogen atoms,<br> chromophore insertion and equilibration, internal waters addition.</p> <p>P96787_wi.pdb is a final prepared structure.</p> <p>Input files for spectra calculations are rhodopsin_gaussian.inp and rhodopsin_orca.inp.</p> <p> </p>
In silico modeling of arch-3: Medeller and RosettaCM model building.
<p>Here are all the files nessesary for prediction of archaerhodopsin-3 structure using Medeller or RosettaCM algorithms. </p> <p>For modeling using Medeller please read README_MEDELLER file for all the information. For RosettaCM:</p> <p>Please, before using these scripts adjust them for you cluster. You will need the recent version of Rosetta package installed.</p> <p>Let's take for example stucture P96787 (arch-3) as a query, 1UAZ_full.pdb as a template.</p> <p>Also, you'll need grishex.script, thread.script, hybridize.script, relaxate.script, stage1_membrane.wts, stage2_membrane.wts, stage3_rlx_membrane.wts, uuu.grishin, rosetta_cm.options, rosetta_cm.xml, relax.options, cluster.options that are located in the "GENERAL" folder here. Let's assume they are located in the workfolder.</p> <p>Create an alignment file. For pairwise alignement use AlignMe/MP-T. Copy the alignment information in the file P96787_1UAZ.aln in the workfolder.</p> <p>Put in the workfolder attached P96787_3.frags P96787_9.frags attached here -- fragment files for Rosetta.<br> Put in the workfolder attached P96787.octopus.</p> <p>mv 1UAZ_full.pdb 1UAZ.pdb</p> <p>Open the grish.script and check: "target" and "templateA" which describe each line of the alignment file change on the names of the lines in your alignment file!<br> For example for AlignMe it will be P96787 1UAZ.<br> ./grishex.script P96787 1UAZ</p> <p>./thread.script P96787 1UAZ</p> <p><br> Run the hybridize script: ./hybridize.script P96787 1UAZ 500<br> It will take several days.</p> <p>It will create file hybridized_P96787.out -- a binary silent file of 500 structures with the rebuilt loops and inserted fragments, if<br> there were gaps. It will take several days also.</p> <p>Run the ./relax.script P96787 50<br> <br> It will create file relaxed_P96787.out -- a binary silent file of 25000 structures -- 50 for each hybridized.</p> <p>Time to evaluate the results.</p> <p>Clustering.</p> <p>Run ./do.script P96787<br> open LISTIK<br> delete the _0001 in the end of each line.<br> Run ./resulting1.script</p> <p>In the folder PDBSS open cluster_summary.txt.<br> The first structure in the list is the best one. It is located in the folder PDBSS.</p> <p><br> </p>
Supplementary information for: "A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling".
<p>This is supplementary data for F1000Research article: A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling.</p> <p>Here are files for modeling archaerhodopsin-3 with I-TASSER, Medeller and RosettaCM algorithms, structure postprocessing and spectra calculations.</p>
In-vitro Major Arterial Cardiovascular Simulator: Benchmark Data Set for in-silico Model Validation
<p><strong>Background</strong><br> <br> The data described here supplements the paper "In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark Data Sets for in-silico Model Validation" (to be submitted). It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper M. Wisotzki, A. Mair, P. Schlett, B. Lindner, M. Oberhardt, S. Bernhard, In Vitro Major Arterial Cardiovascular Simulator to Generate Benchmark Data Sets for In Silico Model Validation (2022), Data 7(11), DOI: 10.3390/data7110145 and the Zenodo doi when using this dataset.</p> <p><strong>General description / Dataset Structure</strong></p> <p>Each mat-File describes a different stenosis degree at the popliteal artery of the in-vitro simulator MACSim (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function.</p> <p>The different stenosis degrees for each degree are:<br> ScenarioI: 100 % Area fraction (no stenosis)<br> ScenarioII: 37,5 % Area fraction<br> ScenarioIII: 23,4 % Area fraction<br> ScenarioIV: 6,56 % Area fraction</p> <p><strong>Data fields for each file</strong></p> <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>name of the scenario according to the paper, corresponds to filename</td> </tr> <tr> <td>configuration</td> <td>parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table> <p> </p> <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the paper (node numbering, not sensor numbers) or in the software SISCA (https://gitlab.com/agbernhard.lse.thm/sisca) in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor, unit mmHg for type 'p' and ml/s for type 'q' </td> </tr> <tr> <td>anatomicalPosition</td> <td> <p>name of the corresponding anatomical position</p> </td> </tr> </tbody> </table>
COVID-19 patient data from a study in Singapore curated for input into an in silico infection model
Open the record for dataset details and reuse information.
Supplementary information for: "A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling".
<p>This is supplementary data for F1000Research article: A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling.</p> <p>Here are files for modeling archaerhodopsin-3 with I-TASSER, Medeller and RosettaCM algorithms, structure postprocessing and spectra calculations.</p> <p>Please, refer to the readme.txt for the description.</p>
Fig. 4. The stability parameters for modelled D10 in In silico characterization of the impact of mutation (LEU112PRO) on the structure and function of carotenoid cleavage dioxygenase 8 in Oryza sativa
Fig. 4. The stability parameters for modelled D10 protein: wild-type (Blue), mutant (Green), Complex I (wild-type & auxin complex) (orange), Complex II (wild-type & cytokinin complex) (purple), Complex III (mutant-auxin complex) (red) and (D) Complex IV (mutant-cytokinin complex) (black) throughout 100 ns simulation: (A) RMSD, (B) RMSF, (C) PCA of C-α movement, and (D) Radius of gyration. The trajectory projected to the two-dimensional space. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Data from: In silico study of the role of cell growth factors in photosynthesis using a virtual leaf tissue generator coupled to a microscale photosynthesis gas exchange model
Computational tools that allow in silico analysis of the role of cell growth and division on photosynthesis are scarce. We present a freely available tool that combines a virtual leaf tissue generator and a two-dimensional microscale model of gas transport during C3 photosynthesis. A total of 270 mesophyll geometries were generated with varying degree of growth anisotropy, growth extent and extent of schizogenous airspace formation in the palisade mesophyll. The anatomical properties of the virtual leaf tissue and microscopic cross sections of actual leaf tissue of tomato (Solanum lycopersicum L.) were statistically compared. Model equations for transport of CO2 in the liquid phase of the leaf tissue were discretized over the geometries. The virtual leaf tissue generator produced a leaf anatomy of tomato that was statistically similar to real tomato leaf tissue. The response of photosynthesis to intercellular CO2 predicted by a model that used the virtual leaf tissue geometry compared well with measured values. The results indicate that the light-saturated rate of photosynthesis was influenced by interactive effects of extent and directionality of cell growth and degree of airspace formation through the exposed surface of mesophyll per leaf area. The tool could be used further in investigations of improving photosynthesis and gas exchange in relation to cell growth and leaf anatomy.
Structures, Scripts and Outputs for In Silico Modeling and Scoring of PROTAC-Mediated Ternary Complex Poses
<p>The input structures, run and analysis scripts, and output files for the PROTAC modeling work as reported in the paper.</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.
DANDI Archive for NWB datasets
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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
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