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1,744 results for “peptide”
A meta-proteogenomic approach to peptide identification incorporating assembly uncertainty and genomic variation
<p>Supplementary data to "A meta-proteogenomic approach to peptide identification incorporating assembly uncertainty and genomic variation"</p>
Supporting data for "Quality control for the target decoy approach for peptide identification"
<p>Supporting data for the manuscript "Quality control for the target decoy approach for peptide identification". Input files are raw mass spectrometry runs, to be downloaded from the PRIDE Archive (https://www.ebi.ac.uk/pride/), which can be processed with the parameter files, Nextflow workflow, and Python scripts in "workflow-scripts-parameters.zip". The resulting output files that were used for the manuscript are provided in search-results.zip.</p>
The ER folding sensor UGGT1 acts on TAPBPR-chaperoned peptide-free MHC I
<p>Adaptive immune responses are triggered by antigenic peptides presented on major histocompatibility complex class I (MHC I) at the surface of pathogen-infected or cancerous cells. Formation of stable peptide-MHC I complexes is facilitated by tapasin and TAPBPR, two related MHC I-specific chaperones that catalyze selective loading of suitable peptides onto MHC I in a process called peptide editing or proofreading. On their journey to the cell surface, MHC I complexes must pass a quality control step performed by UGGT1, which senses the folding status of the transiting N-linked glycoproteins in the endoplasmic reticulum (ER). UGGT1 reglucosylates non-native glycoproteins and thereby allows them to revisit the ER folding machinery. Here, we describe a reconstituted <em>in-vitro</em> system of purified human proteins that enabled us to delineate the function of TAPBPR during the UGGT1-catalyzed quality control and reglucosylation of MHC I. By combining glycoengineering with liquid chromatography-mass spectrometry, we show that TAPBPR promotes reglucosylation of peptide-free MHC I by UGGT1. Thus, UGGT1 cooperates with TAPBPR in fulfilling a crucial function in the quality control mechanisms of antigen processing and presentation.</p>
Data for: High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display
<p>Tyrosine kinases and SH2 (phosphotyrosine recognition) domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue. Although the preferred recognition motifs of many kinases and SH2 domains are known, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically-encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases and SH2 domains. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences. We also screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. We extended this platform to the analysis of SH2 domains and showed that screens could predict relative binding affinities. Finally, we expanded our method to assess the impact of non-canonical and post-translationally modified amino acids on sequence recognition. This specificity profiling platform will shed new light on phosphotyrosine signaling and could readily be adapted to other protein modification/recognition domains.</p>
Data for publication "Lipid oxidation controls peptide self-assembly near membranes through a surface attraction mechanism"
<p>The data provided refer to our published article:</p> <p>T. John,* S. Piantavigna, T. J. A. Dealey, B. Abel, H. J. Risselada, L. L. Martin*, Lipid oxidation controls peptide self-assembly near<br>membranes through a surface attraction mechanism, Chem. Sci. 14 (2023), 3730-3741. <a href="https://doi.org/10.1039/d3sc00159h">https://doi.org/10.1039/d3sc00159h</a>.</p>
Taste Peptides and their derivatives
<p>Food Ai Researcher</p>
Microscopic View of the iRGD Peptide Binding Mechanism
<p>Input and trajectory files used to obtain an atomistic picture of the folding and mechanism of binding of the iRGD peptide to RGD integrin receptors.</p>
MetaPep: A core peptide database for faster human gut metaproteomics database searches
<p>Metaproteomics has increasingly been applied to study functional changes in the human gut microbiome. And peptide identification is an important step in metaproteomics research. However, the large search space in metaproteomics studies causes significant challenges for peptide identification. Here, we constructed MetaPep, a core peptide database (including both collections of peptide sequences and tandem MS spectra) greatly accelerating the peptide identifications. Raw files from fifteen metaproteomics projects were re-analyzed and the identified peptide-spectrum matches (PSMs) were used to construct the MetaPep database. The constructed MetaPep database achieved rapid and accurate identification of peptides for human gut metaproteomics.</p>
Anisotropic Gold Nanomaterial Synthesis Using Peptide Facet Specificity and Timed Intervention: Experiment and simulation data, figures, and notebooks for recreating figures
<p><strong>/Data</strong></p> <p>Contains all experimental and simulation data used to generate figures in the manuscript. Each folder is labelled with the relevant technique: atomic force microscopy (AFM), dynamic light scattering (DLS), molecular dynamics (MD), small-angle X-ray scattering (SAXS), scanning electron microscopy (SEM), transmission electron microscopy and selected area electron diffraction (TEM-SAED), and ultraviolet-visible light spectroscopy (UV-Vis). Folders may contain subfolders from different experiments and should be unchanged for the notebooks to point to the correct path when loading data. Any file which contains SI in the filename indicates sample information, which includes sample concentration, delay associated with creating the sample (Delay4 is the relevant column name), unique identification (UID), etc.</p> <p><strong>/Data/AFM</strong></p> <p>Each filename is labelled with the number of delay associated with the sample or No Z2 Control in the case of the control sample. Each sample contains 3 files, the raw AFM data, a processed image, and an excel file with line profile measurements.</p> <p><strong>/Data/DLS</strong></p> <p>Contains exported data from Malvern Nano ZS DLS instrument.</p> <p><strong>/Data/Images</strong></p> <p>Images from the colloidal stability experiment.</p> <p><strong>/Data/MD</strong></p> <p>Molecular dynamics data and some notebooks for processing peptide and facet combinations.</p> <p><strong>/Data/SAXS</strong></p> <p>Reduced SAXS data from time-resolved study of nanoplatelet growth.</p> <p><strong>/Data/SEM</strong></p> <p>SEM images of samples prepared with and without dynamic intervention (control).</p> <p><strong>/Data/TEM_SAED</strong></p> <p>TEM images of samples prepared with and without dynamic intervention (control), and SAED image of a nanoplatelet.</p> <p><strong>/Data/UV-Vis</strong></p> <p>Spectroscopy data from different Z2 variants, the microplate based stability assay, cuvette based stability assay, and the time resolved (kinetics) measurements of particle growth as a result of manual dynamic intervention.</p> <p><strong>/Notebooks</strong></p> <p>This folder contains all of the Jupyter notebooks used to plot data and fit SAXS scattering profiles.</p> <p>See <strong>/Notebooks/environment.yml</strong> for packages necessary to execute the notebooks here and in <strong>/Synthesis_Protocol</strong>. We recommend installing this environment by using:</p> <p> </p> <p>Refer to <a href="https://github.com/SasView/sasmodels">https://github.com/SasView/sasmodels</a> and the first cell of <strong>/Notebooks/SAXS_Fitting.ipynb</strong> for specific instructions on how to complete installation of the sasmodels module (sasmodels will be installed by Pip if you correctly use the shared environment.yml file).</p> <p><strong>/Figures</strong></p> <p>Figures presented in the publication</p>
Meta-GWAS of C-peptide in Type 1 Diabetes
<p>7,252 subjects with type 1 diabetes from four studies (SDRNT1BIO, DCCT, CACTI and WESDR) were included in this analysis. This dataset includes summary stats for 8,150,646 autosomal SNPs.</p>
Molecular Simulation of Stapled Peptides: Supporting Material
<p>A compressed (tar-gzip) archive containing input and analysis scripts, along with partial analysis data, for Chapter 14 of Computational Peptide Science: Methods and Protocols (Thomas Simonson, ed.) entitled "Molecular Simulation of Stapled Peptides" by Victor Ovchinnikov, Aravinda Munasinghe, and Martin Karplus; Methods in Molecular Biology, vol. 2405, https://doi.org/10.1007/978-1-0716-1855-4_14, 2022, Springer.</p>
Data for: High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display
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Data from: Modulating peptide co-assembly via macromolecular crowding: Recipes for co-assembled structures
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Peptide-driven control of somersaulting in Hydra vulgaris
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Data from: Free energy analysis of peptide-induced pore formation in lipid membranes by bridging atomistic and coarse-grained simulations
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Regression models generated by APRANK (computational prioritization of antigenic proteins and peptides from complete pathogen proteomes)
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Effect of metabolosome encapsulation peptides on enzyme activity, co-aggregation, incorporation and bacterial microcompartment formation
<p><strong>Supplementary Video 1</strong>. This approach revealed the structures formed by fvMT and BMC shell proteins are remarkably varied in size, shape and volume.</p> <p><strong>Supplementary Video 2. </strong>Recombinant BMCs containing L20-fvMT and -</p> <p><strong>Supplementary Video 3. </strong> Allowing us to quantitate the volume (empty: 54900±11013 nm<sup>3</sup> (n=29); L20-fvMT: 336411±177722 nm<sup>3 </sup>(n=60)) and the largest diameter (empty: 61.77±15.38 nm (n=29); L20-fvMT: 127.51±60.97 nm (n=60)) of these structures</p>
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>
X-ray diffraction images of the beta4 tetramer of the C-terminal peptide of the split chain transketolase
<p>X-ray images for PDB entry 6YAJ</p> <p>DOI for the pdb is https://doi.org/10.2210/pdb6YAJ/pdb</p> <p>Title: A 'Split-Gene' Transketolase From the Hyper-Thermophilic Bacterium Carboxydothermus hydrogenoformans : Structure and Biochemical Characterization.<br> Journal: Front Microbiol<br> Volume: 11<br> Pages: 592353 - 592353<br> Year: 2020<br> PubMed ID: 33193259<br> DOI: 10.33 89/fmicb .2020.592353</p> <p> </p> <p> </p>
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>
ScienceDex guides
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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.