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356 results for “In silico”
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.
Java tool for PCR, in silico PCR and genotyping
<p>We performed in silico PCR analysis of several complete plant genomes using a list of primers corresponding to an inverted repeat sequence of Hordeum-Triticum Athos miniature inverted-repeat transposable element (MITE) sequences. MITE nonautonomous members of Class II element families are derived by internal deletion of autonomous elements, and they are short (70-300 bp in length) and have conserved terminal repeats.</p> <p>For example, Athos, one of the MITE families described in grasses. Athos element sequences were collected from the genome of Hordeum vulgare, of which there are about 205 per complete genome. The Athos element sequences are highly truncated, including partial loss of terminal inverted repeats in the barley genome. Sequences of terminal inverted repeats contain multiple point mutations, insertions or deletions, which creates a difficulty for the selection of universal primers that would cover all whole copies of this element. Therefore, we selected all unique sequence variants for terminal inverted repeats and used them as primers to identify and obtain complete MITE elements for genomes of other cereals and as a negative control, we used the genome of human and long-horned nomad bee (Nomada hirtipes). Since the sequences of terminal inverted repeats for Athos element were different and quite degenerate, we used all 46 unique variants simultaneously as Forward primer in the analysis. The same primer will act as Forward and also Reverse. The length of the primers was 15 nucleotides, which localise to the furthest region of the terminal inverted repeat at the Athos element. The size for the amplicon in this case could be 30 to 200 nucleotides, including truncated elements with a central part. We used search conditions with control options: type=primer number3errors=0; minlen=30; maxlen=200. The results of this analysis are represented in Table 2. In the genome of Hordeum vulgare we identified 768 Athos and related elements, which is much more than was detected by blast analysis (205 copies for GCF_904849725.1, Blast: RefSeq Genome Database). This is because we detected not only Athos elements but also related MITE elements with overlapping end repeats. </p> <p>In our analysis, we could only detect whole Athos and related elements that contained both repeats, whereas the central part could vary. For the Hordeum bulbosum genome, we detected a 1620 record number of complete Athos and related elements compared to other species of the Hordeum family. This corresponds to the doubled genome size of this species compared to other species of the Hordeum family. For wheat genomes (Aegilops tauschii, Triticum dicoccoides), being the most similar to species of the Hordeum family, numerous copies of the related Athos and related elements were detected, with this MITE occurring much more frequently in the wheat genome than in the genome of Hordeum vulgare. It is well observed that the copy number of Athos and related elements directly depends on the genome size; the larger the genome, the greater the copy number of this element detected.</p>
In silico study for screening of natural phytochemicals and metformin as a potential inhibitor of interleukin 6 as target for covid-19
<p>Interleukin 6 is considered the corner stone at treatment of covid-19 specially the severe cases and it is the marker of severe inflammation and the gate of cytokine storm , so the future studies for this important proinflammatory cytokine storm , the first target for speed recovery from sars cov2 it is overcoming this cytokine which is immune response to virus , it is responsible about the fever and most sypmtoms of severity of covid-19 also may has role at smell and taste , so this study target this interleukin-6 by discovering natural potential inhibitors of IL-6. We do molecular docking for some natural phytochemicals like EGCG , bromelain , luteolin , vitexin and isovitexin , in adding to metformin and polyetheylene glycocl </p>
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>
Datasets for the manuscript "In silico proof of principle of machine learning-based antibody design at unconstrained scale"
<p>The zip file contains dataset files for the manuscript "In silico proof of principle of machine learning-based antibody design at unconstrained scale"</p>
An in silico guided directed evolution of protease: Spotlighting the beneficial positions
<p>Supplementary Information for "An in silico guided directed evolution of protease: Spotlighting the beneficial positions".</p>
Silica in Silico: a Molecular Dynamics Characterization of the Early Stages of Protein Embedding for Atom Probe Tomography
<p>The .zip archive contains the trajectories of all the simulations performed and analysed within the manuscript. The water molecules were removed for control systems.</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>
In silico subcellular targeting predictions for cytosolic aminoacyl tRNA-synthetases (aaRS) in parasitic plants
<p>Eukaryotic nuclear genomes often encode distinct sets of protein translation machinery for function in the cytosol vs. organelles (mitochondria and plastids). This phenomenon raises questions about why multiple translation systems are maintained even though they are capable of comparable functions, and whether they evolve differently depending on the compartment where they operate. These questions are particularly interesting in land plants because translation machinery, including aminoacyl-tRNA synthetases (aaRS), is often dual-targeted to both the plastids and mitochondria. These two organelles have quite different metabolisms, with much higher rates of translation in plastids to supply the abundant, rapid-turnover proteins required for photosynthesis. Previous studies have indicated that plant organellar aaRS evolve more slowly compared to mitochondrial aaRS in other eukaryotes that lack plastids. Thus, we investigated the evolution of nuclear-encoded organellar and cytosolic translation machinery across a broad sampling of angiosperms, including non-photosynthetic (heterotrophic) plant species with reduced rates of plastid gene expression to test the hypothesis that translational demands associated with photosynthesis constrain the evolution of bacterial-like enzymes involved in organellar tRNA metabolism. Remarkably, heterotrophic plants exhibited wholesale loss of many organelle-targeted aaRS and other enzymes, even though translation still occurs in their mitochondria and plastids. These losses were often accompanied by apparent retargeting of cytosolic enzymes and tRNAs to the organelles, sometimes preserving aaRS-tRNA charging relationships but other times creating surprising mismatches between cytosolic aaRS and mitochondrial tRNA substrates. Our findings indicate that the presence of a photosynthetic plastid drives the retention of specialized systems for organellar tRNA metabolism.</p>
In silico data for: Folding correctors can restore CFTR post-translational folding landscape by allosteric domain-domain coupling
<p>Directory layout and description for deposited data, scripts, and results associated with</p> <p><strong>Folding correctors can restore CFTR post-translational folding landscape by allosteric domain-domain coupling</strong></p> <p>Naoto Soya, Haijin Xu, Ariel Roldan, Zhengrong Yang, Haoxin Ye, Fan Jiang, Aiswarya Premchandar, Guido Veit, Susan P.C. Cole, John Kappes, Tamas Hegedus, and Gergely L. Lukacs</p> <p> </p> <p>Two files are provided:</p> <ol> <li><strong>soya_md_trajectories.tar</strong> - This file contains the trajectories merged from the last part (450-500 ns) of the parallel simulations: md_450000_500000.xtc</li> <li><strong>soya_insilico_data.zip</strong> - This file contains all other deposited files including input data, scripts, results files.<br> The content of this file can be found below:</li> </ol> <p><strong>README.md </strong>- the content of this description</p> <p><strong>homo - Homology modeling</strong></p> <ul> <li>run*.py, myloopmodel.py, and mymodel.py files are separated for technical reasons, for running model-building in parallel mode</li> <li><strong>cftr-loop</strong> - Demonstrates the removal of the RI and seeling the break with loopmodeling</li> <li><strong>mrp1</strong> - Scripts and input files for human MRP1 homology modeling; the large unresolved loop in NBD1 was not modeled but sealed for MD; this required renumbering of the ouput</li> <li><strong>mrp6</strong> - Scripts, input, and output files for human MRP1 homology modeling; output: mrp6_human_closed.pdb; the selected CFTR and MRP1 models were the input for MD simulaitons; to see these energy minimized structures, please see the corresponding 'md' directory below.</li> </ul> <p><strong>md - Moldecular dynamics</strong></p> <ul> <li>The <strong>md_system_info.xlsx</strong> file contains the basic properties of simulation boxes</li> <li>MD parameter files: step6*.mdp for minimization and equilibration; step7_production.mdp for production run</li> <li>wordom.dat is the wordom configuration file</li> <li>cmap_mda.mp.py is a script for contact map calculation</li> <li><strong>cftr-*, mrp1-*</strong> <ul> <li>the simulation system generated by CHARMM-GUI: step5_charmm2gmx.pdb</li> <li>the output gro file of parallel simulations (the last state of the sysmtems): md_[1-6].gro</li> <li>! the trajectory merged from the last part (450-500 ns) of the parallel simulations: trajectories md_450000_500000.xtc are in a separte file (soya_md_trajectories.tar) with the same directory structure</li> <li>the merged trajectory contains only the SOLU; the corresponding structure file: prot.pdb</li> <li>index.ndx</li> </ul> </li> </ul> <p><strong>figures</strong></p> <ul> <li><strong>figure-3a</strong> <ul> <li>pdb files are the output of gmx rmsf</li> <li>pse file is saved visualization of the pdb files for PyMOL<br> </li> </ul> </li> <li><strong>figure-3c-s4b</strong> <ul> <li>You can run color_all.tcl in VMD to reproduce the network communities in structural context; this is dependent on the .pdb and .vmd files also deposited in this directory</li> <li>Network community members (residues) are listed in the Word files</li> <li>dri in file names and in scripts refers to 6ss<br> </li> </ul> </li> <li><strong>figure-s3a</strong> <ul> <li>tmd1_structures.pse contains the structures for PyMOL</li> <li>Please see the Source Data file for plotting RMSF</li> <li>Contact map data are in the tmd1_wt.npy and tmd1_r170g.npy file<br> </li> </ul> </li> <li><strong>figure-s3e</strong> <ul> <li>PyMOL pse files to visualise the dynamics of NBD1/2 structures<br> </li> </ul> </li> <li><strong>figure-s4a</strong> <ul> <li>Contains the calculated betweenness.txt files</li> <li>betweenness_plots.py for plotting</li> <li>wt_prot.pdb: required for plotting with resi thick-labels<br> </li> </ul> </li> <li><strong>figure-s5c</strong> <ul> <li>PyMOL pse files to visualise the dynamics of NBD1/2 structures<br> </li> </ul> </li> <li><strong>figure-s8d</strong> <ul> <li>Data for MRP1/ABCC1</li> <li>You can run color_all.tcl in VMD to reproduce the network communities in structural context; this is dependent on the .pdb and .vmd files also deposited in this directory</li> <li>Network community members (residues) are listed in the Word files</li> </ul> </li> </ul>
In silico and empirical evaluation of twelve metabarcoding primer sets for insectivorous diet analyses
Open the record for dataset details and reuse information.
In silico subcellular targeting predictions for cytosolic aminoacyl tRNA-synthetases (aaRS) in parasitic plants
Open the record for dataset details and reuse information.
MS data set: Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data
<p>Data set consisting of raw LC-MS2 data, LC-MS1 peak data and a description</p> <p>For unreviewed publication preprint: <strong>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS<sup>1</sup>) and <em>in silico </em>Peptide Mass Data</strong></p> <p>ABSTRACT</p> <p>Over the past decade, modern methods of mass spectrometry (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. While MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem mass spectrometry (LC-MS<sup>2</sup>) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be very time-consuming, especially if the experimental LC-MS<sup>2</sup> data are tested against sequence databases covering a broad panel of different microbiological taxa.</p> <p>In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS<sup>1</sup> data are then extracted and systematically tested against an in silico library of peptide mass data compiled in house. The library has been computed from the UniProt Knowledgebase Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from spectral distances between experimental and in silico peptide mass data and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient – less than two minutes per sample - and has been successfully tested by a set of 19 different microbial pathogens. The approach is rapid, accurate and automatable and holds great potential for future microbiological applications.</p> <p><em>For details see the following preprint: Lasch, P. Schneider, A. Blumenscheit, C. and Doellinger, J. “Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data”. bioRxiv preprint, http://dx.doi.org/10.1101/870089</em></p> <p> </p>
Dateset of in silico investigations on protein_protein Interaction of GST isoforms with ASK1 and JNK1
<p>Please refer to the description of the files document for details on the files in the zip folder.</p> <p>The dataset contains molecular dynamics simulation files, and other in silico investigation output files used to describe the protein-protein interactions of seven GST isoforms with that of MAPK8 (JNK1) and MAP3K5 (ASK1). </p>
A comparison between mouse, in silico, and robot odor plume navigation reveals advantages of mouse odor-tracking
<p>Localization of odors is essential to animal survival, and thus animals are adept at odor-navigation. In natural conditions animals encounter odor sources in which odor is carried by air flow varying in complexity. We sought to identify potential minimalist strategies that can effectively be used for odor-based navigation and asses their performance in an increasingly chaotic environment. To do so, we compared mouse, <i>in silico</i> model, and Arduino-based robot odor-localization behavior in a standardized odor landscape. Mouse performance remains robust in the presence of increased complexity, showing a shift in strategy towards faster movement with increased environmental complexity. Implementing simple binaral and temporal models of tropotaxis and klinotaxis, an <i>in silico</i> model and Arduino robot, in the same environment as the mice, are equally successful in locating the odor source within a plume of low complexity. However, performance of these algorithms significantly drops when the chaotic nature of the plume is increased. Additionally, both algorithm-driven systems show more successful performance when using a strictly binaral model at a larger sensor separation distance and more successful performance when using a temporal and binaral model when using a smaller sensor separation distance. This suggests that with an increasingly chaotic odor environment, mice rely on complex strategies that allow for robust odor localization that cannot be resolved by minimal algorithms that display robust performance at low levels of complexity. Thus, highlighting that an animal's ability to modulate behavior with environmental complexity is beneficial for odor localization.</p>
Reproducible in-silico omics analyses - GSE37703: Differential analysis of HOXA1 in adult cells dataset
<p>GSE37703: Differential analysis of HOXA1 in adult cells at isoform resolution by RNA-Seq’ for quantification by Kallisto and differential abundance with Sleuth dataset used for the "Reproducible in-silico omics analyses across clouds and clusters" paper.</p>
Reproducible in-silico omics analyses - Supplementary Figure 3
<p>Supplementary Figure 3. Interleaved output of two RAxML Phylogenetic Trees of the same sequences estimated on Mac OSX (blue) and Amazon Linux (red). Differences in the branch lengths of resulting trees are shown in color. No such differences were observed when running a Nextflow dockerized version of the same command.</p>
Reproducible in-silico omics analyses - Main figure
<p>Figure 1: Nextflow produces stable analysis across different platforms. (a) Leishmania infantum clone JPCM5 genome annotation was predicted using a native and a dockerized (Debian Linux) version of the Companion eukaryotic annotation pipeline. The native and dockerized versions were run on Mac OSX and Amazon Linux platforms. The Venn diagram shows the existence of small, but significant discrepancies when comparing the genomic coordinates of predicted coding genes and non-coding RNAs, with some of these disparities including entire genes. (b) Results were deterministic on each platform, and totally identical readouts were measured when deploying the dockerized version. (c) A similar comparison carried out on a Kallisto/Sleuth pipeline when looking for differentially expressed genes (q-value <0.01) in an RNA-seq experiment collected from human lung fibroblasts reveals a comparable fluctuation between the Mac OSX and the Amazon Linux platform. Similarly, in this case both platforms produce identical readouts when deploying the dockerized version of the pipeline.<br> </p>
Reproducible in-silico omics analyses - Supplementary Figure 2
<p>Supplementary Figure 2. Kallisto Nextflow pipeline. The native Kallisto pipeline is converted to Nextflow and composed of three processes. The first two processes call Kallisto to index the transcriptome and then pseudo-map for RNA-seq quantification, and the third one for Sleuth to perform differential expression analysis.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.